Variabilidad climática en el gradiente altitudinal en la vertiente del Caribe (5 msnm y 1400 msnm).
Andrés Carranza Quirós, Stacy Chacón Fallas, Ana María Ocampo Cambronero, Argerie Oviedo Bolaños, Génesis Rodriguez Naranjo.
Resumen
Resumen: Costa Rica presenta un clima variable, con altas temperaturas, y abundantes precipitaciones. Los vientos provenientes del Caribe, generan variaciones en el comportamiento de diversos factores climáticos en el país. Esta investigación plantea analizar la variabilidad climática en los últimos 30 años en dos gradientes altitudinales en la vertiente del Caribe, para determinar los factores que producen cambios en las temperaturas y precipitación. Se trabajó una base de datos de precipitación (mm), temperatura máxima y mínima (°C) en dos estaciones, proporcionados por el Instituto Meteorológico Nacional. Mediante el programa R-ClimDex, se calcularon diversos índices climáticos, basados en un previo análisis de regresión lineal.Cada uno de los índices con valores significativos (P-value <0.05), se sometieron a la prueba de Shapiro.Wilk a los residuos para demostrar la normalidad de los datos con ayuda del programa R. Precipitaciones con mayor intensidad y temperaturas más bajas son los factores con más significancia. Dichas variaciones obedecen a los fenómenos ENOS, que generan lluvias en menos tiempo y más violentas. Los rayos del sol no penetran en su totalidad a causa de mucha nubosidad, por lo que la temperatura baja. Esto supone la existencia de variabilidad climática, afectando a largo plazo los ecosistemas y las actividades humanas.
Palabras clave: Variabilidad climática, precipitación, temperatura, gradiente altitudinal, Vertiente del Caribe.
Introducción
Costa Rica es un país que se caracteriza porque gran parte del año presenta un clima caracterizado por sus altas temperaturas y abundantes lluvias representativo de un clima tropical. Por esta razón la variación más importante es la lluvia ya que afectan los diferentes sistemas de viento y la topografía (Lizano y Salas, 2001). Costa Rica comprende la vertiente del caribe la cual presenta temperaturas muy variadas de acuerdo con la altitud, siendo ésta la distancia vertical que existe sobre cualquier punto de la Tierra tomando como referencia el nivel del mar, por lo que ésta zona se encuentra directamente expuesta a los vientos alisios que llegan cargados de humedad al Caribe. Este parámetro produce una gran cantidad de variaciones en el comportamiento de diferentes componentes o factores climáticos (Retana y Villalobos, 2000). Estas variaciones reciben el nombre de variabilidad climática la cual es un fenómeno que afecta a una gran cantidad de sectores, desde las actividades humanas como en los diferentes entornos naturales que sufren importantes variaciones en la estabilidad de sus ecosistemas (Yáñez-Arancibia, Twilley y Lara-Domínguez, 1998).
Existen varios factores a la hora de estudiar los efectos de la variabilidad climática sobre un entorno, donde la precipitación y la medición de las temperaturas máximas y mínimas ofrecen un claro panorama a la hora de determinar estos cambios (Puertas y Carvajal, 2008). La precipitación que es la caída de agua desde la atmósfera hasta la superficie terrestre, formando parte del ciclo del agua. La precipitación se genera por la condensación del agua, es decir, la formación de nubes por la acumulación de agua en la atmósfera. Otro factor importante es la temperatura atmosférica, la cual se ve reflejada cuando las radiaciones solares llegan a la superficie terrestre provocando que su energía caliente el suelo y así mismo las capas del aire que se encuentran en contacto. Este efecto se ve reflejado en el aumento de la temperatura a lo largo de los años ya que las radiaciones no son absorbidas por el suelo y se reflejan para escapar de nuevo a la atmósfera, sin embargo, se devuelven a la superficie terrestre debido al dióxido de carbono y el vapor (Alfaro y Amador, 1997). La temperatura atmosférica, comprende temperaturas máximas, que es la mayor temperatura del aire alcanzada en un lugar en un día (máxima diaria), en un mes (máxima mensual) o en un año (máxima anual), y temperaturas mínimas, que al contrario, es la menor temperatura registrada en un lugar a lo largo de un día, mes o año.
Es importante comprender la diferencia entre variabilidad y cambio climático, la variabilidad según Alzate, Rojas, Mosquera y Ramón (2015) se puede definir como cambios o modificaciones en los componentes climáticos en periodos de tiempo cortos mientras que el cambio climático se refiere a diferencias climáticas a lo largo de grandes periodos y que presentan un estado irreversible a su condición previa. Debido a esto es que la variabilidad climática debe de ser analizada en rangos temporales pequeños como meses, ya que si se promedian las variables a lo largo de un año no se detectarán las diferencias. La variabilidad climática puede ser causada por oscilaciones térmicas, que son diferencias numéricas entre los valores máximos y mínimos de temperatura observados en un punto dado durante un período de tiempo. Una de estas oscilaciones es la Oscilación del sur (ENOS), que son los cambios interanuales, con una escala de tiempo de varios años (2 a 7 años), de las condiciones atmosféricas y temperaturas oceánicas fluctuantes sobre los océanos Pacifico e Indico ecuatoriales (Fernández y Ramírez, 1991). El Niño es la fase caliente del ENOS, y se refiere a la interacción climática océano-atmósfera a gran escala, asociada a un calentamiento periódico. Por otra parte, La Niña es la fase fría del ENOS asociada al Niño, es precedida y seguida por periodos en los cuales las temperaturas superficiales del mar son más bajas de lo normal en el pacífico central y oriental y los vientos alisios son muy fuertes (Fernández y Ramírez, 1991).
En este contexto, es de gran importancia estudiar la variabilidad climática, ya que se pueden realizar perspectivas a corto y a mediano plazo, y con esto se podrían implementar acciones respecto a la adaptación de la variabilidad climática (Markus, 2017). El presente trabajo tiene como objetivo analizar la variabilidad climática en los últimos 30 años en dos gradientes altitudinales en la vertiente del Caribe (5 y 1400 msnm), para determinar los impactos que podrían producir los cambios en temperatura (máxima y mínima) y precipitación de ambas zonas.
Objetivos
Objetivo general
Analizar la variabilidad climática en los últimos 30 años en dos gradientes altitudinales en la vertiente del Caribe (5 y 1400 msnm), para determinar los impactos que podrían generar los cambios en temperatura y precipitación de ambas zonas.
Objetivos específicos
1-Determinar los principales cambios en la precipitación y temperatura (mínima y máxima) en dos altitudes de la vertiente Caribe (5 y 1400 msnm).
2-Comparar la variabilidad climática entre las dos estaciones del Caribe a diferentes altitudes (5 y 1400 msnm).
3-Generar perspectivas de los cambios en la precipitación y temperatura (máxima y mínima) de ambas zonas.
Materiales y Métodos
En esta investigación se utilizó datos de precipitación (mm) y temperatura (máxima y mínima, °C) diarios de dos estaciones meteorológicas (Linda Vista y Limón) proporcionados por el Instituto Meteorológico Nacional (IMN, Cuadro 1). Ambas estaciones poseen el mismo periodo de medición de datos diarios entre 1980 y 2010 y se encuentran en la misma vertiente Caribe, pero difieren en altitud (Limón: 5 msnm; y Linda Vista: 1400 msnm). 
La altitud entre ambas estaciones fue un factor determinante en esta investigación. Se tomaron en cuenta estaciones con poca similitud en términos de gradiente altitudinal, con el fin de determinar el comportamiento de las lluvias y la afectación de otros fenómenos en cada uno de las fronteras de la vertiente del Caribe. Las estaciones meteorológicas poseen un periodo de 30 años, que representa el mínimo de años requerido para el estudio de clima en una zona específica.
Figura1
Mediante el programa de R, se calculó el promedio mensual de la temperatura máxima, temperatura mínima y precipitación. Para poder calcular la temperatura promedio mensual de cada año, cada mes debe contener como mínimo el 50 % de la información completa. Con respecto a los datos de precipitación, se realiza una sumatoria, debe de existir mínimo 24 días que se refieren al 80% de la información completa por mes.
Este programa necesita de ciertos parámetros, para poder realizar un análisis estadístico. Se realizó una comparación de la información disponible durante los treinta años con de la primera década de los datos. Se calculó para los valores mínimos y máximos con el percentil 5 y 95 tanto de la temperatura máxima como de la temperatura mínima. Se calculó el percentil 95 para los datos de precipitación.
Para poder describir la variabilidad mediante análisis estadísticos se utilizan diversas metodologías, las cuales estudian el comportamiento del clima en espacio y tiempo. Un ejemplo de ello, es la aplicación de un programa estadístico llamado RClimDex, el cual realiza un análisis estadístico de la variabilidad temporal de las precipitaciones, temperaturas máximas y mínimas. (Zhang y Yang, 2004). Este programa fue diseñado para dar seguimiento y detectar el cambio climático. Además se utiliza como plataforma de trabajo, el programa R por ser vigoroso y completo en lo que respecta al análisis estadístico y al mismo tiempo muy minucioso en la elaboración de gráficos. RClimDex facilita una interfaz para el cálculo de índices extremos climáticos con límites que pueden ser definidos por el usuario. Este programa permite calcular 27 índices (Zhang y Yang, 2004), dentro de los cuales solo 26 de ellos fueron útiles para la investigación realizada. Los índices utilizados en el programa RClimDex, se basan en valores de temperatura máxima diaria, temperatura mínima diaria y precipitación diaria, donde Tx representa los valores de temperatura máxima, Tn los valores de temperatura mínima, PRCP indica la cantidad de precipitación y RR define los días húmedos (días con lluvia). Los valores de referencia que se requieren para el análisis de los índices se obtuvieron a partir de los registros diarios de los primeros 10 años de estudio (1980-1990). Se calcularon los valores máximos de precipitación diaria que se registran con el percentil 90 que se refiere al 90% de las precipitaciones; se establece este valor con el fin de eliminar los valores extremos poco frecuentes Además de ello, se calculan los valores máximos y mínimos de la temperatura máxima y los valores máximos y mínimos de la temperatura mínima. Con ello, se presenta un resumen de los 26 índices de RClimDex, que se utilizaron para el análisis de esta investigación y se describen a continuación. Cabe destacar que uno de los índices con los que trabaja este programa, se utiliza para calcular temperaturas menores a los 0°C, por ello, no cumple con los objetivos de este trabajo, ya que Costa Rica al ser un país tropical, no cuenta con esas temperaturas.}
Análisis de datos Los índices previamente mencionados se sometieron a análisis de regresión lineal (α=0.05). Todos los supuestos de regresión lineal fueron sometidos a prueba (normalidad de residuos: Shapiro Wilk, Waine (1991); e independencia de residuos). Los datos atípicos fueron previamente detectados y eliminados de todas las regresiones lineales. RClimDex genera diferentes tipos de figuras de cada uno de los índices calculados, sin embargo, para efectos de esta investigación fueron nuevamente realizados con ayuda de lenguaje de programa de R, versión 3.5.1 (RCoreTeam, 2018).
Resultados
Como lo muestra el cuadro 2, se utilizaron 26 índices, dentro de los cuales solo 17 resultaron con valores significativos (resaltados en negrita en el cuadro). Los resultados del análisis de los datos significativos diarios de tres décadas realizado tanto para la estación 81003 (Limón) y la estación 73018 (Linda Vista, El Guarco) se muestran en los cuadros a continuación: 
Todos los índices mencionados en el cuadro 3 y 4, demuestran que cumplen con valores significativos a la hora de realizar el análisis de regresión, con un p-value menor a 0.005 en todos los casos, lo que demuestra que hay diversos factores que afectan la linealidad de los datos, se nota claramente que existe una tendencia a la variabilidad climática, la mayoría de los índices muestran un comportamiento creciente con respecto al tiempo. Dicha información estadística se corrobora observando los gráficos de los principales índices para las estaciones de Limón y El Guarco.
En la figura 2 los índices de la estación 73018 (Linda Vista, El Guarco) se observa que la gráfica A muestra que durante las tres últimas décadas hubo un descenso en la precipitación alrededor de 16mm a 8 mm. En la gráfica B se muestra un aumento constante de la máxima precipitación que se está concentrando en caer en 5 días seguidos. En la gráfica C hubo un aumento de las precipitaciones mayores a 10 mm durante los últimos 30 año.En la gráfica C se mostró un aumento constante de las precipitaciones mayores a 20 mm durante los últimos 30 años.
"SDII"
lm(sdii~year,data=X73018_RClimDex_SDII_sustituido)->x
summary(x)->y
y
visreg(x,main="SDII (Precipitación Diaria)",xlab="Tiempo (Días)",ylab="Precipitación (mm)")
shapiro.test(x$residuals)
"RX5DAY"
lm(annual~year,data=X73018_RClimDex_RX5day_sustituido)->x
summary(x)->y
y
visreg(x,main="RX5day(Máxima
precipitación 5 días seguidos)",xlab="Tiempo (Años)",ylab="Precipitación (mm)")
shapiro.test(x$residuals)
"R10mm"
lm(R10~year,data=X73018_RClimDex_R10mm_sustituido)->x
summary(x)->y
y
visreg(x,main="R10mm (Precipitacion > 10mm)",xlab="Tiempo (Años)",ylab="Precipitación (mm)")
shapiro.test(x$residuals)
"R20mm"
lm(R20~year,data=X73018_RClimDex_R20mm_sustituido)->x
summary(x)->y
y
visreg(x,main="R20mm (Precipitación > 20mm)",xlab="Tiempo (Años)",ylab="Precipitación (mm)")
shapiro.test(x$residuals)
En la figura 3 la precipitación anual en días muy húmedos de la gráfica A aumentó de manera constante ya que se observa un cambio alrededor de 100mm a 500mm durante los últimos 30 años. En la gráfica B muestra que conforme pasan los años los días secos consecutivos van disminuyendo de manera constante. En la gráfica C los días húmedos consecutivos aumentaron de manera constante durante los últimos 30 años. En la gráfica D la precipitación anual total aumentó alrededor de 1000 mm a 2000 mm de manera constante en los días húmedos durante los últimos 30 años.
"R95P"
lm(r95p~year,data =X73018_RClimDex_R95p_sustituido)->x
summary(x)->y
y
visreg(x,main="R95P(Días muy húmedos)",xlab="Tiempo(años)",ylab="Precipitación anual(mm)")
shapiro.test(x$residuals)
"CDD"
lm(cdd~year,data=X73018_RClimDex_CDD_sustituidooo)->x
summary(x)->y
y
visreg(x,main="CDD(Días secos consecutivos)",xlab="Tiempo(años)",ylab="Días secos(días)")
shapiro.test(x$residuals)
influencePlot(x)
z<-X73018_RClimDex_CDD_sustituidooo[-c(1,3,4,31),]
lm(cdd~year,data = z)->z2
shapiro.test(z2$residuals)
summary(z2)
visreg(z2,main="Días secos consecutivos",xlab="Tiempo(años",ylab="Días secos(días)")
"CWD"
lm(cwd~year,data=X73018_RClimDex_CWD_sustituido)->x
summary(x)->y
y
visreg(x,main="CWD(Días húmedos consecutivos)",xlab="Tiempo(años)",ylab="Días húmedos(días)")
shapiro.test(x$residuals)
"PRCPTOT"
lm(prcptot~year,data =X73018_RClimDex_PRCPTOT_sustituir)->x
summary(x)->y
y
visreg(x,main="PCRPTOT(Precipitación total anual en los días húmedos)",xlab="Tiempo(años)",ylab="Precipitación(mm)")
shapiro.test(x$residuals)
En la figura 4 se muestran la cantidad de días fríos (Figura A) donde se puede observar una pendiente ascendente con respecto a las últimas décadas por lo que estos días aumentan cada vez más, de igual manera las noches caliente (Figura B) tienden a presentar una pendiente ascendente ya que pasó de tener solo una noche caliente en 1980 a 5 en 1992.
"TX10P(Días Fríos)"
lm(annual~year,data=X73018_RClimDex_TX10P_sustituido)->x
summary(x)->y
y
visreg(x)
plot(x)
shapiro.test(x$residuals)
influencePlot(x)
x2<-X73018_RClimDex_TX10P_sustituido[-c(12,27,28),]
lm(annual~year,data=x2)->x3
shapiro.test(x3$residuals)
summary(x3)
visreg(x3)
"TN90P (Noches Calientes)"
lm(annual~year,data=X73018_RClimDex_TN90P_sustituido)->x
summary(x)->y
y
visreg(x,main="TX10P (Noches Calientes)",xlab="Tiempo (Años)",ylab="Noches Calientes (Días)")
shapiro.test(x$residuals)
En la figura 5 la precipitación de la estación 81003 (Limón) muestra que la intensidad de diaria de la precipitación de la gráfica A aumenta hasta alrededor de 22 mm/día durante los últimos 30 años. En el caso de la gráfica B y C la precipitación máxima aumentó en 1 día y en 5 días respectivamente durante un periodo de 30 años. En la gráfica C se muestra un aumento de los días muy húmedos con respecto a los últimos 30 años.
"RX1day(Cantaidad máxima de precipitación en 1 día)"
datosP<-lm(annual~year, data = X81003_RClimDex_RX1day_1_ )
datosP
summary(datosP)
shapiro.test (datosP$residuals)
graf<-visreg(datosP,"year", partial=F, main= "RX1day(Cantaidad máxima de precipitación en 1 día)", xlab= "Tiempo(Años)", ylab="Precipitación(mm)")
graf
En la figura 6 los índices de precipitación para la estación 18003 (Limón) muestran en la gráfica A un aumento del número de días mayores a 43 mm con respecto a los últimos 30 años y en el caso de la gráfica B se observa que los días extremadamente tienen un incremento con respecto a los últimos 30 años.
"R43(Número de días>43mm)"
dat<-lm(Rnn~year, data = X81003_RClimDex_R43_515mm)
dat
summary(dat)
shapiro.test (dat$residuals)
graf<-visreg(dat,"year", partial=F, main= "R43(Número de días>43mm)", xlab= "Tiempo(Años)", ylab="Días>43mm(días)")
graf
"R99p(Días extremadamente húmedos)"
datosl<-lm(r99p~year, data = X81003_RClimDex_R99p)
datosl
summary(datosl)
shapiro.test (datosl$residuals)
graf<-visreg(datosl,"year", partial=F, main= "R99p(Días extremadamente húmedos)", xlab= "Tiempo(Años)", ylab="Precipitación(mm)")
graf
En la figura 7 se muestra en la gráfica A que la temperatura diurna es menor bajando un grado, en la gráfica B sin embargo los días fríos han disminuido, y las noches calientes aumentado en la (Figura C). En la figura D se observa una disminución de los días calientes de 5 a 1.
"DTR(Temperatura Diurna)"
datos1<-lm(annual~year, data = X81003_RClimDex_DTR_usar)
datos1
summary(datos1)
shapiro.test (datos1$residuals)
graf<-visreg(datos1,"year", partial=F, main= "DTR(Temperatura Diurna)", xlab= "Tiempo(días)", ylab="Temperatura(°C)")
graf
"TN90P(Noches calientes)"
datos8<-lm(annual~year, data = X81003_RClimDex_TN90P)
datos8
summary(datos8)
shapiro.test (datos8$residuals)
graf<-visreg(datos8,"year", partial=F, main= "TN90P(Noches calientes)", xlab= "Tiempo(Años)", ylab="Tiempo(días)")
graf
"TX90P(Días calientes)"
datis<-lm(annual~year, data = X81003_RClimDex_TX90P)
datis
summary(datis)
shapiro.test (datis$residuals)
graf<-visreg(datis,"year", partial=F, main= "TX90P(Días calientes)", xlab= "Tiempo(Años)", ylab="Tiempo(días)")
graf
Finalmente en la figura 8 se observa que en la figura A hay una disminución en las temperaturas máximas, esto confirmado por la figura B donde cada vez son menos los días con temperaturas mayores a 32° pasando de 20 en 1980 a 10 en el 2010.
"SU32(Días de verano>32)"
datosA<-lm(su~year, data = X81003_RClimDex_SU32_4)
datosA
summary(datosA)
shapiro.test (datosA$residuals)
graf<-visreg(datosA,"year", partial=F, main= "SU32(Días de verano>32)", xlab= "Tiempo(Años)", ylab="Días>32mm(días)")
graf
Discusión
Para la temperatura en la estación de Linda Vista, El Guarco (73018) (Figura 4) podemos observar una tendencia en las temperaturas a disminuir, este aumento de temperaturas bajas en esta región del país puede ser causado por el aumento en la nubosidad que a su vez genera una prolongación en las temperaturas mínimas y una disminución de las temperaturas máximas (Gómez y Fernández 1996, Fernández y Ramírez 1991),las cuales según nuestros resultados coinciden con estas conclusiones. De igual manera la estación localizada en Limón presenta las mismas tendencias a aumentar las temperaturas mínimas (Figuras 7 y 8) por lo que podemos concluir que a partir de los datos recolectados de ambas estaciones que una gran parte de la vertiente del Caribe está presentando una tendencia hacia temperaturas más bajas, esto se debe a que las nubes influyen en la calidez del clima, de esta manera las nubes que se encuentran bajas provocan una disminución de la temperatura ya que son más densas y no permiten que los rayos del Sol penetren en su totalidad.(Marvel, 2017)
Un aumento en la nubosidad a causa de las precipitaciones genera un fenómeno que explica el comportamiento de las noches a calentarse cada vez más. La nubosidad al reflejar la radiación, tiene un doble efecto sobre el aumento o disminución de las temperaturas en la superficie terrestre. Por el día, una alta concentración de nubes reflejan la radiación solar que incide a la superficie produciendo una sensación de frescura, mientras que en la noche, la nubosidad se encarga de reflejar la radiación infrarroja emitida desde la superficie de la tierra, generando noches más calientes. A su vez, las propias nubes llegan a calentarse durante el proceso y emiten parte de esa radiación a la superficie. (Zúñiga y Crespo, 2010)
Posteriormente al analizar los datos obtenidos con respecto a la precipitación de la estación de Linda Vista, El Guarco (Figuras 2 y 3) podemos ver que las precipitaciones diarias tienden a disminuir, esto puede ser explicado ya que ahora las precipitaciones se concentran en un número de días menor (Figura 2B), por lo que aunque diariamente hay menos precipitaciones, esto se repone en una cantidad de días menor de lluvias. En la estación localizada en Limón se observan las mismos conclusiones (Figuras 5 y 6) con la excepción de que además las lluvias diarias también aumentan lo que significa que las precipitaciones en esta región han aumentado significativamente. Fernández y Ramírez (1991) explican que estas observaciones realizadas en el Caribe obedecen a comportamientos propios del fenómenos del ENOS el cual entre los meses de julio y agosto se observa una tendencia a mucha precipitación con una inclinación a ser cortas y violentas e incluso la aparición de temporales debido al aumento de los vientos alisios sobre esta región.
Se calculó, a partir de las regresiones obtenidas, cuánto cambiarían las variables tanto de temperatura máxima y mínima como de precipitación al cabo de 5 y 10 años (Cuadro 3). Se notó que aunque los cambios van a seguir la misma tendencia, estos a corto y mediano plazo no son una gran amenaza ya que son irrelevantes y no corresponden a un cambio brusco de las condiciones climáticas por lo que también se puede inferir que estas condiciones no van a generar un cambio climático como tal. Por otro lado si analizamos los cambios a largo plazo, podrían generar cierto riesgo al alterar de manera muy significativa el ambiente físico. Se puede observar evidencia de que existen cambios poco relevantes (Cuadro 4) ya que son solo diez años de diferencia pero cuando hablamos de 40 años o más, la variabilidad climática será más visibles.
Podemos concluir que existe variabilidad climática en la vertiente del caribe lo cual puede suponer una problemática a largo plazo tanto para el ecosistema como para las actividades humanas, especialmente para los sectores más vulnerables como lo son el sector agrícola y el ecoturismo (Amador y Alfaro, 2009). En el caso de la primera situaciones como las inundaciones causan además de la pérdida de zonas de cultivo, que el agua de mar sature el suelo y esta se filtre hacia los mantos acuíferos los cuales son utilizados para el riego de los cultivos, algo indispensable para la agricultura (Altieri y Nicholls, 2009). En el caso del ecoturismo Díaz (2012) menciona que la cantidad de ganancias en los entornos naturales con gran atracción turística pueden llegar a verse influenciados negativamente, llegando a tener pérdidas económicas hasta de un 30% si estos se empiezan a ver afectados por la variabilidad climática. Con esto es pertinente realizar mayores observaciones con respecto a las medidas necesarias para disminuir los impactos que estos fenómenos causan.
Referencias
Yáñez-Arancibia, A., Twilley, R. R., y Lara-Domínguez, A. L. (1998). Los ecosistemas de manglar frente al cambio climático global. Madera y Bosques, 4(2), 3-19.
Puertas Orozco, O. L., y Carvajal Escobar, Y. (2008). Incidence of El Niño southern oscillation in the precipitation and the temperature of the air in Colombia, using Climate Explorer. Ingeniería y Desarrollo, (23), 104-118.
Alzate, D., Rojas, E., Mosquera, J., y Ramón, J. (2015). Cambio climático y variabilidad climática para el periodo 1981-2010 en las cuencas de los ríos Zulia y pamplonita, norte de Santander–Colombia. Revista Luna Azul, (40).
Lizano, O y Salas, D. (2001). Variaciones geomorfológicas en los últimos 50 años en la isla Damas, Quepos, Costa Rica. Revista Biología Tropical. 49(2):171-177.
Retana, J y Villalobos, R. (2000). Caracterización pluviométrica de la fase cálida de ENOS en Costa Rica basado en probabilidades de ocurrencia de eventos en tres escenarios: seco, normal y lluvioso. Tópicos Meteorológicos y Oceanográficos. 7(2):124-130.
Amador, J.A. y A. “Ciclones tropicales y sociedad: Una aproximación al enfoque científico de estos fenómenos atmosféricos como referente para la investigación social en desastres”, en Concepciones y Representaciones de la Naturaleza y la Ciencia en América Latina, editado por R.Viales, J. Amador y F.J. Solano. San José: Editorial de la Universidad de Costa Rica, 2009, 159-179.
Fallas, J.C y R. Oviedo. “Temporales”. Cap. III. En: Fenómenos atmosféricos y cambio climático, visión centroamericana. Instituto Meteorológico Nacional, San José, Costa Rica, (2003): 38.
Fernández, W., y Ramírez, P. (1991). El Niño, la Oscilación del Sur y sus efectos en Costa Rica: una revisión. Tecnología en Marcha, 11(1), 3-10.
R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
Waine, D. (1991). Bioestadística, base para el análisis de las ciencias de la salud. Mexico., México DF. LIMUSA. Recuperado de http://www.academia.edu/17988752/Bioestadistica_Base_para_el_analisis_de_las_ciencias_de_la_salud
Zhang, X., y Yang, F. (2004). RClimDex (1.0). Manual de Usuario. Canadá.
N/A. S/F. Universidad Nacional de Costa Rica. Recuperado de http://www.repositorio.una.ac.cr/bitstream/handle/11056/7520/estaciones_imn_2008.216.png?sequence=1&isAllowed=y
Altieri, M. A., & Nicholls, C. I. (2009). Cambio climático y agricultura campesina: impactos y respuestas adaptativas. LEISA revista de agroecología, 14, 5-8.
Gómez, I. E., y Fernández, W. (1996). Variación interanual de la temperatura en Costa Rica. Top. Meteor. Oceanogr, 3(1), 27-44.
Amador, J. A., y Alfaro, E. J. (2009). Métodos de reducción de escala: aplicaciones al tiempo, clima, variabilidad climática y cambio climático. Revibec: revista iberoamericana de economía ecológica, 11, 39-52.
Moreno-Díaz, M. L. (2012). Actividades socioeconómicas en el Parque Nacional Isla del Coco, Costa Rica y posibles efectos de la variabilidad climática. Revista de Biología Tropical, 60(3), 113-129.
Zúñiga, I., y Crespo, E. (2010) Meteorología y climatología. Recuperado de https://books.google.co.cr/books?id=E6iXJ2QZiQ4C&pg=PA63&dq=radiacion+infrarroja+nocturna+calor+en+las+noches+nubosidad&hl=es&sa=X&ved=0ahUKEwiGlub67a_eAhUkwlkKHb7OB-gQ6AEIKzAB#v=onepage&q=radiacion%20infrarroja%20nocturna%20calor%20en%20las%20noches%20nubosidad&f=false
Anexos
Anexo 1. Precipitación mensual estación 81003 (Limón)
Anexo2. Precipitación mensual estación 73018 (Linda Vista, El Guarco)
Anexo 3.Temperatura mensual estación 81003 (Limón)
Anexo 4.Instalación de paquetes requeridos para la organización de datos
install.packages("reshape")
install.packages("splitstackshape")
install.packages("plyr")
install.packages("ggplot2")
install.packages("xts")
install.packages("dygraphs")Anexo 5.Organización de la base de datos
#####################################################################
## Script para organizar Información Climática INAMHI 2016
## Credits: Junior Pastor PÉREZ-MOLINA
## Date: 2017/10/08
#####################################################################
rm(list = ls()) #Remove all objects
graphics.off() #Remove all graphics
cat("\014") #Remove script in windows console
if(!grepl("Organización información climática", getwd())){x= cat(prompt = "Please set the working directory to the project folder ")}
#####################################################################
#####################################################################
## Cargar los paquetes requeridos para organizar Información Climática
#####################################################################
# install.packages("reshape")
library(reshape)
# install.packages("splitstackshape")
library(splitstackshape)
# install.packages("plyr")
library("plyr", lib.loc="~/R/win-library/3.4")
# install.packages("ggplot2")
library(ggplot2)
# install.packages("xts")
library(xts)
# install.packages("dygraphs")
library(dygraphs)
#####################################################################
#####################################################################
## PRECIPITACION -> Loading database
#####################################################################
Precipitacion<-read.delim("Data/PRCP.txt",header=FALSE,sep="\t",dec=".")
names(Precipitacion)<-c("codigo","year","mes","d1","d2","d3","d4","d5","d6","d7","d8","d9","d10","d11","d12","d13","d14","d15","d16","d17","d18","d19","d20","d21","d22","d23","d24","d25","d26","d27","d28","d29","d30","d31")
library(reshape)
Precipitacion <- melt(Precipitacion, id=c("codigo","year","mes"))
Precipitacion$value<-as.numeric(as.character(Precipitacion$value))
names(Precipitacion)<-c("codigo","year","month","day","PRCP")
Precipitacion<-data.frame(Precipitacion)
Precipitacion$day<-as.character(Precipitacion$day)
Precipitacion$day<-gsub("d", "", Precipitacion$day)
Precipitacion$day<-as.numeric(Precipitacion$day)
Precipitacion$date<-paste(Precipitacion$year,Precipitacion$month,Precipitacion$day, sep="-")
Precipitacion$date<-as.Date(Precipitacion$date, format = "%Y-%m-%d")
Precipitacion<-Precipitacion[order(Precipitacion$codigo,Precipitacion$year, Precipitacion$month, Precipitacion$day),]
Precipitacion$PRCP<-replace(Precipitacion$PRCP, is.na(Precipitacion$PRCP), -99.9)
Precipitacion$date2<-Precipitacion$date
Precipitacion$date2<-ifelse(is.na(Precipitacion$date2),1,0)
Precipitacion[Precipitacion$date2==1,]<-NA
Precipitacion<-Precipitacion[order(Precipitacion$codigo,Precipitacion$year, Precipitacion$month, Precipitacion$day),]
Precipitacion$date2<-NULL
Precipitacion<-na.omit(Precipitacion)
Precipitacion_ok<-data.frame()
for(i in unique(Precipitacion$codigo)){
sub<-Precipitacion[Precipitacion$codigo==i,]
date<-data.frame(seq(as.Date(paste(format(as.Date(min(na.omit(sub$date)), format="%Y/%m/%d"),"%Y"),"-01-01"),format = "%Y -%m-%d"),
as.Date(paste(format(as.Date(max(na.omit(sub$date)), format="%Y/%m/%d"),"%Y"),"-12-31"),format = "%Y -%m-%d"),
by="day"))
names(date)<-c("date")
date$date<-as.Date(date$date, format = "%Y-%m-%d")
sub2<-merge(date,sub, all=TRUE)
sub2$codigo<-c(i)
Precipitacion_ok <- rbind(Precipitacion_ok,data.frame(sub2))
}
Precipitacion_ok$date2<-Precipitacion_ok$date
library(splitstackshape)
Precipitacion_ok<-cSplit(Precipitacion_ok, "date2", "-")
Precipitacion_ok<-data.frame(Precipitacion_ok[,2],Precipitacion_ok[,7],Precipitacion_ok[,8],Precipitacion_ok[,9],Precipitacion_ok[,6])
names(Precipitacion_ok)<-c("codigo","year","month","day","PCPT")
#####################################################################
#####################################################################
## Tmax -> Loading database
#####################################################################
Tmax<-read.delim("Data/TMAX.txt",header=FALSE,sep="\t",dec=".")
names(Tmax)<-c("codigo","year","mes","d1","d2","d3","d4","d5","d6","d7","d8","d9","d10","d11","d12","d13","d14","d15","d16","d17","d18","d19","d20","d21","d22","d23","d24","d25","d26","d27","d28","d29","d30","d31")
library(reshape)
Tmax <- melt(Tmax, id=c("codigo","year","mes"))
Tmax$value<-as.numeric(as.character(Tmax$value))
names(Tmax)<-c("codigo","year","month","day","Tmax")
Tmax<-data.frame(Tmax)
Tmax$day<-as.character(Tmax$day)
Tmax$day<-gsub("d", "", Tmax$day)
Tmax$day<-as.numeric(Tmax$day)
Tmax$date<-paste(Tmax$year,Tmax$month,Tmax$day, sep="-")
Tmax$date<-as.Date(Tmax$date, format = "%Y-%m-%d")
Tmax<-Tmax[order(Tmax$codigo,Tmax$year, Tmax$month, Tmax$day),]
Tmax$Tmax<-replace(Tmax$Tmax, is.na(Tmax$Tmax), -99.9)
Tmax$date2<-Tmax$date
Tmax$date2<-ifelse(is.na(Tmax$date2),1,0)
Tmax[Tmax$date2==1,]<-NA
Tmax<-Tmax[order(Tmax$codigo,Tmax$year, Tmax$month, Tmax$day),]
Tmax$date2<-NULL
Tmax<-na.omit(Tmax)
Tmax_ok<-data.frame()
for(i in unique(Tmax$codigo)){
sub<-Tmax[Tmax$codigo==i,]
date<-data.frame(seq(as.Date(paste(format(as.Date(min(na.omit(sub$date)), format="%Y/%m/%d"),"%Y"),"-01-01"),format = "%Y -%m-%d"),
as.Date(paste(format(as.Date(max(na.omit(sub$date)), format="%Y/%m/%d"),"%Y"),"-12-31"),format = "%Y -%m-%d"),
by="day"))
names(date)<-c("date")
date$date<-as.Date(date$date, format = "%Y-%m-%d")
sub2<-merge(date,sub, all=TRUE)
sub2$codigo<-c(i)
Tmax_ok <- rbind(Tmax_ok,data.frame(sub2))
}
Tmax_ok$date2<-Tmax_ok$date
library(splitstackshape)
Tmax_ok<-cSplit(Tmax_ok, "date2", "-")
Tmax_ok<-data.frame(Tmax_ok[,2],Tmax_ok[,7],Tmax_ok[,8],Tmax_ok[,9],Tmax_ok[,6])
names(Tmax_ok)<-c("codigo","year","month","day","Tmax")
#####################################################################
#####################################################################
## Tmin -> Loading database
#####################################################################
Tmin<-read.delim("Data/TMIN.txt",header=FALSE,sep="\t",dec=".")
names(Tmin)<-c("codigo","year","mes","d1","d2","d3","d4","d5","d6","d7","d8","d9","d10","d11","d12","d13","d14","d15","d16","d17","d18","d19","d20","d21","d22","d23","d24","d25","d26","d27","d28","d29","d30","d31")
library(reshape)
Tmin <- melt(Tmin, id=c("codigo","year","mes"))
Tmin$value<-as.numeric(as.character(Tmin$value))
names(Tmin)<-c("codigo","year","month","day","Tmin")
Tmin<-data.frame(Tmin)
Tmin$day<-as.character(Tmin$day)
Tmin$day<-gsub("d", "", Tmin$day)
Tmin$day<-as.numeric(Tmin$day)
Tmin$date<-paste(Tmin$year,Tmin$month,Tmin$day, sep="-")
Tmin$date<-as.Date(Tmin$date, format = "%Y-%m-%d")
Tmin<-Tmin[order(Tmin$codigo,Tmin$year, Tmin$month, Tmin$day),]
Tmin$Tmin<-replace(Tmin$Tmin, is.na(Tmin$Tmin), -99.9)
Tmin$date2<-Tmin$date
Tmin$date2<-ifelse(is.na(Tmin$date2),1,0)
Tmin[Tmin$date2==1,]<-NA
Tmin<-Tmin[order(Tmin$codigo,Tmin$year, Tmin$month, Tmin$day),]
Tmin$date2<-NULL
Tmin<-na.omit(Tmin)
Tmin_ok<-data.frame()
for(i in unique(Tmin$codigo)){
sub<-Tmin[Tmin$codigo==i,]
date<-data.frame(seq(as.Date(paste(format(as.Date(min(na.omit(sub$date)), format="%Y/%m/%d"),"%Y"),"-01-01"),format = "%Y -%m-%d"),
as.Date(paste(format(as.Date(max(na.omit(sub$date)), format="%Y/%m/%d"),"%Y"),"-12-31"),format = "%Y -%m-%d"),
by="day"))
names(date)<-c("date")
date$date<-as.Date(date$date, format = "%Y-%m-%d")
sub2<-merge(date,sub, all=TRUE)
sub2$codigo<-c(i)
Tmin_ok <- rbind(Tmin_ok,data.frame(sub2))
}
Tmin_ok$date2<-Tmin_ok$date
library(splitstackshape)
Tmin_ok<-cSplit(Tmin_ok, "date2", "-")
Tmin_ok<-data.frame(Tmin_ok[,2],Tmin_ok[,7],Tmin_ok[,8],Tmin_ok[,9],Tmin_ok[,6])
names(Tmin_ok)<-c("codigo","year","month","day","Tmin")
#####################################################################
#####################################################################
## PCPT_Tmax_Tmin -> Loading database
#####################################################################
PCPT_Tmax<-merge(Precipitacion, Tmax, by=c("date","codigo", "year", "month", "day"), all=TRUE)
PCPT_Tmax_Tmin<-merge(PCPT_Tmax, Tmin, by=c("date","codigo", "year", "month", "day"), all=TRUE)
PCPT_Tmax_Tmin_ok<-data.frame()
for(i in unique(PCPT_Tmax_Tmin$codigo)){
sub<-PCPT_Tmax_Tmin[PCPT_Tmax_Tmin$codigo==i,]
date<-data.frame(seq(as.Date(paste(format(as.Date(min(na.omit(sub$date)), format="%Y/%m/%d"),"%Y"),"-01-01"),format = "%Y -%m-%d"),
as.Date(paste(format(as.Date(max(na.omit(sub$date)), format="%Y/%m/%d"),"%Y"),"-12-31"),format = "%Y -%m-%d"),
by="day"))
names(date)<-c("date")
date$date<-as.Date(date$date, format = "%Y-%m-%d")
sub2<-merge(date,sub, all=TRUE)
sub2$codigo<-c(i)
PCPT_Tmax_Tmin_ok <- rbind(PCPT_Tmax_Tmin_ok,data.frame(sub2))
}
PCPT_Tmax_Tmin_ok$date2<-PCPT_Tmax_Tmin_ok$date
library(splitstackshape)
PCPT_Tmax_Tmin_ok<-cSplit(PCPT_Tmax_Tmin_ok, "date2", "-")
PCPT_Tmax_Tmin_ok<-data.frame(PCPT_Tmax_Tmin_ok[,2],PCPT_Tmax_Tmin_ok[,9],PCPT_Tmax_Tmin_ok[,10],PCPT_Tmax_Tmin_ok[,11],PCPT_Tmax_Tmin_ok[,6],PCPT_Tmax_Tmin_ok[,7],PCPT_Tmax_Tmin_ok[,8])
names(PCPT_Tmax_Tmin_ok)<-c("codigo","year","month","day","PCPT","Tmax","Tmin")
#####################################################################
#####################################################################
## Save database for RClimDex
#####################################################################
pb <- winProgressBar(title="Hola, :-) .. espera solo un poco estoy creado los RClimDex txt ... ", label="0% done", min=0, max=100, initial=0, width = 900)
for(i in unique(PCPT_Tmax_Tmin_ok$codigo)){
sub<-PCPT_Tmax_Tmin_ok[PCPT_Tmax_Tmin_ok$codigo==i,]
sub<-data.frame(sub)
sub$PCPT<-replace(sub$PCPT, is.na(sub$PCPT), -99.9)
sub$Tmax<-replace(sub$Tmax, is.na(sub$Tmax), -99.9)
sub$Tmin<-replace(sub$Tmin, is.na(sub$Tmin), -99.9)
write.table(sub[,c(-1)],paste("Results/Estaciones/",i,"_RClimDex.txt"),sep="\t",dec=".", col.names = F, row.names = F)
for (ii in 1:100){
Sys.sleep(0.0005) # slow down the code for illustration purposes
info <- sprintf("%d%% done", round((ii/100)*100))
setWinProgressBar(pb, ii/(100)*100, label=info)
}
}
close(pb)
#####################################################################
#####################################################################
## Database... PCPT_Tmax_Tmin_ok
#####################################################################
PCPT_Tmax_Tmin_ok$PCPT<-replace(PCPT_Tmax_Tmin_ok$PCPT, PCPT_Tmax_Tmin_ok$PCPT==-99.9, NA)
PCPT_Tmax_Tmin_ok$Tmax<-replace(PCPT_Tmax_Tmin_ok$Tmax, PCPT_Tmax_Tmin_ok$Tmax==-99.9, NA)
PCPT_Tmax_Tmin_ok$Tmin<-replace(PCPT_Tmax_Tmin_ok$Tmin, PCPT_Tmax_Tmin_ok$Tmin==-99.9, NA)
PCPT_Tmax_Tmin_ok$date<-as.Date(paste(PCPT_Tmax_Tmin_ok$year, PCPT_Tmax_Tmin_ok$month, PCPT_Tmax_Tmin_ok$day, sep="-"))
PCPT_Tmax_Tmin_ok$year_month<-paste(PCPT_Tmax_Tmin_ok$year, PCPT_Tmax_Tmin_ok$month, "1", sep="-")
library("plyr", lib.loc="~/R/win-library/3.4")
##########################
#---- (50% completa Temperatura mensual) ----#
#---- (80% completa Precipitación mensual) ----#
##########################
COMPLETA<-15 # (15/30)*100= 50% INFORMACIÓN COMPLETA TEMPERATURA MENSUAL
COMPLETA_PCPT<-24 # (24/30)*100= 80% INFORMACIÓN COMPLETA PRECIPITACIÓN MENSUAL
library(plyr)
str(PCPT_Tmax_Tmin_ok)
PCPT_Tmax_Tmin_ok$PCPT<-as.numeric(PCPT_Tmax_Tmin_ok$PCPT)
PCPT_Tmax_Tmin_ok$Tmax<-as.numeric(PCPT_Tmax_Tmin_ok$Tmax)
PCPT_Tmax_Tmin_ok$Tmin<-as.numeric(PCPT_Tmax_Tmin_ok$Tmin)
PCPT_Tmax_Tmin_sum<-ddply(PCPT_Tmax_Tmin_ok, c("codigo","year_month"), summarise,
PCPT_sum = ifelse(length(na.omit(PCPT))>COMPLETA_PCPT, sum(na.omit(PCPT)),""),
Tmax_max = ifelse(length(na.omit(Tmax))>COMPLETA, max(na.omit(Tmax)),""),
Tmax_mean = ifelse(length(na.omit(Tmax))>COMPLETA, mean(na.omit(Tmax)),""),
Tmax_min = ifelse(length(na.omit(Tmax))>COMPLETA, min(na.omit(Tmax)),""),
Tmin_max = ifelse(length(na.omit(Tmin))>COMPLETA, max(na.omit(Tmin)),""),
Tmin_mean = ifelse(length(na.omit(Tmin))>COMPLETA, mean(na.omit(Tmin)),""),
Tmin_min = ifelse(length(na.omit(Tmin))>COMPLETA, min(na.omit(Tmin)),""))
PCPT_Tmax_Tmin_sum$year_month<-as.Date(PCPT_Tmax_Tmin_sum$year_month, format = "%Y-%m-%d")
##########################
#---- (80% completa Precipitación anual) ----#
##########################
COMPLETA<-292 #(292/365)*100= 80% INFORMACIÓN COMPLETA PRECIPITACION ANUAL
PCPT_anual<-ddply(PCPT_Tmax_Tmin_ok, c("codigo","year"), summarise,
PCPT_sum= ifelse(length(na.omit(PCPT))>COMPLETA, sum(na.omit(PCPT)),""))
PCPT_anual$year<-as.numeric(PCPT_anual$year)
write.table(PCPT_Tmax_Tmin_sum,"Results/PCPT_Tmax_Tmin_sum.txt",sep="\t",dec=".", col.names = T, row.names = F)
write.table(PCPT_anual,"Results/PCPT_anual.txt",sep="\t",dec=".", col.names = T, row.names = F)
#####################################################################Anexo 6.Creación de gráficos de precipitación y temperatura mensual
## Smart graphics
#####################################################################
####################################
Nombre_estacion<-"81003" #########PCPT_anual PCPT_Tmax_Tmin_sum
####################################
PCPT_anual<-read.delim("Results/PCPT_anual.txt",header=TRUE,sep="\t",dec=".")
PCPT_anual$year<-as.numeric(PCPT_anual$year)
PCPT_anual$year<-as.Date(paste(PCPT_anual$year,"-01-01"), format = "%Y -%m-%d")
PCPT_Tmax_Tmin_sum<-read.delim("Results/PCPT_Tmax_Tmin_sum.txt",header=TRUE,sep="\t",dec=".")
PCPT_Tmax_Tmin_sum$year_month<-as.Date(PCPT_Tmax_Tmin_sum$year_month, format = "%Y-%m-%d")
#####################################################################
library(ggplot2)
library(xts)
library(dygraphs)
# Function
presAnnotation <- function(dygraph, x, text) {
dygraph %>%
dyAnnotation(x, text, attachAtBottom = TRUE, width = 40)
}
#####################################################################
## Temperatura escala mensual ---- (50% completa) ----
#####################################################################
sub1<-PCPT_Tmax_Tmin_sum[PCPT_Tmax_Tmin_sum$codigo==Nombre_estacion,]
Tmax_max<- xts(sub1$Tmax_max,order.by=(sub1$year_month),tz="GMT")
Tmax_mean<- xts(sub1$Tmax_mean,order.by=(sub1$year_month),tz="GMT")
Tmax_min<- xts(sub1$Tmax_min,order.by=(sub1$year_month),tz="GMT")
Tmin_max<- xts(sub1$Tmin_max,order.by=(sub1$year_month),tz="GMT")
Tmin_mean<- xts(sub1$Tmin_mean,order.by=(sub1$year_month),tz="GMT")
Tmin_min<- xts(sub1$Tmin_min,order.by=(sub1$year_month),tz="GMT")
stocks1 <- cbind(Tmax_max,Tmax_mean,Tmax_min,Tmin_max,Tmin_mean,Tmin_min)
dygraph(stocks1,ylab=("Temperature (°C)"), main=paste("Estación: ", Nombre_estacion)) %>%
dySeries("..1",label="Temp. max maximum (°C)", drawPoints = TRUE, pointSize = 3) %>%
dySeries("..2",label="Temp. max promedio (°C)", drawPoints = TRUE, pointSize = 3) %>%
dySeries("..3",label="Temp. max minimum (°C)", drawPoints = TRUE, pointSize = 3) %>%
dySeries("..4",label="Temp. min maximum (°C)", drawPoints = TRUE, pointSize = 3) %>%
dySeries("..5",label="Temp. min promedio (°C)", drawPoints = TRUE, pointSize = 3) %>%
dySeries("..6",label="Temp. min minimum (°C)", drawPoints = TRUE, pointSize = 3) %>%
dyOptions(colors = c("darkred","red","darksalmon","skyblue","orange","yellow")) %>%
dyHighlight(highlightSeriesOpts = list(strokeWidth = 1))%>%
dyLegend(width = 400)%>%
dyRangeSelector()
#####################################################################
## Precipitación total mensual ---- (80% completa) ----
#####################################################################
PCPT<- xts(sub1$PCPT_sum,order.by=(sub1$year_month),tz="GMT")
stocks1 <- cbind(PCPT)
dygraph(stocks1,ylab=("Precipitación total mensual (mm)"),main=paste("Estación: ", Nombre_estacion)) %>%
dySeries(label="Precipitación total mensual (mm)", stepPlot = TRUE, fillGraph = TRUE, color = "blue") %>%
dyOptions(colors = c("blue"), fillGraph = TRUE, fillAlpha = 0.4) %>%
dyHighlight(highlightSeriesOpts = list(strokeWidth = 1))%>%
dyLegend(width = 400)%>%
dyRangeSelector()
#####################################################################
## Precipitación total anual ---- (80% completa) ----
#####################################################################
sub2<-PCPT_anual[PCPT_anual$codigo==Nombre_estacion,]
PCPT<- xts(sub2$PCPT_sum,order.by=(sub2$year),tz="GMT")
stocks1 <- cbind(PCPT)
dygraph(stocks1,ylab=("Precipitación total anual (mm)"), main=paste("Estación: ", Nombre_estacion)) %>%
dySeries(label="Precipitación total anual (mm)", stepPlot = TRUE, fillGraph = TRUE, color = "blue") %>%
dyOptions(colors = c("blue"), fillGraph = TRUE, fillAlpha = 0.4) %>%
dyHighlight(highlightSeriesOpts = list(strokeWidth = 1))%>%
dyLegend(width = 400)%>%
dyRangeSelector()
#####################################################################
#####################################################################
## Graphics PDF
#####################################################################
pdf(file="Results/Fig. Temperatura mensual.pdf", width = 10, height = 6)
par(mfrow=c(1,1),mgp = c(1.5,0.5,0), mar = c(3,3,4,1.5))
for (i in unique(PCPT_Tmax_Tmin_sum$codigo)){
sub<-PCPT_Tmax_Tmin_sum[PCPT_Tmax_Tmin_sum$codigo==i,]
sub<-sub[order(sub$codigo,sub$year_month),]
sub$Tmax_max[!is.finite(sub$Tmax_max)] <- NA
sub$Tmax_mean[!is.finite(sub$Tmax_mean)] <- NA
sub$Tmax_min[!is.finite(sub$Tmax_min)] <- NA
sub$Tmin_max[!is.finite(sub$Tmin_max)] <- NA
sub$Tmin_mean[!is.finite(sub$Tmin_mean)] <- NA
sub$Tmin_min[!is.finite(sub$Tmin_min)] <- NA
par(xpd=FALSE)
plot(sub$year_month, sub$Tmax_max, type = "b", pch=19, col="darkred", ylim=c(ifelse(min(na.omit(sub$Tmin_min))=="Inf",-1,min(na.omit(sub$Tmin_min))), ifelse(max(na.omit(sub$Tmax_max))=="-Inf",-1,max(na.omit(sub$Tmax_max)))),
main=paste("Estacion= ", i), ylab="Temperatura mensual (°C)", xlab="")
points(sub$year_month, sub$Tmax_mean, type = "b", pch=19, col="red")
points(sub$year_month, sub$Tmax_min, type = "b", pch=19, col="darksalmon")
points(sub$year_month, sub$Tmin_max, type = "b", pch=19, col="skyblue")
points(sub$year_month, sub$Tmin_mean, type = "b", pch=19, col="orange")
points(sub$year_month, sub$Tmin_min, type = "b", pch=19, col="yellow")
par(xpd=TRUE)
legend("top", inset = c(0, -0.08), "", c("Tmax_max","Tmax_mean","Tmax_min","Tmin_max","Tmin_mean","Tmin_min"), pch=c(19),col=c("darkred","red","darksalmon","skyblue","orange","yellow"),h=TRUE, text.col = c("darkred","red","darksalmon","skyblue","orange","yellow"),merge = F, bg = NULL,bty='n')
}
dev.off()
#####################################################################
pdf(file="Results/Fig. Precipitacion total mensual.pdf", width = 10, height = 6)
par(mfrow=c(1,1),mgp = c(1.5,0.5,0), mar = c(3,3,4,1.5))
for (i in unique(PCPT_Tmax_Tmin_sum$codigo)){
sub<-PCPT_Tmax_Tmin_sum[PCPT_Tmax_Tmin_sum$codigo==i,]
sub<-sub[order(sub$codigo,sub$year_month),]
sub$PCPT_sum[!is.finite(sub$PCPT_sum)] <- NA
sub$Tmax_max[!is.finite(sub$Tmax_max)] <- NA
sub$Tmax_mean[!is.finite(sub$Tmax_mean)] <- NA
sub$Tmax_min[!is.finite(sub$Tmax_min)] <- NA
sub$Tmin_max[!is.finite(sub$Tmin_max)] <- NA
sub$Tmin_mean[!is.finite(sub$Tmin_mean)] <- NA
sub$Tmin_min[!is.finite(sub$Tmin_min)] <- NA
par(xpd=FALSE)
plot(sub$year_month, sub$PCPT_sum, type = "h", pch=19, col="blue", ylim=c(ifelse(min(na.omit(sub$PCPT_sum))=="Inf",0,0), ifelse(max(na.omit(sub$PCPT_sum))=="-Inf",0,max(na.omit(sub$PCPT_sum)))),
main=paste("Estacion= ", i), ylab="Precipitacion total mensual (mm)", xlab="", lwd=3)
points(sub$year_month, sub$PCPT_sum, type="l", col="red", lty=3,lwd=1)
abline(h=0, col="black", lty=1, lwd=2)
par(xpd=TRUE)
legend("top", inset = c(0, -0.08), "", c("Precipitación total mensual"),lty=1, lwd=3,col=c("blue"),h=TRUE, text.col = c("blue"),merge = F, bg = NULL,bty='n')
}
dev.off()
#####################################################################
pdf(file="Results/Fig. Precipitacion total anual.pdf", width = 10, height = 6)
par(mfrow=c(1,1),mgp = c(1.5,0.5,0), mar = c(3,3,4,1.5))
for (i in unique(PCPT_anual$codigo)){
sub<-PCPT_anual[PCPT_anual$codigo==i,]
sub<-sub[order(sub$codigo,sub$year),]
sub$PCPT_sum[!is.finite(sub$PCPT_sum)] <- NA
par(xpd=FALSE)
plot(sub$year, sub$PCPT_sum, type = "h", pch=19, col="blue", ylim=c(ifelse(min(na.omit(sub$PCPT_sum))=="Inf",0,0), ifelse(max(na.omit(sub$PCPT_sum))=="-Inf",0,max(na.omit(sub$PCPT_sum)))),
main=paste("Estacion= ", i), ylab="Precipitacion total anual (mm)", xlab="", lwd=3)
points(sub$year, sub$PCPT_sum, type="l", col="red", lty=3,lwd=1)
abline(h=0, col="black", lty=1, lwd=2)
par(xpd=TRUE)
legend("top", inset = c(0, -0.08), "", c("Precipitación total mensual"),lty=1, lwd=3,col=c("blue"),h=TRUE, text.col = c("blue"),merge = F, bg = NULL,bty='n')
}
dev.off()
#####################################################################
#####################################################################
## Save database summary (monthly and year) for each station
#####################################################################
pb <- winProgressBar(title="Hola de nuevo, :-) .. espera solo un poco estoy creado database para cada estación meteorológica en formato.txt ... ", label="0% done", min=0, max=100, initial=0, width = 900)
for(i in unique(PCPT_Tmax_Tmin_sum$codigo)){
sub<-PCPT_Tmax_Tmin_sum[PCPT_Tmax_Tmin_sum$codigo==i,]
sub<-sub[order(sub$codigo,sub$year_month),]
sub$Tmax_max[!is.finite(sub$Tmax_max)] <- NA
sub$Tmax_mean[!is.finite(sub$Tmax_mean)] <- NA
sub$Tmax_min[!is.finite(sub$Tmax_min)] <- NA
sub$Tmin_max[!is.finite(sub$Tmin_max)] <- NA
sub$Tmin_mean[!is.finite(sub$Tmin_mean)] <- NA
sub$Tmin_min[!is.finite(sub$Tmin_min)] <- NA
write.table(sub,paste("Results/Estaciones datos resumidos/",i,"_database_mensual.txt"),sep="\t",dec=".", col.names = T, row.names = F)
for (ii in 1:100){
Sys.sleep(0.0005) # slow down the code for illustration purposes
info <- sprintf("%d%% done", round((ii/100)*100))
setWinProgressBar(pb, ii/(100)*100, label=info)
}
}
close(pb)
#####################################################################
pb <- winProgressBar(title="Hola de nuevo, Soy yo otra vez :-) .. espera solo un poco estoy creado database para cada estación meteorológica en formato.txt ... ", label="0% done", min=0, max=100, initial=0, width = 900)
for(i in unique(PCPT_anual$codigo)){
sub<-PCPT_anual[PCPT_anual$codigo==i,]
sub<-sub[order(sub$codigo,sub$year),]
sub$PCPT_sum[!is.finite(sub$PCPT_sum)] <- NA
write.table(sub,paste("Results/Estaciones datos resumidos/",i,"_database_anual.txt"),sep="\t",dec=".", col.names = T, row.names = F)
for (ii in 1:100){
Sys.sleep(0.0005) # slow down the code for illustration purposes
info <- sprintf("%d%% done", round((ii/100)*100))
setWinProgressBar(pb, ii/(100)*100, label=info)
}
}
close(pb)Anexo 7.Instalación de paquetes requeridos por RClimdex
install.packages("cluster")
install.packages("tcltk")
install.packages("Rcmdr")Anexo 8.Código para generar el software RClimdex
##################################################################################
#"Script RClimdex"
# Rewritten: Junior Pastor PÉREZ-MOLINA
# Small modifications to adapt the analysis of climate variability in the tropics
# Date modified: "Tuesday May 31st, 2016"
##################################################################################
rm(list = ls()) #Remove all objects
graphics.off() #Remove all graphics
cat("\014") #Remove script in windows console
##################################################################################
##################################################################################
# Credicts:
# Climate Indecies Calculation software
# R language with TCL/TK package
# Programmed by Yujun Ouyang,Mar,2004
# rewritten by Yang Feng, July 2004
##################################################################################
# version 1.0, 2004-10-14
# modified, 2006-01-24,
# change .Internal(cbind) to cbind
# change .Internal(rbind) to rbind
# modified 2007-03-23
# change .Internal(rep(0,n)) to rep(0,n)
# modified, 2007-11-26,
# get rid of .Internal on some functions: min(...), sort(...), round(...)
# max(...), also get rid of dig=.. part from sort(...) function
# change decrease=... part to decreasing=... at sort(...)
# modified, 2008-05-05,
# output TMAX mean value and TMIN mean value in nastat() function
# modified, 2008-05-06,
# add a random series on TMAX and TMIN in exceedance rate function
# modified thresholds in TN10p calculation, add an 1e-5 item to avoid
# computational error like 3.60000 > 3.6000, two functions involved:
# nordaytem1() and exceedance()
# modified, 2008-06-16
# change all sort() to mysort(), deal with different version, also combined
# different levels threshold()
##################################################################################
##################################################################################
# Part I
# General functions & TCL/TK functions
##################################################################################
library(cluster)
require(tcltk)
mysort<- if(getRversion()<='2.4.1') function(x,decreasing){.Internal(sort(x,decreasing=decreasing))} else function(x,decreasing){sort.int(x,decreasing=decreasing)}
fontHeading <- tkfont.create(family="times",size=40,weight="bold",slant="italic")
fontHeading1<-tkfont.create(family="times",size=20,weight="bold")
fontHeading2<-tkfont.create(family="times",size=14,weight="bold")
fontTextLabel <- tkfont.create(family="times",size=12)
fontFixedWidth <- tkfont.create(family="courier",size=12)
# initial value for check box
cbvalue1<-tclVar("1"); cbvalue2<-tclVar("1"); cbvalue3<-tclVar("1")
cbvalue4<-tclVar("1"); cbvalue5<-tclVar("1"); cbvalue6<-tclVar("1")
cbvalue7<-tclVar("1"); cbvalue8<-tclVar("1"); cbvalue9<-tclVar("1")
cbvalue10<-tclVar("1"); cbvalue11<-tclVar("1"); cbvalue12<-tclVar("1")
cbvalue13<-tclVar("1"); cbvalue14<-tclVar("1"); cbvalue15<-tclVar("1")
cbvalue16<-tclVar("0"); cbvalue17<-tclVar("0"); cbvalue18<-tclVar("0")
cbvalue19<-tclVar("0"); cbvalue21<-tclVar("1")
# initial value for parameters
stations<-tclVar(paste("")); stdt<-tclVar(paste("3"))
Entry1<-tclVar(paste("1961")); Entry2<-tclVar(paste("1990"))
#Entry3<-tclVar(paste("5"))
Entry4<-tclVar(paste("0"))
Entry5<-tclVar(paste("0"))
Entry6<-tclVar(paste("25")); Entry7<-tclVar(paste("0"))
Entry8<-tclVar(paste("20")); Entry9<-tclVar(paste("0"))
#Entry10<-tclVar(paste("10")); Entry11<-tclVar(paste("5"))
Entry12<-tclVar(paste("25"))
dayim<-as.integer(c(31,28,31,30,31,30,31,31,30,31,30,31))
crt<-3; flag=F
treshold=5; winsize=5
uu<-25; lu<-20
ul<-0; ll<-0
title1<-"Plot of Ind143"; title2<-"Ind143"; title3<-"Years"
#----------- frc -----------------------------------------
frc<-function(dd,year,month,item){
a<-dd[dd$year==year & dd$month==month,item]
a<-a[a>-99]
frc<-length(a)/rdim(year,month)
}#end
#----------- frc ends -----------------------------------------
#----------- done -----------------------------------------
done<-function(){tkdestroy(start1)}
#----------- done ends -----------------------------------------
#----------- percentile -----------------------------------------
percentile<-function(n,x,pctile){
x1<-x[is.na(x)==F]
n1<-length(x1)
a<-mysort(x1,decreasing=F)
b<-n1*pctile+0.3333*pctile+0.3333
bb<-trunc(b)
percentile<-a[bb]+(b-bb)*(a[bb+1]-a[bb])
}#end
#----------- percentile ends -----------------------------------------
#----------- pplotts -----------------------------------------
pplotts<-function(var="prcp",type="h",tit=NULL){
if(var=="dtr"){
ymax<-max(dd[,"tmax"]-dd[,"tmin"],na.rm=T)
ymin<-0
}
else if(var=="prcp"){
ymax<-max(dd[,var],na.rm=T)
ymin<-0
}
else{
ymax<-max(dd[,var],na.rm=T)+1
ymin<-min(dd[,var],na.rm=T)-1
}
if(is.na(ymax)|is.na(ymin)|(ymax==-Inf)|(ymin==-Inf)){
ymax<-100
ymin<-(-100)
}
par(mfrow=c(4,1))
par(mar=c(3.1,2.1,2.1,2.1))
for(i in seq(years,yeare,10)){
at<-rep(1,10)
# if(i>yeare)
for(j in (i+1):min(i+9,yeare+1)){
if(leapyear(j)) at[j-i+1]<-at[j-i]+366
else at[j-i+1]<-at[j-i]+365
}
if(var=="dtr")
ttmp<-dd[dd$year>=i&dd$year<=min(i+9,yeare),"tmax"]-dd[dd$year>=i&dd$year<=min(i+9,yeare),"tmin"]
else ttmp<-dd[dd$year>=i&dd$year<=min(i+9,yeare),var]
plot(1:length(ttmp),ttmp,type=type,col="blue",xlab="",ylab="",xaxt="n",xlim=c(1,3660),ylim=c(ymin,ymax))
abline(h=0)
tt<-seq(1,length(ttmp))
tt<-tt[is.na(ttmp)==T]
axis(side=1,at=at,labels=c(i:(i+9)))
for(k in 1:10) abline(v=at[k],col="yellow")
lines(tt,rep(0,length(tt)),type="p",col="red")
title(paste("Station: ",tit,", ",i,"~",min(i+9,yeare),", ",var,sep=""))
}
}
#----------- pplotts ends -----------------------------------------
#----------- ind143gsl -----------------------------------------
ind143gsl<-function(){
if (latitude<0) south=T else south=F
if (latitude<0) eyear=yeare-1 else eyear=yeare
threshold<-5
a<-eyear-years+1
b<-rep(0,a)
b<-cbind(b,b)
colnames(b)<-c("year","gsl")
i=1
for (year in years:eyear) {
b[i,"year"]<-year
if(south){
gslstart<-dd[dd$year==year&dd$month>6,]
gslstart<-(gslstart[,"tmax"]+gslstart[,"tmin"])/2
gslend<-dd[dd$year==(year+1)&dd$month<7,]
gslend<-(gslend[,"tmax"]+gslend[,"tmin"])/2
}
else{
gslstart<-dd[dd$year==year&dd$month<7,]
gslstart<-(gslstart[,"tmax"]+gslstart[,"tmin"])/2
gslend<-dd[dd$year==year&dd$month>6,]
gslend<-(gslend[,"tmax"]+gslend[,"tmin"])/2
}
beginday<-0
count=0
for(step in 1:length(gslstart)){
if(is.na(gslstart[step])==F){
if(gslstart[step]>threshold) count<-count+1
else count<-0
}
else count<-0
if(count>5){
beginday<-step-5
break
}
}
# if(beginday==0){
# b[i,"gsl"]<-0
# break
# }
endday<-0
count<-0
for(step in 1:length(gslend)){
if(is.na(gslend[step])==F){
if(gslend[step]<threshold) count<-count+1
else count<-0
}
else count<-0
if(count>5){
endday<-step-5
break
}
}
if(sum(is.na(gslstart))+sum(is.na(gslend))>15) b[i,"gsl"]<-NA
else{
if(beginday==0)
b[i,"gsl"]<-0
else {
if(endday==0) b[i,"gsl"]<-length(gslend)+length(gslstart)-beginday
else b[i,"gsl"]<-endday+length(gslstart)-beginday
}
}
i=i+1
}
b<-as.data.frame(b)
nam1<-paste(outinddir,paste(ofilename,"_GSL.csv",sep=""),sep="/")
write.table(b,file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
if(sum(is.na(b[,"gsl"]))>=(yeare-years+1-10)){
betahat<-NA
betastd<-NA
pvalue<-NA
}
else{
fit1<-lsfit(b[,1],b[,"gsl"])
out1<-ls.print(fit1,print.it=F)
pvalue<-round(as.numeric(out1$summary[1,6]),3)
betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
}
cat(file=namt,paste(latitude,longitude,"gsl",years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
nam2<-paste(outjpgdir,paste(ofilename,"_GSL.jpg",sep=""),sep="/")
jpeg(file=nam2,width=1024,height=768)
plotx(b[,1],b[,"gsl"],main=paste("GSL",ofilename,sep=" "),xlab="Year",ylab="GSL")
dev.off()
}
#----------- ind143gsl ends -----------------------------------------
#----------- dataext -----------------------------------------
# seems this function is never used...
dataext<-function(dd,year,month,day,item){
Dataext<-dd[dd$year==year & dd$month==month & dd$day==day,item]
}
#----------- dataext ends -----------------------------------------
# ----------- leapyear -----------------------------------------
# check if 'year' is a leap year
# returns T or F
leapyear<-function(year){
remainder400 <-trunc(year-400*trunc(year/400))
remainder100 <-trunc(year-100*trunc(year/100))
remainder4 <-trunc(year-4*trunc(year/4))
if (remainder400 == 0) leapyear = T
else{
if(remainder100 == 0) leapyear = F
else{
if(remainder4 == 0) leapyear = T
else leapyear = F
}
}
}
# ----------- leapyear ends -----------------------------------------
# ----------- rdim -----------------------------------------
# day # in a month
#
rdim<-function(year,month) {
a<-leapyear(year)
if (month==1) rdim<-31
else if (month==3) rdim<-31
else if (month==4) rdim<-30
else if (month==5) rdim<-31
else if (month==6) rdim<-30
else if (month==7) rdim<-31
else if (month==8) rdim<-31
else if (month==9) rdim<-30
else if (month==10) rdim<-31
else if (month==11) rdim<-30
else if (month==12) rdim<-31
else if (a==T & month==2) rdim<-29
else rdim<-28
}
# ----------- rdim ends -----------------------------------------
# ----------- qcontrol -----------------------------------------
qcontrol<-function(){
tkmessageBox(message=paste("Data QC(",ofilename,") may take a few minutes, click OK to continue.",sep=""))
# output records of problematic like prcp <0 and NA
ddu<-duplicated(dd[,c("year","month","day")])
if(sum(ddu)>0){
nam1<-paste(outlogdir,paste(ofilename,"dupliQC.csv",sep=""),sep="/")
msg=paste("Date duplicated found in original data file, please check:",nam1,sep=" ")
tkmessageBox(message=msg)
ddu2<-dd[duplicated(dd[,c("year","month","day")])==T,c("year", "month", "day")]
nam1<-paste(outlogdir,paste(ofilename,"dupliQC.csv",sep=""),sep="/")
write.table(ddu2,file=nam1,append=F,quote=F,sep=", ",row.names=F)
tkdestroy(start1)
stop(paste("QC stopped due to duplicated date, please check ",nam1,sep=""))
}
mid<-dd[is.na(dd$prcp)==F,] # choose non-MISSING PRCP
mid<-mid[mid$prcp<0,] # Is there unreasonable PRCP?
#dd[is.na(dd$prcp)==F & dd$prcp<0,"prcp"]<-NA
nam1<-paste(outlogdir,paste(ofilename,"_prcpQC.csv",sep=""),sep="/")
write.table(mid,file=nam1,append=F,quote=F,sep=", ",row.names=F)
if (dim(mid)[1]>0) tkmessageBox(message=paste("Errors in prcp, please check the log file",nam1,sep=" "))
# output plots for PRCP
nam1<-paste(outlogdir,paste(ofilename,"_prcpPLOT.pdf",sep=""),sep="/")
pdf(file=nam1)
ttmp<-dd[dd$prcp>=1,"prcp"]
ttmp<-ttmp[is.na(ttmp)==F]
if(length(ttmp)>30){
hist(ttmp,main=paste("Histogram for Station:",ofilename," of PRCP>=1mm",sep=""),breaks=c(seq(0,20,2),max(30,ttmp)),xlab="",col="green",freq=F)
lines(density(ttmp,bw=0.2,from=1),col="red")
}
pplotts(var="prcp",tit=ofilename)
dev.off()
nam1<-paste(outlogdir,paste(ofilename,"_tmaxPLOT.pdf",sep=""),sep="/")
pdf(file=nam1)
pplotts(var="tmax",type="l",tit=ofilename)
dev.off()
nam1<-paste(outlogdir,paste(ofilename,"_tminPLOT.pdf",sep=""),sep="/")
pdf(file=nam1)
pplotts(var="tmin",type="l",tit=ofilename)
dev.off()
nam1<-paste(outlogdir,paste(ofilename,"_dtrPLOT.pdf",sep=""),sep="/")
pdf(file=nam1)
pplotts(var="dtr",type="l",tit=ofilename)
dev.off()
#par(mfrow=c(1,1))
# output problematic temperature like tmax < tmin
mm<-dd[,"tmax"]-dd[,"tmin"]
dd<-cbind(dd,mm)
dimnames(dd)[[2]][7]<-"dtr"
#output "log" file review
temiss<-dd
temiss<-temiss[is.na(temiss[,"tmax"])==F&is.na(temiss[,"tmin"])==F,]
# temiss<-temiss[is.na(temiss[,6])==F,]
temiss<-temiss[temiss[,7]<=0|temiss[,5]<=(-70)|temiss[,5]>=70|temiss[,6]<=(-70)|temiss[,6]>=70,]
dimnames(temiss)[[2]][7]<-"tmax-tmin"
nam1<-paste(outlogdir,paste(ofilename,"_tempQC.csv",sep=""),sep="/")
write.table(temiss,file=nam1,append=F,quote=F,sep=", ",row.names=F)
if (dim(temiss)[1]>0) {
tkmessageBox(message=paste("Errors in temperature, please check the log file",nam1,sep=" "))
# records with abs(tmax)>=70, abs(tmin)>=70 set to NA
dd[is.na(dd[,5])==F & abs(dd[,5])>=70,5]<-NA # This is different from Fclimdex code !!!!!!!!!!!!!!!!!!!!!!!!!!!!
dd[is.na(dd[,6])==F & abs(dd[,6])>=70,6]<-NA
# records with tmax < tmin are set to NA
dd[is.na(dd[,5])==F & is.na(dd[,6])==F & dd[,"dtr"]<0,c("tmax","tmin")]<-NA
# dd[is.na(dd[,5])==F & dd[,"mm"]<0,"tmin"]<-NA
}
# dd<-dd[,-7]
# output problematic temperature which is out of 3 standard diviation (temp only)
ys<-yeare-years+1
tmaxm<-matrix(0,ys,365)
tminm<-matrix(0,ys,365)
tdtrm<-matrix(0,ys,365)
tmaxstd<-rep(0,365)
tminstd<-rep(0,365)
tdtrstd<-rep(0,365)
tmaxmean<-rep(0,365)
tminmean<-rep(0,365)
tdtrmean<-rep(0,365)
for(i in 1:ys)
tmaxm[i,]<-dd[dd[,"year"]==(i+years-1)&(dd[,"month"]*100+dd[,"day"]!=229),"tmax"]
for(i in 1:365){
tmaxstd[i]<-sqrt(var(tmaxm[,i],na.rm=T))
tmaxmean[i]<-mean(tmaxm[,i],na.rm=T)
}
for(i in 1:ys)
tminm[i,]<-dd[dd[,"year"]==(i+years-1)&(dd[,"month"]*100+dd[,"day"]!=229),"tmin"]
for(i in 1:365){
tminstd[i]<-sqrt(var(tminm[,i],na.rm=T))
tminmean[i]<-mean(tminm[,i],na.rm=T)
}
for(i in 1:ys)
tdtrm[i,]<-dd[dd[,"year"]==(i+years-1)&(dd[,"month"]*100+dd[,"day"]!=229),"dtr"]
for(i in 1:365){
tdtrstd[i]<-sqrt(var(tdtrm[,i],na.rm=T))
tdtrmean[i]<-mean(tdtrm[,i],na.rm=T)
}
tmaxstdleap<-rep(0,366)
tmaxstdleap[1:59]<-tmaxstd[1:59]
tmaxstdleap[60]<-tmaxstd[59]
tmaxstdleap[61:366]<-tmaxstd[60:365]
tmaxmeanleap<-rep(0,366)
tmaxmeanleap[1:59]<-tmaxmean[1:59]
tmaxmeanleap[60]<-tmaxmean[59]
tmaxmeanleap[61:366]<-tmaxmean[60:365]
tminstdleap<-rep(0,366)
tminstdleap[1:59]<-tminstd[1:59]
tminstdleap[60]<-tminstd[59]
tminstdleap[61:366]<-tminstd[60:365]
tminmeanleap<-rep(0,366)
tminmeanleap[1:59]<-tminmean[1:59]
tminmeanleap[60]<-tminmean[59]
tminmeanleap[61:366]<-tminmean[60:365]
tdtrstdleap<-rep(0,366)
tdtrstdleap[1:59]<-tdtrstd[1:59]
tdtrstdleap[60]<-tdtrstd[59]
tdtrstdleap[61:366]<-tdtrstd[60:365]
tdtrmeanleap<-rep(0,366)
tdtrmeanleap[1:59]<-tdtrmean[1:59]
tdtrmeanleap[60]<-tdtrmean[59]
tdtrmeanleap[61:366]<-tdtrmean[60:365]
tmp<-matrix(0,dim(dd)[1],6)
dimnames(tmp)<-list(NULL,c("tmaxlow","tmaxup","tminlow","tminup","dtrlow","dtrup"))
idx<-0
for(i in years:yeare){
if(leapyear(i)==T){
tmp[(idx+1):(idx+366),1]<-tmaxmeanleap-crt*tmaxstdleap
tmp[(idx+1):(idx+366),2]<-tmaxmeanleap+crt*tmaxstdleap
tmp[(idx+1):(idx+366),3]<-tminmeanleap-crt*tminstdleap
tmp[(idx+1):(idx+366),4]<-tminmeanleap+crt*tminstdleap
tmp[(idx+1):(idx+366),5]<-tdtrmeanleap-crt*tdtrstdleap
tmp[(idx+1):(idx+366),6]<-tdtrmeanleap+crt*tdtrstdleap
idx<-idx+366
}
else{
tmp[(idx+1):(idx+365),1]<-tmaxmean-crt*tmaxstd
tmp[(idx+1):(idx+365),2]<-tmaxmean+crt*tmaxstd
tmp[(idx+1):(idx+365),3]<-tminmean-crt*tminstd
tmp[(idx+1):(idx+365),4]<-tminmean+crt*tminstd
tmp[(idx+1):(idx+365),5]<-tdtrmean-crt*tdtrstd
tmp[(idx+1):(idx+365),6]<-tdtrmean+crt*tdtrstd
idx<-idx+365
}
}
odata<-cbind(dd,tmp)
odata<-odata[is.na(odata[,"tmax"])==F,]
odata<-odata[is.na(odata[,"tmin"])==F,]
odata<-odata[is.na(odata[,"dtr"])==F,]
o1data<-odata[odata[,5]<odata[,8]|odata[,5]>odata[,9]|odata[,6]<odata[,10]|odata[,6]>odata[,11]|odata[,7]<odata[,12]|odata[,7]>odata[,13],]
#o2data<-odata[odata[,5]>odata[,8],]
#o3data<-odata[odata[,6]<odata[,9],]
#o4data<-odata[odata[,6]>odata[,10],]
#write.table(errstdo,file=nam1,append=F,quote=F,sep=",",row.names=F)
if (dim(o1data)[1] > 0){
nam1<-paste(outlogdir,paste(ofilename,"_tepstdQC.csv",sep=""),sep="/")
tkmessageBox(message=paste("Outliers found, please check the log file: ",nam1,sep=""))
ofile<-cbind(o1data[,c(1,2,3,8,5,9,10,6,11,12,7,13)])
write.table(round(ofile,digit=2),file=nam1,append=F,quote=F,sep=",",row.names=F)
}
dd<-dd[,c("year","month","day","prcp","tmax","tmin")]; assign("dd",dd,envir=.GlobalEnv)
namcal<-paste(nama,"indcal.csv",sep="")
assign("namcal",namcal,envir=.GlobalEnv)
write.table(dd,file=namcal,append=F,quote=F,sep=",",row.names=F,na="-99.9")
tkmessageBox(message=paste("If you have checked data(", namcal,"), click OK to continue.",sep=""))
}
# ------------ qcontrol ends --------------------------------
# ---------------------------------------------------------
# NASTST
nastat<-function(){
dd <- read.table(namcal,header=T,sep=",",na.strings="-99.9",colClasses=rep("real",6))
assign("dd",dd,envir=.GlobalEnv)
# NA statistics
nast<-rep(0,12)
nast<-array(nast,c(1,12,12,(yeare-years+1)))
dimnames(nast)<-list(NULL,c("ynapr","ynatma","ynatmi","napr","natma","natmi","mnapr>3","mnatma>3","mnatmi>3","ynapr>15","ynatma>15","ynatmi>15"),NULL,NULL)
ys<-yeare-years+1
year=years
aa1<-matrix(NA,12*ys,4) # monthly
dimnames(aa1)<-list(NULL,c("year","month","tmaxm","tminm"))
aa1[,"year"]<-years:yeare
aa1[,"year"]<-mysort(aa1[,"year"],decreasing=F) # Is this step necessary???
aa1[,"month"]<-1:12
aa2<-matrix(NA,ys,3) # annual
dimnames(aa2)<-list(NULL,c("year","tmaxm","tminm"))
aa2[,"year"]<-years:yeare
for (i in 1:(yeare-years+1)){
month<-1; midvalue1<-dd[dd$year==year,] # data for each year
aa2[i,"tmaxm"]<-mean(midvalue1[,"tmax"],na.rm=T) # annual mean of Tmax, Tmin
aa2[i,"tminm"]<-mean(midvalue1[,"tmin"],na.rm=T)
for (j in 1:12){
midvalue2<-midvalue1[midvalue1$month==month,] # data for each month
aa1[(i-1)*12+j,"tmaxm"]<-mean(midvalue2[,"tmax"],na.rm=T) # monthly mean of Tmax, Tmin
aa1[(i-1)*12+j,"tminm"]<-mean(midvalue2[,"tmin"],na.rm=T)
nast[1,"ynapr",j,i]<-dim(midvalue1[is.na(midvalue1$prcp),])[1] # annual missing days
if (nast[1,"ynapr",j,i]>15) nast[1,"ynapr>15",j,i]<-NA
nast[1,"ynatma",j,i]<-dim(midvalue1[is.na(midvalue1$tmax),])[1]
if (nast[1,"ynatma",j,i]>15) nast[1,"ynatma>15",j,i]<-NA
nast[1,"ynatmi",j,i]<-dim(midvalue1[is.na(midvalue1$tmin),])[1]
if (nast[1,"ynatmi",j,i]>15) nast[1,"ynatmi>15",j,i]<-NA
nast[1,"napr",j,i]<-dim(midvalue2[is.na(midvalue2$prcp),])[1] # monthly missing days
if (nast[1,"napr",j,i]>3) nast[1,"mnapr>3",j,i]<-NA
nast[1,"natma",j,i]<-dim(midvalue2[is.na(midvalue2$tmax),])[1]
if (nast[1,"natma",j,i]>3) nast[1,"mnatma>3",j,i]<-NA
nast[1,"natmi",j,i]<-dim(midvalue2[is.na(midvalue2$tmin),])[1]
if (nast[1,"natmi",j,i]>3) nast[1,"mnatmi>3",j,i]<-NA
month=month+1
}
year=year+1
}
nasto<-t(nast[,,,1])
for ( i in 2:(yeare-years+1)){
nasto<-rbind(nasto,t(nast[,,,i])) }
nastout<-matrix(0,(yeare-years+1)*12,2)
dimnames(nastout)<-list(NULL,c("year","month"))
nastout<-as.data.frame(nastout)
nastout[,"year"]<-years:yeare
nastout[,"year"]<-mysort(nastout[,"year"],decreasing=F)
nastout[,"month"]<-1:12
nastout<-cbind(nastout,nasto); assign("nastout",nastout,envir=.GlobalEnv)
nastatistic<-nastout[,1:8]
nacor<-nastout[,-(3:8)]
ynacor<-matrix(0,ys,4)
dimnames(ynacor)<-list(NULL,c("year","ynapr>15","ynatma>15","ynatmi>15"))
ynacor[,"year"]<-years:yeare
ynacor<-as.data.frame(ynacor)
for (year in years:yeare){
ynacor[ynacor$year==year,"ynapr>15"]<-nacor[nacor$year==year & nacor$month==1,"ynapr>15"]
ynacor[ynacor$year==year,"ynatma>15"]<-nacor[nacor$year==year & nacor$month==1,"ynatma>15"]
ynacor[ynacor$year==year,"ynatmi>15"]<-nacor[nacor$year==year & nacor$month==1,"ynatmi>15"]
}
nacor<-nacor[,1:5]
assign("nacor",nacor,envir=.GlobalEnv)
assign("ynacor",ynacor,envir=.GlobalEnv)
if(sum(is.na(ynacor[,"ynapr>15"])==F)==0) prallna<-1
else prallna<-0
if(sum(is.na(ynacor[,"ynatma>15"])==F)==0) txallna<-1
else txallna<-0
if(sum(is.na(ynacor[,"ynatmi>15"])==F)==0) tnallna<-1
else tnallna<-0
assign("prallna",prallna,envir=.GlobalEnv)
assign("txallna",txallna,envir=.GlobalEnv)
assign("tnallna",tnallna,envir=.GlobalEnv)
assign("nastatistic",nastatistic,envir=.GlobalEnv)
nam1<-paste(outlogdir,paste(ofilename,"_nastatistic.csv",sep=""),sep="/") # Output the result
cat(file=nam1,"TITLE,YEAR,JAN,FEB,MAR,APR,MAY,JUN,JUL,AUG,SEP,OCT,NOV,DEC,ANN\n")
for(year in years:yeare)
for(i in 1:3) {
if(i==1) tit<-"PRCP"
if(i==2) tit<-"TMAX"
if(i==3) tit<-"TMIN"
line<-paste(tit,year,sep=",")
for(mon in 1:12)
line<-paste(line,nastatistic[nastatistic$year==year&nastatistic$month==mon,i+5],sep=",")
line<-paste(line,nastatistic[nastatistic$year==year&nastatistic$month==1,i+2],sep=",")
cat(file=nam1,line,fill=100,append=T)
}
# write.table(nastatistic,file=nam1,append=F,quote=F,sep=", ",row.names=F)
aa1[,"tmaxm"]<-aa1[,"tmaxm"]+nacor[,"mnatma>3"]
aa1[,"tminm"]<-aa1[,"tminm"]+nacor[,"mnatmi>3"]
aa2[,"tmaxm"]<-aa2[,"tmaxm"]+ynacor[,"ynatma>15"]
aa2[,"tminm"]<-aa2[,"tminm"]+ynacor[,"ynatmi>15"]
ofile1<-paste(outinddir,paste(ofilename,"_TMAXmean.csv",sep=""),sep="/")
odata<-matrix(0,ys,14)
dimnames(odata)<-list(NULL,c("year","jan","feb","mar","apr","may","jun","jul","aug","sep","oct","nov","dec","annual"))
odata[,1]<-years:yeare
odata[,14]<-aa2[,"tmaxm"]
for(i in 1:ys) odata[i,2:13]<-aa1[((i-1)*12+1):(i*12),"tmaxm"]
write.table(round(odata,2),file=ofile1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
odata[,14]<-aa2[,"tminm"]
for(i in 1:ys) odata[i,2:13]<-aa1[((i-1)*12+1):(i*12),"tminm"]
ofile1<-paste(outinddir,paste(ofilename,"_TMINmean.csv",sep=""),sep="/")
write.table(round(odata,2),file=ofile1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
parameter()
}
# -------------- nastat ends ------------------------------------------
# -----------------------------------------------------------------
# getfile
# first step is to open a data file, and read them into "dd".
#
getfile<-function() {
name <- tclvalue(tkgetOpenFile(filetypes="{{TEXT Files} {.txt}} {{All files} *}"))
if (name=="") return();
dd <- read.table(name,header=F,col.names=c("year","month","day","prcp","tmax","tmin"),colClasses=rep("real",6))
nama<-substr(name,start=1,stop=(nchar(name)-4))
outdirtmp<-strsplit(name,"/")[[1]]
if(length(outdirtmp)<=2){ # for Windiws
outinddir<-paste(strsplit(name,":")[[1]][1],"indices",sep=":/")
outlogdir<-paste(strsplit(name,":")[[1]][1],"log",sep=":/")
outjpgdir<-paste(strsplit(name,":")[[1]][1],"plots",sep=":/")
outtrddir<-paste(strsplit(name,":")[[1]][1],"trend",sep=":/")
}
else{ # Unix/Linux
outdir<-outdirtmp[1]
for(i in 2:(length(outdirtmp)-1))
outdir<-paste(outdir,outdirtmp[i],sep="/")
outinddir<-paste(outdir,"indices",sep="/")
outlogdir<-paste(outdir,"log",sep="/")
outjpgdir<-paste(outdir,"plots",sep="/")
outtrddir<-paste(outdir,"trend",sep="/")
}
ofilename<-substr(outdirtmp[length(outdirtmp)],start=1,stop=(nchar(outdirtmp[length(outdirtmp)])-4))
# create output folders
if(!file.exists(outinddir)) dir.create(outinddir)
if(!file.exists(outlogdir)) dir.create(outlogdir)
if(!file.exists(outjpgdir)) dir.create(outjpgdir)
if(!file.exists(outtrddir)) dir.create(outtrddir)
# dimnames(dd)<-list(NULL,c("year","month","day","prcp","tmax","tmin"))
assign("nama",nama,envir=.GlobalEnv)
assign("outinddir",outinddir,envir=.GlobalEnv)
assign("outlogdir",outlogdir,envir=.GlobalEnv)
assign("outjpgdir",outjpgdir,envir=.GlobalEnv)
assign("outtrddir",outtrddir,envir=.GlobalEnv)
assign("ofilename",ofilename,envir=.GlobalEnv)
# dd<-dd[dd$tmax!=-99.9,]
# dd$year<-dd$year+40 # just for the test data
# replace missing value with NA
dd[dd$prcp<=(-99.),"prcp"]<-NA
dd[dd$tmax<=(-99.),"tmax"]<-NA
dd[dd$tmin<=(-99.),"tmin"]<-NA
# replace missing records
ddd<-matrix(NA,365,6)
dddl<-matrix(NA,366,6)
dimnames(ddd)<-list(NULL,c("year","month","day","prcp","tmax","tmin"))
dimnames(dddl)<-list(NULL,c("year","month","day","prcp","tmax","tmin"))
ddd[1:31,"month"]<-1; ddd[1:31,"day"]<-c(1:31); ddd[32:59,"month"]<-2; ddd[32:59,"day"]<-c(1:28)
ddd[60:90,"month"]<-3; ddd[60:90,"day"]<-c(1:31); ddd[91:120,"month"]<-4; ddd[91:120,"day"]<-c(1:30)
ddd[121:151,"month"]<-5; ddd[121:151,"day"]<-c(1:31); ddd[152:181,"month"]<-6; ddd[152:181,"day"]<-c(1:30)
ddd[182:212,"month"]<-7; ddd[182:212,"day"]<-c(1:31); ddd[213:243,"month"]<-8; ddd[213:243,"day"]<-c(1:31)
ddd[244:273,"month"]<-9; ddd[244:273,"day"]<-c(1:30); ddd[274:304,"month"]<-10; ddd[274:304,"day"]<-c(1:31)
ddd[305:334,"month"]<-11; ddd[305:334,"day"]<-c(1:30); ddd[335:365,"month"]<-12; ddd[335:365,"day"]<-c(1:31)
dddl[1:31,"month"]<-1; dddl[1:31,"day"]<-c(1:31); dddl[32:60,"month"]<-2; dddl[32:60,"day"]<-c(1:29)
dddl[61:91,"month"]<-3; dddl[61:91,"day"]<-c(1:31); dddl[92:121,"month"]<-4; dddl[92:121,"day"]<-c(1:30)
dddl[122:152,"month"]<-5; dddl[122:152,"day"]<-c(1:31); dddl[153:182,"month"]<-6; dddl[153:182,"day"]<-c(1:30)
dddl[183:213,"month"]<-7; dddl[183:213,"day"]<-c(1:31); dddl[214:244,"month"]<-8; dddl[214:244,"day"]<-c(1:31)
dddl[245:274,"month"]<-9; dddl[245:274,"day"]<-c(1:30); dddl[275:305,"month"]<-10; dddl[275:305,"day"]<-c(1:31)
dddl[306:335,"month"]<-11; dddl[306:335,"day"]<-c(1:30); dddl[336:366,"month"]<-12; dddl[336:366,"day"]<-c(1:31)
years<-dd[1,1];yeare<-dd[dim(dd)[1],1] # star and end years
if (leapyear(years)) dddd<-dddl else dddd<-ddd
dddd[,"year"]<-years
for (year in years:yeare){ # year loop start
if (leapyear(year)) dddd1<-dddl else dddd1<-ddd
dddd1[,"year"]<-year
if (year!=years) dddd<-rbind(dddd,dddd1)
} # year loop end
dddd<-as.data.frame(dddd)
dddd2<-merge(dddd,dd,by=c("year","month","day"),all.x=T)
dddd2<-dddd2[,-(4:6)]
dimnames(dddd2)[[2]]<-c("year","month","day","prcp","tmax","tmin")
tmporder<-dddd2[,"year"]*10000+dddd2[,"month"]*100+dddd2[,"day"]
dd<-dddd2[order(tmporder),]
assign("years",years,envir=.GlobalEnv)
assign("yeare",yeare,envir=.GlobalEnv)
assign("dd",dd,envir=.GlobalEnv)
tkmessageBox(message=paste("Data(",ofilename,") loaded, click OK to continue.",sep=""))
# enter station name and the times of stadard deviation
infor1<-tktoplevel()
tkfocus(infor1)
tkgrab.set(infor1)
tkwm.title(infor1,"Set Parameters for Data QC")
textEntry1<-stations; textEntry2<-stdt
textEntryWidget1<-tkentry(infor1,width=30,textvariable=textEntry1)
textEntryWidget2<-tkentry(infor1,width=30,textvariable=textEntry2)
# tkgrid(tklabel(infor1,text="!!Enter parameters please",font=fontHeading1))
tkgrid(tklabel(infor1,text=" Station name or code:"),textEntryWidget1)
tkgrid(tklabel(infor1,text="Criteria(number of Standard Deviation):"),textEntryWidget2)
ok1<-function(){
station<-as.character(tclvalue(textEntry1)); assign("station",station,envir=.GlobalEnv)
crt<-as.numeric(tclvalue(textEntry2)); assign("crt",crt,envir=.GlobalEnv)
tkgrab.release(infor1)
tkdestroy(infor1)
stations<-textEntry1; assign("stations",stations,envir=.GlobalEnv)
stdt<-textEntry2; assign("stdt",stdt,envir=.GlobalEnv)
qcontrol(); tkfocus(start1)
}# end of ok
cancel1<-function(){
tkmessageBox(message="You have to enter these parameters before you can move on.")
tkfocus(infor1)}# end of cancel1
ok1.but<- tkbutton(infor1,text=" OK ",command=ok1)
cancel1.but<-tkbutton(infor1,text=" CANCEL ",command=cancel1)
tkgrid(ok1.but,cancel1.but)
}
# end of getfile
#------------------------------------------------------------
# -----------------------------------------------------------
# parameter
#
parameter<-function(){
infor<-tktoplevel()
tkfocus(infor)
tkgrab.set(infor)
tkwm.title(infor,"Set Parameter Values")
textEntry1<-Entry1
textEntry2<-Entry2
# textEntry3<-Entry3
textEntry4<-Entry4
textEntry5<-Entry5
textEntry6<-Entry6;textEntry7<-Entry7
textEntry8<-Entry8;textEntry9<-Entry9
# textEntry10<-Entry10;textEntry11<-Entry11
textEntry12<-Entry12
textEntryWidget1<-tkentry(infor,width=20,textvariable=textEntry1)
textEntryWidget2<-tkentry(infor,width=20,textvariable=textEntry2)
# textEntryWidget3<-tkentry(infor,width=20,textvariable=textEntry3)
textEntryWidget4<-tkentry(infor,width=20,textvariable=textEntry4)
textEntryWidget5<-tkentry(infor,width=20,textvariable=textEntry5)
textEntryWidget6<-tkentry(infor,width=20,textvariable=textEntry6)
textEntryWidget7<-tkentry(infor,width=20,textvariable=textEntry7)
textEntryWidget8<-tkentry(infor,width=20,textvariable=textEntry8)
textEntryWidget9<-tkentry(infor,width=20,textvariable=textEntry9)
# textEntryWidget10<-tkentry(infor,width=20,textvariable=textEntry10)
# textEntryWidget11<-tkentry(infor,width=20,textvariable=textEntry11)
textEntryWidget12<-tkentry(infor,width=20,textvariable=textEntry12)
tkgrid(tklabel(infor,text="User defined parameters for Indices Calculation",font=fontHeading1))
tkgrid(tklabel(infor,text="First year of base period"),textEntryWidget1)
tkgrid(tklabel(infor,text="Last year of base period"),textEntryWidget2)
tkgrid(tklabel(infor,text="Latitude of this station location"),textEntryWidget4)
tkgrid(tklabel(infor,text="Longitude of this station location"),textEntryWidget5)
tkgrid(tklabel(infor,text="User defined upper threshold of daily maximum temperature"),textEntryWidget6)
tkgrid(tklabel(infor,text="User defined lower threshold of daily maximum temperature"),textEntryWidget7)
tkgrid(tklabel(infor,text="User defined upper threshold of daily minimum temperature"),textEntryWidget8)
tkgrid(tklabel(infor,text="User defined lower threshold of daily minimum temperature"),textEntryWidget9)
tkgrid(tklabel(infor,text="User defined daily precipitation threshold"),textEntryWidget12)
#----------- OK1 -----------------------------------------
ok1<-function(){
# tkmessageBox(message="This process may take 2 mins to initialize the data. Please wait until the window disapear!")
startyear<-as.numeric(tclvalue(textEntry1)); assign("startyear",startyear,envir=.GlobalEnv)
endyear<-as.numeric(tclvalue(textEntry2)); assign("endyear",endyear,envir=.GlobalEnv)
if(startyear<years|endyear>yeare){
if(startyear<years) msg<-paste("Input base period start:", startyear," less then start year of data:", years, sep=" ")
else msg<-paste("Input base period end:", endyear," greater then end year of data:", yeare, sep=" ")
tkmessageBox(message=msg)
tkfocus(infor)
return()
}
# winsize<-as.numeric(tclvalue(textEntry3)); assign("winsize",winsize,envir=.GlobalEnv)
latitude<-as.numeric(tclvalue(textEntry4)); assign("latitude",latitude,envir=.GlobalEnv)
longitude<-as.numeric(tclvalue(textEntry5)); assign("longitude",longitude,envir=.GlobalEnv)
# threshold<-as.numeric(tclvalue(textEntry5)); assign("threshold",threshold,envir=.GlobalEnv)
uuu<-as.numeric(tclvalue(textEntry6)); assign("uuu",uuu,envir=.GlobalEnv)
ulu<-as.numeric(tclvalue(textEntry7)); assign("uul",ulu,envir=.GlobalEnv)
uul<-as.numeric(tclvalue(textEntry8)); assign("ulu",uul,envir=.GlobalEnv)
ull<-as.numeric(tclvalue(textEntry9)); assign("ull",ull,envir=.GlobalEnv)
# up<-as.numeric(tclvalue(textEntry10)); assign("up",up,envir=.GlobalEnv)
# lp<-as.numeric(tclvalue(textEntry11)); assign("lp",lp,envir=.GlobalEnv)
nn<-as.numeric(tclvalue(textEntry12)); assign("nn",nn,envir=.GlobalEnv)
startpoint<-startyear-1; assign("startpoint",startpoint,envir=.GlobalEnv)
endpoint<-endyear+1; assign("endpoint",endpoint,envir=.GlobalEnv)
nordaytem1()
tkgrab.release(infor)
tkdestroy(infor)
Entry1<-textEntry1; assign("Entry1",Entry1,envir=.GlobalEnv)
Entry2<-textEntry2; assign("Entry2",Entry2,envir=.GlobalEnv)
# Entry3<-textEntry3; assign("Entry3",Entry3,envir=.GlobalEnv)
Entry4<-textEntry4; assign("Entry4",Entry4,envir=.GlobalEnv)
Entry5<-textEntry5; assign("Entry5",Entry5,envir=.GlobalEnv)
Entry6<-textEntry6; assign("Entry6",Entry6,envir=.GlobalEnv)
Entry7<-textEntry7; assign("Entry7",Entry7,envir=.GlobalEnv)
Entry8<-textEntry8; assign("Entry8",Entry8,envir=.GlobalEnv)
Entry9<-textEntry9; assign("Entry9",Entry9,envir=.GlobalEnv)
# Entry10<-textEntry10; assign("Entry10",Entry10,envir=.GlobalEnv)
# Entry11<-textEntry11; assign("Entry11",Entry11,envir=.GlobalEnv)
Entry12<-textEntry12; assign("Entry12",Entry12,envir=.GlobalEnv)
main1()
}
#----------- OK1 ends -----------------------------------------
#----------- cancel1 -----------------------------------------
cancel1<-function(){
# tkmessageBox(message="Please enter these parameters before you can move forward!!")
# tkfocus(infor)
tkdestroy(infor)
# tkdestroy(main)
# tkfocus(start1)
return()
}
#----------- cancel1 ends -----------------------------------------
ok1.but<- tkbutton(infor,text=" OK ",command=ok1)
cancel1.but<-tkbutton(infor,text=" CANCEL ",command=cancel1)
tkgrid(ok1.but,cancel1.but)
}
#----------- parameter ends -----------------------------------------
# End of Part I (general functions & TCL/TK functions
##################################################################################
##################################################################################
# Part II
# Functions of calculating climate indecies
##################################################################################
#----------- hwfi -----------------------------------------
hwfi<-function(){
if (flag==T) return()
hwfi<-matrix(0,(yeare-years+1),2)
dimnames(hwfi)[[2]]<-c("year","wsdi")
hwfi[,"year"]<-years:yeare
for (year in years:yeare) {
if(leapyear(year)){
aa<-rep(0,366)
aa[1:59]<-aas[,"pcmax90"][1:59]
aa[60]<-aa[59]
aa[61:366]<-aas[,"pcmax90"][60:365]
}
else aa<-aas[,"pcmax90"]
bb<-dd[dd$year==year,"tmax"]
if(length(aa)!=length(bb)) stop("ERROR in WSDI, check data!")
midval<-bb-aa
ylen<-length(aa)
ycnt<-0
icnt<-0
for(i in 1:ylen){
if(is.na(midval[i])==F&midval[i]>0)
icnt<-icnt+1
else{
if(icnt>=6) ycnt<-ycnt+icnt
icnt<-0
}
if(i==ylen&icnt>=6) ycnt<-ycnt+icnt
}
hwfi[year-years+1,2]<-ycnt
}
hwfi<-as.data.frame(hwfi)
hwfi[,"wsdi"]<-hwfi[,"wsdi"]+ynacor[,"ynatma>15"]
nam1<-paste(outinddir,paste(ofilename,"_WSDI.csv",sep=""),sep="/")
write.table(hwfi,file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
if(sum(is.na(hwfi[,"wsdi"]))>=(yeare-years+1-10)){
betahat<-NA
betastd<-NA
pvalue<-NA
}
else{
fit1<-lsfit(hwfi[,"year"],hwfi[,"wsdi"])
out1<-ls.print(fit1,print.it=F)
pvalue<-round(as.numeric(out1$summary[1,6]),3)
betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
}
cat(file=namt,paste(latitude,longitude,"wsdi",years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
nam2<-paste(outjpgdir,paste(ofilename,"_WSDI.jpg",sep=""),sep="/")
jpeg(nam2,width=1024,height=768)
plotx(hwfi[,1],hwfi[,2], main=paste("WSDI",ofilename,sep=" "),ylab="WSDI",xlab="Year")
dev.off()
}
# ------------ hwfi ends ------------------------------------
# ------------- cwdi ----------------------------------------
cwdi<-function(){
if (flag==T) return()
cwdi<-matrix(0,(yeare-years+1),2)
dimnames(cwdi)[[2]]<-c("year","csdi")
cwdi[,"year"]<-years:yeare
for (year in years:yeare) {
if(leapyear(year)){
aa<-rep(0,366)
aa[1:59]<-aas[,"pcmin10"][1:59]
aa[60]<-aa[59]
aa[61:366]<-aas[,"pcmin10"][60:365]
}
else aa<-aas[,"pcmin10"]
bb<-dd[dd$year==year,"tmin"]
if(length(aa)!=length(bb)) stop("ERROR in CWDI, check data!")
midval<-aa-bb
ylen<-length(aa)
ycnt<-0
icnt<-0
for(i in 1:ylen){
if(is.na(midval[i])==F&midval[i]>0)
icnt<-icnt+1
else{
if(icnt>=6) ycnt<-ycnt+icnt
icnt<-0
}
if(i==ylen&icnt>=6) ycnt<-ycnt+icnt
}
cwdi[year-years+1,2]<-ycnt
}
cwdi<-as.data.frame(cwdi)
cwdi[,"csdi"]<-cwdi[,"csdi"]+ynacor[,"ynatmi>15"]
nam1<-paste(outinddir,paste(ofilename,"_CSDI.csv",sep=""),sep="/")
write.table(cwdi,file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
if(sum(is.na(cwdi[,"csdi"]))>=(yeare-years+1-10)){
betahat<-NA
betastd<-NA
pvalue<-NA
}
else{
fit1<-lsfit(cwdi[,"year"],cwdi[,"csdi"])
out1<-ls.print(fit1,print.it=F)
pvalue<-round(as.numeric(out1$summary[1,6]),3)
betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
}
cat(file=namt,paste(latitude,longitude,"csdi",years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
nam2<-paste(outjpgdir,paste(ofilename,"_CSDI.jpg",sep=""),sep="/")
jpeg(nam2,width=1024,height=768)
plotx(cwdi[,1],cwdi[,2],main=paste("CSDI",ofilename,sep=" "),ylab="CSDI",xlab="Year")
dev.off()
}
# ----------------- cwdi ends -------------------------------------------
#----------- r95ptot -----------------------------------------
r95ptot<-function(){
prcptmp<-dd[dd$year>=startyear&dd$year<=endyear&dd$prcp>=1,"prcp"]
prcptmp<-prcptmp[is.na(prcptmp)==F]
len<-length(prcptmp)
prcp95<-percentile(len,prcptmp,0.95)
prcp99<-percentile(len,prcptmp,0.99)
ys<-yeare-years+1
dp<-matrix(0,ys,4)
dimnames(dp)<-list(NULL,c("year","r95p","r99p","prcptot"))
dp[,"year"]<-years:yeare
for(i in years:yeare){
dp[(i-years+1),"r95p"]<-sum(dd[dd$year==i&dd$prcp>prcp95,"prcp"],na.rm=T)
dp[(i-years+1),"r99p"]<-sum(dd[dd$year==i&dd$prcp>prcp99,"prcp"],na.rm=T)
dp[(i-years+1),"prcptot"]<-sum(dd[dd$year==i&dd$prcp>=1,"prcp"],na.rm=T)
}
dp[,"r95p"]<-round(dp[,"r95p"],1)+ynacor[,"ynapr>15"]
dp[,"r99p"]<-round(dp[,"r99p"],1)+ynacor[,"ynapr>15"]
dp[,"prcptot"]<-round(dp[,"prcptot"],1)+ynacor[,"ynapr>15"]
dp<-as.data.frame(dp)
nam1<-paste(outinddir,paste(ofilename,"_R95p.csv",sep=""),sep="/")
nam2<-paste(outinddir,paste(ofilename,"_R99p.csv",sep=""),sep="/")
nam3<-paste(outinddir,paste(ofilename,"_PRCPTOT.csv",sep=""),sep="/")
write.table(dp[,c("year","r95p")],file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
write.table(dp[,c("year","r99p")],file=nam2,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
write.table(dp[,c("year","prcptot")],file=nam3,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
for(i in c("r95p","r99p","prcptot")){
if(sum(is.na(dp[,i]))>=(yeare-years+1-10)){
betahat<-NA
betastd<-NA
pvalue<-NA
}
else{
fit1<-lsfit(dp[,"year"],dp[,i])
out1<-ls.print(fit1,print.it=F)
pvalue<-round(as.numeric(out1$summary[1,6]),3)
betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
}
cat(file=namt,paste(latitude,longitude,i,years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
}
nam4<-paste(outjpgdir,paste(ofilename,"_R95p.jpg",sep=""),sep="/")
jpeg(nam4,width=1024,height=768)
plotx(dp[,1],dp[,"r95p"],main=paste("R95p",ofilename,sep=" "),xlab="Year",ylab="R95p")
dev.off()
nam5<-paste(outjpgdir,paste(ofilename,"_R99p.jpg",sep=""),sep="/")
jpeg(nam5,width=1024,height=768)
plotx(dp[,1],dp[,"r99p"],main=paste("R99p",ofilename,sep=" "),xlab="Year",ylab="R99p")
dev.off()
nam6<-paste(outjpgdir,paste(ofilename,"_PRCPTOT.jpg",sep=""),sep="/")
jpeg(nam6,width=1024,height=768)
plotx(dp[,1],dp[,"prcptot"],main=paste("PRCPTOT",ofilename,sep=" "),xlab="Year",ylab="PRCPTOT")
dev.off()
}
#----------- r95ptot ends -----------------------------------------
#----------- daysprcp20 -----------------------------------------
daysprcp20<-function(){
ys<-yeare-years+1
R20<-rep(0,ys)
yearss<-c(years:yeare)
target<-as.data.frame(cbind(yearss,R20))
for (year in years:yeare){
mid<-dd[dd$year==year,"prcp"]
mid<-mid[is.na(mid)==F]
target[target$yearss==year,"R20"]<-length(mid[mid>=20])}# end for
dimnames(target)[[2]][1]<-"year"
target[,"R20"]<-target[,"R20"]+ynacor[,"ynapr>15"]
nam1<-paste(outinddir,paste(ofilename,"_R20mm.csv",sep=""),sep="/")
write.table(target,file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
if(sum(is.na(target[,"R20"]))>=(yeare-years+1-10)){
betahat<-NA
betastd<-NA
pvalue<-NA
}
else{
fit1<-lsfit(target[,"year"],target[,"R20"])
out1<-ls.print(fit1,print.it=F)
pvalue<-round(as.numeric(out1$summary[1,6]),3)
betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
}
cat(file=namt,paste(latitude,longitude,"r20mm",years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
nam2<-paste(outjpgdir,paste(ofilename,"_R20mm.jpg",sep=""),sep="/")
jpeg(nam2,width=1024,height=768)
plotx(target[,1],target[,2],main=paste("R20mm",ofilename,sep=" "),xlab="Year",ylab="R20mm")
dev.off()
}
#----------- daysprcp20 ends -----------------------------------------
#----------- daysprcpn -----------------------------------------
daysprcpn<-function(){
ys<-yeare-years+1
Rnn<-rep(0,ys)
yearss<-c(years:yeare)
target<-as.data.frame(cbind(yearss,Rnn))
for (year in years:yeare){
mid<-dd[dd$year==year,"prcp"]
mid<-mid[is.na(mid)==F]
target[target$yearss==year,"Rnn"]<-length(mid[mid>=nn])
}
dimnames(target)[[2]][1]<-"year"
target[,"Rnn"]<-target[,"Rnn"]+ynacor[,"ynapr>15"]
nam1<-paste(outinddir,paste(ofilename,"_R",as.character(nn),"mm.csv",sep=""),sep="/")
write.table(target,file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
if(sum(is.na(target[,"Rnn"]))>=(yeare-years+1-10)){
betahat<-NA
betastd<-NA
pvalue<-NA
}
else{
fit1<-lsfit(target[,1],target[,"Rnn"])
out1<-ls.print(fit1,print.it=F)
pvalue<-round(as.numeric(out1$summary[1,6]),3)
betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
}
cat(file=namt,paste(latitude,longitude,paste("R",as.character(nn),"mm",sep=""),years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
nam2<-paste(outjpgdir,paste(ofilename,"_R",as.character(nn),"mm.jpg",sep=""),sep="/")
jpeg(nam2,width=1024,height=768)
plotx(target[,1],target[,2],main=paste("R",as.character(nn),"mm",ofilename,sep=" "),xlab="Year",ylab="Rnnmm")
dev.off()
}
#----------- daysprcpn ends -----------------------------------------
#----------- nordaytem1 -----------------------------------------
nordaytem1<-function(){ # initialize data
# normal temp
daynorm<-dd[dd$year>=startyear,]
daynorm<-daynorm[daynorm$year<=endyear,] # initialize daynorm matrix
daynor<-daynorm # create target matrix
nn<-dd[dd$year==startpoint,]
nn<-nn[nn$month==12,]
nn<-nn[nn$day>(31-round(winsize/2)),]
daynorm<-rbind(nn,daynorm)
nn<-dd[dd$year==endpoint,]
nn<-nn[nn$month==1,]
nn<-nn[nn$day<=round(winsize/2),]
daynorm<-rbind(daynorm,nn)
daynorm1<-daynorm[,-4]
daynorm1[daynorm1$month==2 & daynorm1$day==29,]<--99
daynorm1<-daynorm1[daynorm1$year!=-99,]
dayt<-daynorm1[,c("tmax","tmin")]
ddtem<-dd[,-4]
ddtem[ddtem$month==2 & ddtem$day==29,]<--99
ddtem<-ddtem[ddtem$year!=-99,]
assign("ddtem",ddtem,envir=.GlobalEnv)
a<-matrix(-99,5,5)
dimnames(a)[[2]]<-c("year","month","day","tmax","tmin")
ddtemt<-rbind(a,ddtem); assign("ddtemt",ddtemt,envir=.GlobalEnv)
ys<-endyear-startyear+1
window<-matrix(0,winsize,2)
windows<-array(window,c(winsize,2,366,ys))
dimnames(windows)<-list(NULL,c("tmax","tmin"),NULL,NULL)
i=winsize-round(winsize/2,digits=0)
i1=round(winsize/2,digits=0)
daynormm<-daynorm[,c("tmax","tmin")]
daynormm<-as.matrix(daynormm)
year<-startyear
for (k in 1:ys){
if (leapyear(year)==T) jj<-366 else {jj<-365; windows[,,366,k]<--99 }
year<-year+1
for (j in 1:jj){
windows[,,j,k]<-daynormm[(i-i1):(i+i1),]
i=i+1 }}
mwindows<-colMeans(windows,na.rm=T)
tmax<-mwindows["tmax",,]; tmax<-tmax[tmax!=-99]
tmin<-mwindows["tmin",,]; tmin<-tmin[tmin!=-99]
daynor[,"tmax"]<-tmax; daynor[,"tmin"]<-tmin
a<-rep(0,nrow(daynor))
a<-(daynor[,"tmax"]+daynor[,"tmin"])/2
daytemave<-a
daynor<-cbind(daynor,daytemave)
# output the result to globe enviroment
assign("daynor",daynor,envir=.GlobalEnv)
assign("daynorm",daynorm,envir=.GlobalEnv)
assign("daynorm1",daynorm1,envir=.GlobalEnv)
assign("dayt",dayt,envir=.GlobalEnv)
# prcp percentile 95% and 99%
prcpnorm<-dd[dd$year>=startyear,]
prcpnorm<-prcpnorm[prcpnorm$year<=endyear,] # initialize prcpnorm matrix
nnp<-dd[dd$year==startpoint,]
nnp<-nnp[nnp$month==12,]
nnp<-nnp[nnp$day>29,]
prcpnorm<-rbind(nnp,prcpnorm)
nnp<-dd[dd$year==endpoint,]
nnp<-nnp[nnp$month==1,]
nnp<-nnp[nnp$day<=2,]
prcpnorm<-rbind(prcpnorm,nnp)
prcpnorm<-prcpnorm[,1:4]
# remove Feb 29
prcpnorm[prcpnorm$month==2 & prcpnorm$day==29,]<--99
prcpnorm<-prcpnorm[prcpnorm$year!=-99,]
assign("prcpnorm",prcpnorm,envir=.GlobalEnv)
ys<-endyear-startyear+1
aasp<-matrix(NA,365,3)
dimnames(aasp)<-list(NULL,c("day","prcp95","prcp99"))
aasp[,"day"]<-1:365
msp<-5*ys
prcpnorm<-as.matrix(prcpnorm)
pwindow<-matrix(0,5,1)
pwindows<-array(pwindow,c(5,1,365,ys)) #array used to store all windows
ip=3
ip1=2
for (k in 1:ys){
for (j in 1:365){
pwindows[,,j,k]<-prcpnorm[(ip-ip1):(ip+ip1),"prcp"]
ip=ip+1}}
prcpwin<-matrix(0,msp,2)
prcpwin[,2]<-1:ys
prcpwin[,2]<-mysort(prcpwin[,2],decreasing=F)
prcpwins<-array(prcpwin,c(msp,2,365))
for (j in 1:365){
for (i in 1:ys){
prcpwins[prcpwins[,2,j]==i,1,j]<-pwindows[,,j,i]}}
#assign("exwins",exwins,envir=.GlobalEnv)
for (i in 1:365){
assp<-prcpwins[,,i]
if(sum(is.na(assp[,1])==F)>=1)
aasp[i,"prcp95"]<-percentile(msp,assp[,1],0.95)
else aasp[i,"prcp95"]<-NA
if(sum(is.na(assp[,1])==F)>=1)
aasp[i,"prcp99"]<-percentile(msp,assp[,1],0.99)
else aasp[i,"prcp99"]<-NA
}
assign("aasp",aasp,envir=.GlobalEnv)
aas<-matrix(NA,365,5)
dimnames(aas)<-list(NULL,c("day","pcmax10","pcmax90","pcmin10","pcmin90"))
aas[,"day"]<-1:365
ms<-winsize*ys
dayt<-as.matrix(dayt)
window<-matrix(0,winsize,2)
windows<-array(window,c(winsize,2,365,ys)) #array used to store all windows
i=winsize-round(winsize/2,digits=0)
i1=round(winsize/2,digits=0)
for (k in 1:ys){
for (j in 1:365){
windows[,,j,k]<-dayt[(i-i1):(i+i1),]
i=i+1}}
exwin<-matrix(0,ms,3)
exwin[,3]<-1:ys
exwin[,3]<-mysort(exwin[,3],decreasing=F)
assign("exwin",exwin,envir=.GlobalEnv)
#indd<-exwin[exwin[,3]!=ys,3]
exwins<-array(exwin,c(ms,3,365)) # array for bootstrap
for (j in 1:365){
for (i in 1:ys){
exwins[exwins[,3,j]==i,1:2,j]<-windows[,,j,i]}}
assign("exwins",exwins,envir=.GlobalEnv)
for ( i in 1:365){
ass<-exwins[,,i]
ass1<-ass[,1]
ass2<-ass[,2]
kgb1<-length(ass1[is.na(ass1)])
kgb2<-length(ass2[is.na(ass2)])
if (kgb1>37.5 | kgb2>37.5) {flag=T;break}}#150*0.25=37.5
assign("flag",flag,envir=.GlobalEnv)
if (flag==T) tkmessageBox(message="More than 25% data missing, Exceedance rate, HWDI,CWDI will not be calculated!!")
if (flag==T) return()
for (i in 1:365){
ass<-exwins[,,i]
# if(i == 363) {
# ttmp<-ms
# assign("ttmp",ttmp,envir=.GlobalEnv)
# }
itmp<-percentile(ms,ass[,1],c(0.1,0.9))
aas[i,"pcmax10"]<-itmp[1]-1e-5
aas[i,"pcmax90"]<-itmp[2]+1e-5
itmp<-percentile(ms,ass[,2],c(0.1,0.9))
aas[i,"pcmin10"]<-itmp[1]-1e-5
aas[i,"pcmin90"]<-itmp[2]+1e-5 }
assign("aas",aas,envir=.GlobalEnv)# matrix to store 10 and 90 percentile
# exceedance rate before 1961 and after 2000
before<-dd[dd$year<startyear,]
after<-dd[dd$year>endyear,]
# dataframe store the before monthly exceedance rate
ys1<-startyear-years;ys2<-yeare-endyear
bmonex<-matrix(NA,ys1*12,6)
dimnames(bmonex)<-list(NULL,c("year","month","tx10p","tx90p","tn10p","tn90p"))
bmonex[,"month"]<-rep(1:12,ys1)
bmonex[,"year"]<-years:(startyear-1)
bmonex[,"year"]<-mysort(bmonex[,"year"],decreasing=F)
bmonex<-as.data.frame(bmonex)
# dataframe store the after monthly exceedance rate
amonex<-matrix(NA,ys2*12,6)
dimnames(amonex)<-list(NULL,c("year","month","tx10p","tx90p","tn10p","tn90p"))
amonex[,"month"]<-rep(1:12,ys2)
amonex[,"year"]<-(endyear+1):yeare
amonex[,"year"]<-mysort(amonex[,"year"],decreasing=F)
amonex<-as.data.frame(amonex)
# dataframe store yearly exceedance rate (before and after)
yearex<-c(years:(startyear-1)); txg10p<-rep(0,length(yearex))
txg90p<-rep(0,length(yearex)); tng10p<-rep(0,length(yearex))
tng90p<-rep(0,length(yearex))
bd<-as.data.frame(cbind(yearex,txg10p,txg90p,tng10p,tng90p))
colnames(bd)[1]<-"year"
yearex<-c((endyear+1):yeare); txg10p<-rep(0,length(yearex))
txg90p<-rep(0,length(yearex)); tng10p<-rep(0,length(yearex))
tng90p<-rep(0,length(yearex))
ad<-as.data.frame(cbind(yearex,txg10p,txg90p,tng10p,tng90p))
colnames(ad)[1]<-"year"
year=years;jjj6=1
for (i in 1:ys1){
midvalue<-ddtem[ddtem$year==year,]
exmax10<-midvalue[,4]-aas[,2]
exmax10m1<-exmax10[1:31]; exmax10m2<-exmax10[32:59]; exmax10m3<-exmax10[60:90]
exmax10m4<-exmax10[91:120]; exmax10m5<-exmax10[121:151]; exmax10m6<-exmax10[152:181]
exmax10m7<-exmax10[182:212]; exmax10m8<-exmax10[213:243]; exmax10m9<-exmax10[244:273]
exmax10m10<-exmax10[274:304]; exmax10m11<-exmax10[305:334]; exmax10m12<-exmax10[335:365]
exmax90<-midvalue[,4]-aas[,3]
exmax90m1<-exmax90[1:31]; exmax90m2<-exmax90[32:59]; exmax90m3<-exmax90[60:90]
exmax90m4<-exmax90[91:120]; exmax90m5<-exmax90[121:151]; exmax90m6<-exmax90[152:181]
exmax90m7<-exmax90[182:212]; exmax90m8<-exmax90[213:243]; exmax90m9<-exmax90[244:273]
exmax90m10<-exmax90[274:304]; exmax90m11<-exmax90[305:334]; exmax90m12<-exmax90[335:365]
exmin10<-midvalue[,5]-aas[,4]
exmin10m1<-exmin10[1:31]; exmin10m2<-exmin10[32:59]; exmin10m3<-exmin10[60:90]
exmin10m4<-exmin10[91:120]; exmin10m5<-exmin10[121:151]; exmin10m6<-exmin10[152:181]
exmin10m7<-exmin10[182:212]; exmin10m8<-exmin10[213:243]; exmin10m9<-exmin10[244:273]
exmin10m10<-exmin10[274:304]; exmin10m11<-exmin10[305:334]; exmin10m12<-exmin10[335:365]
exmin90<-midvalue[,5]-aas[,5]
exmin90m1<-exmin90[1:31]; exmin90m2<-exmin90[32:59]; exmin90m3<-exmin90[60:90]
exmin90m4<-exmin90[91:120]; exmin90m5<-exmin90[121:151]; exmin90m6<-exmin90[152:181]
exmin90m7<-exmin90[182:212]; exmin90m8<-exmin90[213:243]; exmin90m9<-exmin90[244:273]
exmin90m10<-exmin90[274:304]; exmin90m11<-exmin90[305:334]; exmin90m12<-exmin90[335:365]
bd[i,"txg10p"]<-length(exmax10[exmax10<0&is.na(exmax10)==F])
bd[i,"txg90p"]<-length(exmax90[exmax90>0&is.na(exmax90)==F])
bd[i,"tng10p"]<-length(exmin10[exmin10<0&is.na(exmin10)==F])
bd[i,"tng90p"]<-length(exmin90[exmin90>0&is.na(exmin90)==F])
bmonex[jjj6,"tx10p"]<-length(exmax10m1[exmax10m1<0&is.na(exmax10m1)==F])
bmonex[jjj6,"tx90p"]<-length(exmax90m1[exmax90m1>0&is.na(exmax90m1)==F])
bmonex[jjj6,"tn10p"]<-length(exmin10m1[exmin10m1<0&is.na(exmin10m1)==F])
bmonex[jjj6,"tn90p"]<-length(exmin90m1[exmin90m1>0&is.na(exmin90m1)==F])
bmonex[jjj6+1,"tx10p"]<-length(exmax10m2[exmax10m2<0&is.na(exmax10m2)==F])
bmonex[jjj6+1,"tx90p"]<-length(exmax90m2[exmax90m2>0&is.na(exmax90m2)==F])
bmonex[jjj6+1,"tn10p"]<-length(exmin10m2[exmin10m2<0&is.na(exmin10m2)==F])
bmonex[jjj6+1,"tn90p"]<-length(exmin90m2[exmin90m2>0&is.na(exmin90m2)==F])
bmonex[jjj6+2,"tx10p"]<-length(exmax10m3[exmax10m3<0&is.na(exmax10m3)==F])
bmonex[jjj6+2,"tx90p"]<-length(exmax90m3[exmax90m3>0&is.na(exmax90m3)==F])
bmonex[jjj6+2,"tn10p"]<-length(exmin10m3[exmin10m3<0&is.na(exmin10m3)==F])
bmonex[jjj6+2,"tn90p"]<-length(exmin90m3[exmin90m3>0&is.na(exmin90m3)==F])
bmonex[jjj6+3,"tx10p"]<-length(exmax10m4[exmax10m4<0&is.na(exmax10m4)==F])
bmonex[jjj6+3,"tx90p"]<-length(exmax90m4[exmax90m4>0&is.na(exmax90m4)==F])
bmonex[jjj6+3,"tn10p"]<-length(exmin10m4[exmin10m4<0&is.na(exmin10m4)==F])
bmonex[jjj6+3,"tn90p"]<-length(exmin90m4[exmin90m4>0&is.na(exmin90m4)==F])
bmonex[jjj6+4,"tx10p"]<-length(exmax10m5[exmax10m5<0&is.na(exmax10m5)==F])
bmonex[jjj6+4,"tx90p"]<-length(exmax90m5[exmax90m5>0&is.na(exmax90m5)==F])
bmonex[jjj6+4,"tn10p"]<-length(exmin10m5[exmin10m5<0&is.na(exmin10m5)==F])
bmonex[jjj6+4,"tn90p"]<-length(exmin90m5[exmin90m5>0&is.na(exmin90m5)==F])
bmonex[jjj6+5,"tx10p"]<-length(exmax10m6[exmax10m6<0&is.na(exmax10m6)==F])
bmonex[jjj6+5,"tx90p"]<-length(exmax90m6[exmax90m6>0&is.na(exmax90m6)==F])
bmonex[jjj6+5,"tn10p"]<-length(exmin10m6[exmin10m6<0&is.na(exmin10m6)==F])
bmonex[jjj6+5,"tn90p"]<-length(exmin90m6[exmin90m6>0&is.na(exmin90m6)==F])
bmonex[jjj6+6,"tx10p"]<-length(exmax10m7[exmax10m7<0&is.na(exmax10m7)==F])
bmonex[jjj6+6,"tx90p"]<-length(exmax90m7[exmax90m7>0&is.na(exmax90m7)==F])
bmonex[jjj6+6,"tn10p"]<-length(exmin10m7[exmin10m7<0&is.na(exmin10m7)==F])
bmonex[jjj6+6,"tn90p"]<-length(exmin90m7[exmin90m7>0&is.na(exmin90m7)==F])
bmonex[jjj6+7,"tx10p"]<-length(exmax10m8[exmax10m8<0&is.na(exmax10m8)==F])
bmonex[jjj6+7,"tx90p"]<-length(exmax90m8[exmax90m8>0&is.na(exmax90m8)==F])
bmonex[jjj6+7,"tn10p"]<-length(exmin10m8[exmin10m8<0&is.na(exmin10m8)==F])
bmonex[jjj6+7,"tn90p"]<-length(exmin90m8[exmin90m8>0&is.na(exmin90m8)==F])
bmonex[jjj6+8,"tx10p"]<-length(exmax10m9[exmax10m9<0&is.na(exmax10m9)==F])
bmonex[jjj6+8,"tx90p"]<-length(exmax90m9[exmax90m9>0&is.na(exmax90m9)==F])
bmonex[jjj6+8,"tn10p"]<-length(exmin10m9[exmin10m9<0&is.na(exmin10m9)==F])
bmonex[jjj6+8,"tn90p"]<-length(exmin90m9[exmin90m9>0&is.na(exmin90m9)==F])
bmonex[jjj6+9,"tx10p"]<-length(exmax10m10[exmax10m10<0&is.na(exmax10m10)==F])
bmonex[jjj6+9,"tx90p"]<-length(exmax90m10[exmax90m10>0&is.na(exmax90m10)==F])
bmonex[jjj6+9,"tn10p"]<-length(exmin10m10[exmin10m10<0&is.na(exmin10m10)==F])
bmonex[jjj6+9,"tn90p"]<-length(exmin90m10[exmin90m10>0&is.na(exmin90m10)==F])
bmonex[jjj6+10,"tx10p"]<-length(exmax10m11[exmax10m11<0&is.na(exmax10m11)==F])
bmonex[jjj6+10,"tx90p"]<-length(exmax90m11[exmax90m11>0&is.na(exmax90m11)==F])
bmonex[jjj6+10,"tn10p"]<-length(exmin10m11[exmin10m11<0&is.na(exmin10m11)==F])
bmonex[jjj6+10,"tn90p"]<-length(exmin90m11[exmin90m11>0&is.na(exmin90m11)==F])
bmonex[jjj6+11,"tx10p"]<-length(exmax10m12[exmax10m12<0&is.na(exmax10m12)==F])
bmonex[jjj6+11,"tx90p"]<-length(exmax90m12[exmax90m12>0&is.na(exmax90m12)==F])
bmonex[jjj6+11,"tn10p"]<-length(exmin10m12[exmin10m12<0&is.na(exmin10m12)==F])
bmonex[jjj6+11,"tn90p"]<-length(exmin90m12[exmin90m12>0&is.na(exmin90m12)==F])
if(leapyear(year)){
if(dd[dd$year==year&dd$month==2&dd$day==29,"tmax"]>aas[59,"pcmax90"]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,"tmax"])==F&is.na(aas[59,"pcmax90"])==F)
bmonex[jjj6+1,"tx90p"]<-bmonex[jjj6+1,"tx90p"]+1
if(dd[dd$year==year&dd$month==2&dd$day==29,"tmax"]<aas[59,"pcmax10"]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,"tmax"])==F&is.na(aas[59,"pcmax10"])==F)
bmonex[jjj6+1,"tx10p"]<-bmonex[jjj6+1,"tx10p"]+1
if(dd[dd$year==year&dd$month==2&dd$day==29,"tmin"]>aas[59,"pcmin90"]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,"tmin"])==F&is.na(aas[59,"pcmin90"])==F)
bmonex[jjj6+1,"tn90p"]<-bmonex[jjj6+1,"tn90p"]+1
if(dd[dd$year==year&dd$month==2&dd$day==29,"tmin"]<aas[59,"pcmin10"]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,"tmin"])==F&is.na(aas[59,"pcmin10"])==F)
bmonex[jjj6+1,"tn10p"]<-bmonex[jjj6+1,"tn10p"]+1
}
jjj6<-jjj6+12
year=year+1
}
year=endyear+1;jjj6=1
for (i in 1:ys2){
midvalue<-ddtem[ddtem$year==year,]
exmax10<-midvalue[,4]-aas[,2]
exmax10m1<-exmax10[1:31]; exmax10m2<-exmax10[32:59]; exmax10m3<-exmax10[60:90]
exmax10m4<-exmax10[91:120]; exmax10m5<-exmax10[121:151]; exmax10m6<-exmax10[152:181]
exmax10m7<-exmax10[182:212]; exmax10m8<-exmax10[213:243]; exmax10m9<-exmax10[244:273]
exmax10m10<-exmax10[274:304]; exmax10m11<-exmax10[305:334]; exmax10m12<-exmax10[335:365]
exmax90<-midvalue[,4]-aas[,3]
exmax90m1<-exmax90[1:31]; exmax90m2<-exmax90[32:59]; exmax90m3<-exmax90[60:90]
exmax90m4<-exmax90[91:120]; exmax90m5<-exmax90[121:151]; exmax90m6<-exmax90[152:181]
exmax90m7<-exmax90[182:212]; exmax90m8<-exmax90[213:243]; exmax90m9<-exmax90[244:273]
exmax90m10<-exmax90[274:304]; exmax90m11<-exmax90[305:334]; exmax90m12<-exmax90[335:365]
exmin10<-midvalue[,5]-aas[,4]
exmin10m1<-exmin10[1:31]; exmin10m2<-exmin10[32:59]; exmin10m3<-exmin10[60:90]
exmin10m4<-exmin10[91:120]; exmin10m5<-exmin10[121:151]; exmin10m6<-exmin10[152:181]
exmin10m7<-exmin10[182:212]; exmin10m8<-exmin10[213:243]; exmin10m9<-exmin10[244:273]
exmin10m10<-exmin10[274:304]; exmin10m11<-exmin10[305:334]; exmin10m12<-exmin10[335:365]
exmin90<-midvalue[,5]-aas[,5]
exmin90m1<-exmin90[1:31]; exmin90m2<-exmin90[32:59]; exmin90m3<-exmin90[60:90]
exmin90m4<-exmin90[91:120]; exmin90m5<-exmin90[121:151]; exmin90m6<-exmin90[152:181]
exmin90m7<-exmin90[182:212]; exmin90m8<-exmin90[213:243]; exmin90m9<-exmin90[244:273]
exmin90m10<-exmin90[274:304]; exmin90m11<-exmin90[305:334]; exmin90m12<-exmin90[335:365]
ad[i,"txg10p"]<-length(exmax10[exmax10<0&is.na(exmax10)==F])
ad[i,"txg90p"]<-length(exmax90[exmax90>0&is.na(exmax90)==F])
ad[i,"tng10p"]<-length(exmin10[exmin10<0&is.na(exmin10)==F])
ad[i,"tng90p"]<-length(exmin90[exmin90>0&is.na(exmin90)==F])
amonex[jjj6,"tx10p"]<-length(exmax10m1[exmax10m1<0&is.na(exmax10m1)==F])
amonex[jjj6,"tx90p"]<-length(exmax90m1[exmax90m1>0&is.na(exmax90m1)==F])
amonex[jjj6,"tn10p"]<-length(exmin10m1[exmin10m1<0&is.na(exmin10m1)==F])
amonex[jjj6,"tn90p"]<-length(exmin90m1[exmin90m1>0&is.na(exmin90m1)==F])
amonex[jjj6+1,"tx10p"]<-length(exmax10m2[exmax10m2<0&is.na(exmax10m2)==F])
amonex[jjj6+1,"tx90p"]<-length(exmax90m2[exmax90m2>0&is.na(exmax90m2)==F])
amonex[jjj6+1,"tn10p"]<-length(exmin10m2[exmin10m2<0&is.na(exmin10m2)==F])
amonex[jjj6+1,"tn90p"]<-length(exmin90m2[exmin90m2>0&is.na(exmin90m2)==F])
amonex[jjj6+2,"tx10p"]<-length(exmax10m3[exmax10m3<0&is.na(exmax10m3)==F])
amonex[jjj6+2,"tx90p"]<-length(exmax90m3[exmax90m3>0&is.na(exmax90m3)==F])
amonex[jjj6+2,"tn10p"]<-length(exmin10m3[exmin10m3<0&is.na(exmin10m3)==F])
amonex[jjj6+2,"tn90p"]<-length(exmin90m3[exmin90m3>0&is.na(exmin90m3)==F])
amonex[jjj6+3,"tx10p"]<-length(exmax10m4[exmax10m4<0&is.na(exmax10m4)==F])
amonex[jjj6+3,"tx90p"]<-length(exmax90m4[exmax90m4>0&is.na(exmax90m4)==F])
amonex[jjj6+3,"tn10p"]<-length(exmin10m4[exmin10m4<0&is.na(exmin10m4)==F])
amonex[jjj6+3,"tn90p"]<-length(exmin90m4[exmin90m4>0&is.na(exmin90m4)==F])
amonex[jjj6+4,"tx10p"]<-length(exmax10m5[exmax10m5<0&is.na(exmax10m5)==F])
amonex[jjj6+4,"tx90p"]<-length(exmax90m5[exmax90m5>0&is.na(exmax90m5)==F])
amonex[jjj6+4,"tn10p"]<-length(exmin10m5[exmin10m5<0&is.na(exmin10m5)==F])
amonex[jjj6+4,"tn90p"]<-length(exmin90m5[exmin90m5>0&is.na(exmin90m5)==F])
amonex[jjj6+5,"tx10p"]<-length(exmax10m6[exmax10m6<0&is.na(exmax10m6)==F])
amonex[jjj6+5,"tx90p"]<-length(exmax90m6[exmax90m6>0&is.na(exmax90m6)==F])
amonex[jjj6+5,"tn10p"]<-length(exmin10m6[exmin10m6<0&is.na(exmin10m6)==F])
amonex[jjj6+5,"tn90p"]<-length(exmin90m6[exmin90m6>0&is.na(exmin90m6)==F])
amonex[jjj6+6,"tx10p"]<-length(exmax10m7[exmax10m7<0&is.na(exmax10m7)==F])
amonex[jjj6+6,"tx90p"]<-length(exmax90m7[exmax90m7>0&is.na(exmax90m7)==F])
amonex[jjj6+6,"tn10p"]<-length(exmin10m7[exmin10m7<0&is.na(exmin10m7)==F])
amonex[jjj6+6,"tn90p"]<-length(exmin90m7[exmin90m7>0&is.na(exmin90m7)==F])
amonex[jjj6+7,"tx10p"]<-length(exmax10m8[exmax10m8<0&is.na(exmax10m8)==F])
amonex[jjj6+7,"tx90p"]<-length(exmax90m8[exmax90m8>0&is.na(exmax90m8)==F])
amonex[jjj6+7,"tn10p"]<-length(exmin10m8[exmin10m8<0&is.na(exmin10m8)==F])
amonex[jjj6+7,"tn90p"]<-length(exmin90m8[exmin90m8>0&is.na(exmin90m8)==F])
amonex[jjj6+8,"tx10p"]<-length(exmax10m9[exmax10m9<0&is.na(exmax10m9)==F])
amonex[jjj6+8,"tx90p"]<-length(exmax90m9[exmax90m9>0&is.na(exmax90m9)==F])
amonex[jjj6+8,"tn10p"]<-length(exmin10m9[exmin10m9<0&is.na(exmin10m9)==F])
amonex[jjj6+8,"tn90p"]<-length(exmin90m9[exmin90m9>0&is.na(exmin90m9)==F])
amonex[jjj6+9,"tx10p"]<-length(exmax10m10[exmax10m10<0&is.na(exmax10m10)==F])
amonex[jjj6+9,"tx90p"]<-length(exmax90m10[exmax90m10>0&is.na(exmax90m10)==F])
amonex[jjj6+9,"tn10p"]<-length(exmin10m10[exmin10m10<0&is.na(exmin10m10)==F])
amonex[jjj6+9,"tn90p"]<-length(exmin90m10[exmin90m10>0&is.na(exmin90m10)==F])
amonex[jjj6+10,"tx10p"]<-length(exmax10m11[exmax10m11<0&is.na(exmax10m11)==F])
amonex[jjj6+10,"tx90p"]<-length(exmax90m11[exmax90m11>0&is.na(exmax90m11)==F])
amonex[jjj6+10,"tn10p"]<-length(exmin10m11[exmin10m11<0&is.na(exmin10m11)==F])
amonex[jjj6+10,"tn90p"]<-length(exmin90m11[exmin90m11>0&is.na(exmin90m11)==F])
amonex[jjj6+11,"tx10p"]<-length(exmax10m12[exmax10m12<0&is.na(exmax10m12)==F])
amonex[jjj6+11,"tx90p"]<-length(exmax90m12[exmax90m12>0&is.na(exmax90m12)==F])
amonex[jjj6+11,"tn10p"]<-length(exmin10m12[exmin10m12<0&is.na(exmin10m12)==F])
amonex[jjj6+11,"tn90p"]<-length(exmin90m12[exmin90m12>0&is.na(exmin90m12)==F])
if(leapyear(year)){
if(dd[dd$year==year&dd$month==2&dd$day==29,"tmax"]>aas[59,"pcmax90"]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,"tmax"])==F&is.na(aas[59,"pcmax90"])==F)
amonex[jjj6+1,"tx90p"]<-amonex[jjj6+1,"tx90p"]+1
if(dd[dd$year==year&dd$month==2&dd$day==29,"tmax"]<aas[59,"pcmax10"]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,"tmax"])==F&is.na(aas[59,"pcmax10"])==F)
amonex[jjj6+1,"tx10p"]<-amonex[jjj6+1,"tx10p"]+1
if(dd[dd$year==year&dd$month==2&dd$day==29,"tmin"]>aas[59,"pcmin90"]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,"tmin"])==F&is.na(aas[59,"pcmin90"])==F)
amonex[jjj6+1,"tn90p"]<-amonex[jjj6+1,"tn90p"]+1
if(dd[dd$year==year&dd$month==2&dd$day==29,"tmin"]<aas[59,"pcmin10"]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,"tmin"])==F&is.na(aas[59,"pcmin10"])==F)
amonex[jjj6+1,"tn10p"]<-amonex[jjj6+1,"tn10p"]+1
}
jjj6<-jjj6+12
year=year+1 }
bdm<-merge(bmonex,bd,by="year"); assign("bdm",bdm,envir=.GlobalEnv)
adm<-merge(amonex,ad,by="year"); assign("adm",adm,envir=.GlobalEnv)
} # end of nordaytem1 function
#----------- nordaytem1 ends -----------------------------------------
#----------- nordaytem -----------------------------------------
nordaytem<-function(){
nam1<-paste(nama,"_DAYNOR.csv",sep="")
write.table(daynor,file=nam1,append=F,quote=F,sep=", ",row.names=F)
}
#----------- nordaytem ends -----------------------------------------
#----------- dtr -----------------------------------------
dtr<-function(){# day temperature range(monthly average)
len<-yeare-years+1
aa1<-matrix(NA,12*len,3)
dimnames(aa1)<-list(NULL,c("year","month","dtr"))
aa1[,"year"]<-years:yeare
aa1[,"year"]<-mysort(aa1[,"year"],decreasing=F)
aa1[,"month"]<-1:12
temrange<-dd[,"tmax"]-dd[,"tmin"]
temrange<-cbind(dd[,1:2],temrange)
jjj1<-1
for (year in years:yeare){ # start year loop
temrange1<-temrange[temrange$year==year,]
temrangem1<-temrange1[temrange1$month==1,"temrange"]
temrangem2<-temrange1[temrange1$month==2,"temrange"]
temrangem3<-temrange1[temrange1$month==3,"temrange"]
temrangem4<-temrange1[temrange1$month==4,"temrange"]
temrangem5<-temrange1[temrange1$month==5,"temrange"]
temrangem6<-temrange1[temrange1$month==6,"temrange"]
temrangem7<-temrange1[temrange1$month==7,"temrange"]
temrangem8<-temrange1[temrange1$month==8,"temrange"]
temrangem9<-temrange1[temrange1$month==9,"temrange"]
temrangem10<-temrange1[temrange1$month==10,"temrange"]
temrangem11<-temrange1[temrange1$month==11,"temrange"]
temrangem12<-temrange1[temrange1$month==12,"temrange"]
aa1[jjj1,3]<-mean(temrangem1,na.rm=T); aa1[jjj1+1,3]<-mean(temrangem2,na.rm=T)
aa1[jjj1+2,3]<-mean(temrangem3,na.rm=T); aa1[jjj1+3,3]<-mean(temrangem4,na.rm=T)
aa1[jjj1+4,3]<-mean(temrangem5,na.rm=T); aa1[jjj1+5,3]<-mean(temrangem6,na.rm=T)
aa1[jjj1+6,3]<-mean(temrangem7,na.rm=T); aa1[jjj1+7,3]<-mean(temrangem8,na.rm=T)
aa1[jjj1+8,3]<-mean(temrangem9,na.rm=T); aa1[jjj1+9,3]<-mean(temrangem10,na.rm=T)
aa1[jjj1+10,3]<-mean(temrangem11,na.rm=T); aa1[jjj1+11,3]<-mean(temrangem12,na.rm=T)
jjj1<-jjj1+12} #end of year loop
aa1[,"dtr"]<-aa1[,"dtr"]+nacor[,"mnatma>3"]+nacor[,"mnatmi>3"]
ofile<-matrix(0,len,14)
dimnames(ofile)<-list(NULL,c("year","jan","feb","mar","apr","may","jun","jul","aug","sep","oct","nov","dec","annual"))
ofile<-as.data.frame(ofile)
for(j in years:yeare){
k<-j-years+1
ofile[k,1]<-j
ofile[k,2:13]<-round(aa1[aa1[,"year"]==j,"dtr"],digit=2)
ofile[k,14]<-round(mean(t(ofile[k,2:13]),na.rm=T),digit=2)
}
ofile[,14]<-ofile[,14]+ynacor[,"ynatma>15"]+ynacor[,"ynatmi>15"]
nam1<-paste(outinddir,paste(ofilename,"_DTR.csv",sep=""),sep="/")
write.table(ofile,file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
if(sum(is.na(ofile[,14]))>=(yeare-years+1-10)){
betahat<-NA
betastd<-NA
pvalue<-NA
}
else{
fit1<-lsfit(ofile[,1],ofile[,14])
out1<-ls.print(fit1,print.it=F)
pvalue<-round(as.numeric(out1$summary[1,6]),3)
betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
}
cat(file=namt,paste(latitude,longitude,"dtr",years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
nam2<-paste(outjpgdir,paste(ofilename,"_DTR.jpg",sep=""),sep="/")
jpeg(nam2,width=1024,height=768)
plotx(ofile[,1],ofile[,14],main=paste("DTR",ofilename,sep=" "),xlab="Year",ylab="DTR")
dev.off()
} # end of dtr
#----------- dtr ends -----------------------------------------
#----------- daysprcp10 -----------------------------------------
daysprcp10<-function(){
ys<-yeare-years+1
R10<-rep(0,ys)
yearss<-c(years:yeare)
target<-as.data.frame(cbind(yearss,R10))
for (year in years:yeare){
mid<-dd[dd$year==year,"prcp"]
mid<-mid[is.na(mid)==F]
target[target$yearss==year,"R10"]<-length(mid[mid>=10])}
dimnames(target)[[2]][1]<-"year"
target[,"R10"]<-target[,"R10"]+ynacor[,"ynapr>15"]
nam1<-paste(outinddir,paste(ofilename,"_R10mm.csv",sep=""),sep="/")
write.table(target,file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
if(sum(is.na(target[,"R10"]))>=(yeare-years+1-10)){
betahat<-NA
betastd<-NA
pvalue<-NA
}
else{
fit1<-lsfit(target[,1],target[,"R10"])
out1<-ls.print(fit1,print.it=F)
pvalue<-round(as.numeric(out1$summary[1,6]),3)
betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
}
cat(file=namt,paste(latitude,longitude,"r10mm",years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
nam2<-paste(outjpgdir,paste(ofilename,"_R10mm.jpg",sep=""),sep="/")
jpeg(nam2,width=1024,height=768)
plotx(target[,1],target[,2],main=paste("R10mm",ofilename,sep=" "),xlab="Year",ylab="R10mm")
dev.off()
}
#----------- daysprcp10 ends -----------------------------------------
#----------- extremedays -----------------------------------------
extremedays<-function(opt=0){
if(opt==0){
euu=uu
eul=ul
elu=lu
ell=ll
}
else{
euu=uuu
eul=uul
elu=ulu
ell=ull
}
ys<-yeare-years+1
# beginyear<-dd[1,1]
# endyear<-dd[dim(dd)[1],1]
tclext<-c(years:yeare)
su<-rep(0,ys)
id<-su
tr<-su
fd<-su
tclext<-cbind(tclext,su,id,tr,fd)
dimnames(tclext)[[2]][1]<-"year"
i=1
for (year in years:yeare) {
mid1<-dd[dd$year==year,"tmax"]; mid1<-mid1[is.na(mid1)==F]
mid2<-dd[dd$year==year,"tmin"]; mid2<-mid2[is.na(mid2)==F]
tclext[i,"su"]<-length(mid1[mid1>euu])
tclext[i,"id"]<-length(mid1[mid1<eul])
tclext[i,"tr"]<-length(mid2[mid2>elu])
tclext[i,"fd"]<-length(mid2[mid2<ell])
i<-i+1} #for end
tclext<-as.data.frame(tclext)
tclext[,"su"]<-tclext[,"su"]+ynacor[,"ynatma>15"]
tclext[,"id"]<-tclext[,"id"]+ynacor[,"ynatma>15"]
tclext[,"tr"]<-tclext[,"tr"]+ynacor[,"ynatmi>15"]
tclext[,"fd"]<-tclext[,"fd"]+ynacor[,"ynatmi>15"]
# assign("extdays",tclext,envir=.GlobalEnv)
if(opt==0){
nam1<-paste(outinddir,paste(ofilename,"_SU25.csv",sep=""),sep="/")
nam2<-paste(outinddir,paste(ofilename,"_ID0.csv",sep=""),sep="/")
nam3<-paste(outinddir,paste(ofilename,"_TR20.csv",sep=""),sep="/")
nam4<-paste(outinddir,paste(ofilename,"_FD0.csv",sep=""),sep="/")
}
else{
nam1<-paste(outinddir,paste(ofilename,"_SU",as.character(euu),".csv",sep=""),sep="/")
nam2<-paste(outinddir,paste(ofilename,"_ID",as.character(eul),".csv",sep=""),sep="/")
nam3<-paste(outinddir,paste(ofilename,"_TR",as.character(elu),".csv",sep=""),sep="/")
nam4<-paste(outinddir,paste(ofilename,"_FD",as.character(ell),".csv",sep=""),sep="/")
}
write.table(tclext[,c("year","su")],file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
write.table(tclext[,c("year","id")],file=nam2,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
write.table(tclext[,c("year","tr")],file=nam3,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
write.table(tclext[,c("year","fd")],file=nam4,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
# output trend base on annual indicies data
namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
for( i in c("su","id","tr","fd")){
if(sum(is.na(tclext[,i]))>=(yeare-years+1-10)){
betahat<-NA
betastd<-NA
pvalue<-NA
}
else{
fit1<-lsfit(tclext[,"year"],tclext[,i])
out1<-ls.print(fit1,print.it=F)
pvalue<-round(as.numeric(out1$summary[1,6]),3)
betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
}
if(opt==0){
if(i=="su") ii<-"su25"
if(i=="id") ii<-"id0"
if(i=="fd") ii<-"fd0"
if(i=="tr") ii<-"tr20"
}
else{
if(i=="su") ii<-paste("su",as.character(euu),sep="")
if(i=="id") ii<-paste("id",as.character(eul),sep="")
if(i=="tr") ii<-paste("tr",as.character(elu),sep="")
if(i=="fd") ii<-paste("fd",as.character(ell),sep="")
}
cat(file=namt,paste(latitude,longitude,ii,years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
}
namp<-c("","","","")
if(opt==0){
namp[1]<-paste(outjpgdir,paste(ofilename,"_SU25.jpg",sep=""),sep="/")
namp[2]<-paste(outjpgdir,paste(ofilename,"_ID0.jpg",sep=""),sep="/")
namp[3]<-paste(outjpgdir,paste(ofilename,"_TR20.jpg",sep=""),sep="/")
namp[4]<-paste(outjpgdir,paste(ofilename,"_FD0.jpg",sep=""),sep="/")
}
else{
namp[1]<-paste(outjpgdir,paste(ofilename,"_SU",as.character(euu),".jpg",sep=""),sep="/")
namp[2]<-paste(outjpgdir,paste(ofilename,"_ID",as.character(eul),".jpg",sep=""),sep="/")
namp[3]<-paste(outjpgdir,paste(ofilename,"_TR",as.character(elu),".jpg",sep=""),sep="/")
namp[4]<-paste(outjpgdir,paste(ofilename,"_FD",as.character(ell),".jpg",sep=""),sep="/")
}
if(opt==0) ylab<-c("SU25","ID0","TR20","FD0")
else ylab<-c(paste("SU",as.character(euu),sep=""),
paste("ID",as.character(eul),sep=""),
paste("TR",as.character(elu),sep=""),
paste("FD",as.character(ell),sep=""))
xlab<-rep("year",4)
for(i in 1:4){
title1[i]<-paste(ylab[i],ofilename,sep=" ")
jpeg(file=namp[i],width=1024,height=768)
plotx(tclext[,1],tclext[,i+1],main=title1[i],ylab=ylab[i],xlab="Year")
dev.off()
}
}
#----------- extremedays ends -----------------------------------------
#----------- exceedance -----------------------------------------
exceedance<-function(){
if (flag==T) return()
a<-1:365
ys<-endyear-startyear+1;yss<-ys-1
mondays<-c(31,28,31,30,31,30,31,31,30,31,30,31)
mone<-rep(0,12);mons<-mone
for(i in 1:12) mone[i]<-sum(mondays[1:i])
mons[1]<-1
for(i in 2:12) mons[i]<-mone[i-1]+1
monex<-matrix(NA,ys*12,6)
dimnames(monex)<-list(NULL,c("year","month","tx10p","tx90p","tn10p","tn90p"))
monex[,"month"]<-rep(1:12,ys)
monex[,"year"]<-startyear:endyear
monex[,"year"]<-mysort(monex[,"year"],decreasing=F)
monex<-as.data.frame(monex)
b<-matrix(0,365,4)
a<-cbind(a,b)
aa<-array(a,c(365,5,ys))
dimnames(aa)<-list(NULL,c("day","pcmax10","pcmax90","pcmin10","pcmin90"),NULL)
ms<-winsize*ys
i=winsize-round(winsize/2,digits=0)
i1=round(winsize/2,digits=0)
# daynorm2<-daynorm1[-(1:i1),] # daynorm2 is total base period normalized data
daynorm2<-dd[dd$year>=startyear,]
daynorm2<-daynorm2[daynorm2$year<=endyear,]
daynorm2<-daynorm2[daynorm2$month!=2|daynorm2$day!=29,]
daynorm2<-daynorm2[,-4]
# i2<-dim(daynorm2)[1]
# i3<-i2-i1+1
# daynorm2<-daynorm2[-(i3:i2),]
yearex<-c(startyear:endyear); txg10p<-rep(0,length(yearex))
txg90p<-rep(0,length(yearex)); tng10p<-rep(0,length(yearex))
tng90p<-rep(0,length(yearex))
d<-as.data.frame(cbind(yearex,txg10p,txg90p,tng10p,tng90p))
colnames(d)[1]<-"year"
monex<-matrix(0,ys*12,6)
dimnames(monex)<-list(NULL,c("year","month","tx10p","tx90p","tn10p","tn90p"))
monex[,"month"]<-rep(1:12,ys)
monex[,"year"]<-startyear:endyear
monex[,"year"]<-mysort(monex[,"year"],decreasing=F)
monex<-as.data.frame(monex)
ratecount<-matrix(0,365,4)
dimnames(ratecount)<-list(NULL,c("pcmax10","pcmax90","pcmin10","pcmin90"))
for (year in startyear:endyear){ # year loop start
midvalue<-daynorm2[daynorm2$year==year,]
zz=year-startpoint #index in base period, say, zzth year
indd<-exwin[exwin[,3]!=ys,3]
for (k in 1:(ys-1)){ # for k (boot strap) start
for (i in 1:365){ # day loop start
ppc<-exwins[,,i]
ppc<-ppc[ppc[,3]!=zz,]
ppc<-ppc[,-3]
ppc<-cbind(ppc,indd)
ppcc<-rbind(ppc[ppc[,"indd"]==k,],ppc)
itmp<-percentile(ms,ppcc[,1],c(0.1,0.9))
aa[i,"pcmax10",zz]<-itmp[1]-1e-5
aa[i,"pcmax90",zz]<-itmp[2]+1e-5
itmp<-percentile(ms,ppcc[,2],c(0.1,0.9))
aa[i,"pcmin10",zz]<-itmp[1]-1e-5
aa[i,"pcmin90",zz]<-itmp[2]+1e-5
}
ratecount[,"pcmax10"]<-midvalue[,"tmax"]-aa[,"pcmax10",zz]
ratecount[,"pcmax90"]<-midvalue[,"tmax"]-aa[,"pcmax90",zz]
ratecount[,"pcmin10"]<-midvalue[,"tmin"]-aa[,"pcmin10",zz]
ratecount[,"pcmin90"]<-midvalue[,"tmin"]-aa[,"pcmin90",zz]
for(mon in 1:12){
tmptx10p<-ratecount[mons[mon]:mone[mon],"pcmax10"]
tmptx90p<-ratecount[mons[mon]:mone[mon],"pcmax90"]
tmptn10p<-ratecount[mons[mon]:mone[mon],"pcmin10"]
tmptn90p<-ratecount[mons[mon]:mone[mon],"pcmin90"]
monex[(zz-1)*12+mon,"tx10p"]<- monex[(zz-1)*12+mon,"tx10p"]+length(tmptx10p[tmptx10p<0&is.na(tmptx10p)==F])
monex[(zz-1)*12+mon,"tx90p"]<- monex[(zz-1)*12+mon,"tx90p"]+length(tmptx90p[tmptx90p>0&is.na(tmptx90p)==F])
monex[(zz-1)*12+mon,"tn10p"]<- monex[(zz-1)*12+mon,"tn10p"]+length(tmptn10p[tmptn10p<0&is.na(tmptn10p)==F])
monex[(zz-1)*12+mon,"tn90p"]<- monex[(zz-1)*12+mon,"tn90p"]+length(tmptn90p[tmptn90p>0&is.na(tmptn90p)==F])
if(leapyear(year)&mon==2){
if(dd[dd$year==year&dd$month==2&dd$day==29,"tmax"]>aa[59,"pcmax90",zz]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,"tmax"])==F&is.na(aa[58,"pcmax90",zz])==F)
monex[(zz-1)*12+mon,"tx90p"]<-monex[(zz-1)*12+mon,"tx90p"]+1
if(dd[dd$year==year&dd$month==2&dd$day==29,"tmax"]<aa[59,"pcmax10",zz]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,"tmax"])==F&is.na(aa[58,"pcmax10",zz])==F)
monex[(zz-1)*12+mon,"tx10p"]<-monex[(zz-1)*12+mon,"tx10p"]+1
if(dd[dd$year==year&dd$month==2&dd$day==29,"tmin"]>aa[59,"pcmin90",zz]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,"tmin"])==F&is.na(aa[58,"pcmin90",zz])==F)
monex[(zz-1)*12+mon,"tn90p"]<-monex[(zz-1)*12+mon,"tn90p"]+1
if(dd[dd$year==year&dd$month==2&dd$day==29,"tmin"]<aa[59,"pcmin10",zz]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,"tmin"])==F&is.na(aa[58,"pcmin10",zz])==F)
monex[(zz-1)*12+mon,"tn10p"]<-monex[(zz-1)*12+mon,"tn10p"]+1
} #if end
} #for mon end
} #for k (boot strap) end
}# for year (from startyear to endyear) end
monex[,"tx10p"]<-monex[,"tx10p"]/29.
monex[,"tx90p"]<-monex[,"tx90p"]/29.
monex[,"tn10p"]<-monex[,"tn10p"]/29.
monex[,"tn90p"]<-monex[,"tn90p"]/29.
# monex<-rbind(bdm,monex,adm)
# assign("dm",dm,envir=.GlobalEnv)
dm<-merge(monex,d,by="year")
dm<-rbind(bdm,dm,adm)
len<-yeare-years+1
for(i in c("tx10p","tx90p","tn10p","tn90p")){
if (i=="tx10p") {ii<-"_TX10P.csv"; kk<-3;nastat=7}#natma
if (i=="tx90p") {ii<-"_TX90P.csv"; kk<-4;nastat=7}#natma
if (i=="tn10p") {ii<-"_TN10P.csv"; kk<-5;nastat=8}#natmi
if (i=="tn90p") {ii<-"_TN90P.csv"; kk<-6;nastat=8}#natmi
nam1<-paste(outinddir,paste(ofilename,ii,sep=""),sep="/")
ofile<-matrix(0,len,14)
dimnames(ofile)<-list(NULL,c("year","jan","feb","mar","apr","may","jun","jul","aug","sep","oct","nov","dec","annual"))
ofile<-as.data.frame(ofile)
for(j in years:yeare){
if(leapyear(j)) fulldays<-c(31,29,31,30,31,30,31,31,30,31,30,31)
else fulldays<-c(31,28,31,30,31,30,31,31,30,31,30,31)
k<-j-years+1
ofile[k,1]<-j
ofile[k,2:13]<-dm[dm$year==j,kk]
for(mon in 1:12){
if(nastatistic[(k-1)*12+mon,nastat]>10) ofile[k,(mon+1)]<-NA
else ofile[k,(mon+1)]<-dm[(k-1)*12+mon,kk]*fulldays[mon]/(fulldays[mon]-nastatistic[(k-1)*12+mon,nastat])
}
ofile[k,14]<-sum(ofile[k,2:13],na.rm=T)
}
ofile[,14]<-ofile[,14]+ynacor[,nastat-4]
for(j in years:yeare){
k<-j-years+1
if(leapyear(j)) fulldays<-c(31,29,31,30,31,30,31,31,30,31,30,31)
else fulldays<-c(31,28,31,30,31,30,31,31,30,31,30,31)
for(mon in 1:12) ofile[k,mon+1]<-ofile[k,mon+1]*100/fulldays[mon] # change output from counting days to %
}
ofile[,14]<-ofile[,14]*100/365 # change output from counting days to %
write.table(round(ofile,2),file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
if(sum(is.na(ofile[,14]))>=(yeare-years+1-10)){
betahat<-NA
betastd<-NA
pvalue<-NA
}
else{
fit1<-lsfit(ofile[,1],ofile[,14])
out1<-ls.print(fit1,print.it=F)
pvalue<-round(as.numeric(out1$summary[1,6]),3)
betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
}
cat(file=namt,paste(latitude,longitude,i,years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
nam2<-paste(outjpgdir,paste(ofilename,"_",toupper(i),".jpg",sep=""),sep="/")
jpeg(file=nam2,width=1024,height=768)
plotx(ofile[,1],ofile[,14],main=paste(toupper(i),ofilename,sep=" "),ylab=toupper(i),xlab="Year")
dev.off()
}
}
#----------- exceedance ends -----------------------------------------
#----------- index641cdd -----------------------------------------
index641cdd<-function(){
ys<-yeare-years+1
cdd<-rep(0,ys)
year<-c(years:yeare)
target<-as.data.frame(cbind(year,cdd))
year=years
for (i in 1:ys){
mid<-dd[dd$year==year,"prcp"]
# mid<-mid[is.na(mid)==F]
if(i==1) kk<-0
mm<-0
for(j in 1:length(mid)){
if(mid[j]<1&is.na(mid[j])==F) kk<-kk+1
else {
if(mm<kk) mm<-kk
kk<-0
}
}
if(mm<kk){
if(year==yeare) mm<-kk
else
if(dd[dd$year==year+1&dd$month==1&dd$day==1,"prcp"]>=1|is.na(dd[dd$year==year+1&dd$month==1&dd$day==1,"prcp"])==T) mm<-kk
# in case whole year dry, the next year will have a CDD bigger than 365
# then the CDD indice for current year should not be 0 but NA
if(mm==0) mm<-NA
}
target[i,"cdd"]<-mm
year=year+1
}
#for(i in 1:(ys-1))
# if(target[i,"cdd"]==0&target[i+1,"cdd"]>=365) target[i,"cdd"]<-NA
target[,"cdd"]<-target[,"cdd"]+ynacor[,"ynapr>15"]
nam1<-paste(outinddir,paste(ofilename,"_CDD.csv",sep=""),sep="/")
write.table(target,file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
if(sum(is.na(target[,"cdd"]))>=(yeare-years+1-10)){
betahat<-NA
betastd<-NA
pvalue<-NA
}
else{
fit1<-lsfit(target[,1],target[,"cdd"])
out1<-ls.print(fit1,print.it=F)
pvalue<-round(as.numeric(out1$summary[1,6]),3)
betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
}
cat(file=namt,paste(latitude,longitude,"cdd",years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
nam2<-paste(outjpgdir,paste(ofilename,"_CDD.jpg",sep=""),sep="/")
jpeg(nam2,width=1024,height=768)
plotx(target[,1],target[,2],main=paste("CDD",ofilename,sep=" "),xlab="Year",ylab="CDD")
dev.off()
}
#----------- index641cdd ends -----------------------------------------
#----------- index641cwd -----------------------------------------
index641cwd<-function(){
ys<-yeare-years+1
cwd<-rep(0,ys)
year<-years:yeare
target<-as.data.frame(cbind(year,cwd))
year=years
for (i in 1:ys){
mid<-dd[dd$year==year,"prcp"]
# mid<-mid[is.na(mid)==F]
if(i==1) kk<-0
mm<-0
for(j in 1:length(mid)){
if(mid[j]>=1&is.na(mid[j])==F) kk<-kk+1
else {
if(mm<kk) mm<-kk
kk<-0
}
}
if(mm<kk){
if(year==yeare) mm<-kk
else
if(dd[dd$year==year+1&dd$month==1&dd$day==1,"prcp"]<1|is.na(dd[dd$year==year+1&dd$month==1&dd$day==1,"prcp"])==T) mm<-kk
}
target[i,"cwd"]<-mm
year=year+1
}
target[,"cwd"]<-target[,"cwd"]+ynacor[,"ynapr>15"]
nam1<-paste(outinddir,paste(ofilename,"_CWD.csv",sep=""),sep="/")
write.table(target,file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
if(sum(is.na(target[,"cwd"]))>=(yeare-years+1-10)){
betahat<-NA
betastd<-NA
pvalue<-NA
}
else{
fit1<-lsfit(target[,1],target[,"cwd"])
out1<-ls.print(fit1,print.it=F)
pvalue<-round(as.numeric(out1$summary[1,6]),3)
betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
}
cat(file=namt,paste(latitude,longitude,"cwd",years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
nam2<-paste(outjpgdir,paste(ofilename,"_CWD.jpg",sep=""),sep="/")
jpeg(nam2,width=1024,height=768)
plotx(target[,1],target[,2],main=paste("CWD",ofilename,sep=" "),xlab="Year",ylab="CWD")
dev.off()
}
#----------- index641cwd ends -----------------------------------------
#----------- rx1d -----------------------------------------
rx1d<-function(){
len<-yeare-years+1
aa1<-matrix(NA,12*len,3)
dimnames(aa1)<-list(NULL,c("year","month","rx1d"))
aa1[,"year"]<-years:yeare
aa1[,"year"]<-mysort(aa1[,"year"],decreasing=F)
aa1[,"month"]<-1:12
jjj3=1
mid<-dd[,1:4]
for (year in years:yeare){
aaaa<-mid[mid$year==year,]
aaaam1<-aaaa[aaaa$month==1,"prcp"]; aaaam2<-aaaa[aaaa$month==2,"prcp"]
aaaam3<-aaaa[aaaa$month==3,"prcp"]; aaaam4<-aaaa[aaaa$month==4,"prcp"]
aaaam5<-aaaa[aaaa$month==5,"prcp"]; aaaam6<-aaaa[aaaa$month==6,"prcp"]
aaaam7<-aaaa[aaaa$month==7,"prcp"]; aaaam8<-aaaa[aaaa$month==8,"prcp"]
aaaam9<-aaaa[aaaa$month==9,"prcp"]; aaaam10<-aaaa[aaaa$month==10,"prcp"]
aaaam11<-aaaa[aaaa$month==11,"prcp"]; aaaam12<-aaaa[aaaa$month==12,"prcp"]
aa1[jjj3,"rx1d"]<-max(aaaam1,na.rm=T); aa1[jjj3+1,"rx1d"]<-max(aaaam2,na.rm=T)
aa1[jjj3+2,"rx1d"]<-max(aaaam3,na.rm=T); aa1[jjj3+3,"rx1d"]<-max(aaaam4,na.rm=T)
aa1[jjj3+4,"rx1d"]<-max(aaaam5,na.rm=T); aa1[jjj3+5,"rx1d"]<-max(aaaam6,na.rm=T)
aa1[jjj3+6,"rx1d"]<-max(aaaam7,na.rm=T); aa1[jjj3+7,"rx1d"]<-max(aaaam8,na.rm=T)
aa1[jjj3+8,"rx1d"]<-max(aaaam9,na.rm=T); aa1[jjj3+9,"rx1d"]<-max(aaaam10,na.rm=T)
aa1[jjj3+10,"rx1d"]<-max(aaaam11,na.rm=T); aa1[jjj3+11,"rx1d"]<-max(aaaam12,na.rm=T)
jjj3=jjj3+12}
aa1[,"rx1d"]<-aa1[,"rx1d"]+nacor[,"mnapr>3"]
ofile<-matrix(0,len,14)
# aa1<-as.data.frame(aa1)
dimnames(ofile)<-list(NULL,c("year","jan","feb","mar","apr","may","jun","jul","aug","sep","oct","nov","dec","annual"))
ofile<-as.data.frame(ofile)
for(j in years:yeare){
k<-j-years+1
ofile[k,1]<-j
ofile[k,2:13]<-aa1[aa1[,1]==j,3]
ofile[k,14]<-max(ofile[k,2:13],na.rm=F)
}
nam1<-paste(outinddir,paste(ofilename,"_RX1day.csv",sep=""),sep="/")
write.table(ofile,file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
if(sum(is.na(ofile[,14]))>=(yeare-years+1-10)){
betahat<-NA
betastd<-NA
pvalue<-NA
}
else{
fit1<-lsfit(ofile[,1],ofile[,14])
out1<-ls.print(fit1,print.it=F)
pvalue<-round(as.numeric(out1$summary[1,6]),3)
betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
}
cat(file=namt,paste(latitude,longitude,"rx1day",years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
nam2<-paste(outjpgdir,paste(ofilename,"_RX1day.jpg",sep=""),sep="/")
jpeg(nam2,width=1024,height=768)
plotx(ofile[,1],ofile[,14],main=paste("RX1day",ofilename,sep=" "),xlab="Year",ylab="RX1day")
dev.off()
}
#----------- rx1d ends -----------------------------------------
#----------- rx5d -----------------------------------------
rx5d<-function(){
a2<-c(0,0,0,0)
a1<-dd[,"prcp"]
a1<-append(a2,a1)
n<-length(a1)
a<-rep(0,n)
for (i in 5:n){
a[i]<-sum(a1[(i-4):i],na.rm=T)}
a<-a[-(1:4)]
a<-cbind(dd[,1:2],a)
len<-yeare-years+1
aa1<-matrix(NA,12*len,3)
dimnames(aa1)<-list(NULL,c("year","month","rx5d"))
aa1[,"year"]<-years:yeare
aa1[,"year"]<-mysort(aa1[,"year"],decreasing=F)
aa1[,"month"]<-1:12
jjj2=1
for (year in years:yeare){
aaaa<-a[a$year==year,]
aaaam1<-aaaa[aaaa$month==1,"a"]; aaaam2<-aaaa[aaaa$month==2,"a"]
aaaam3<-aaaa[aaaa$month==3,"a"]; aaaam4<-aaaa[aaaa$month==4,"a"]
aaaam5<-aaaa[aaaa$month==5,"a"]; aaaam6<-aaaa[aaaa$month==6,"a"]
aaaam7<-aaaa[aaaa$month==7,"a"]; aaaam8<-aaaa[aaaa$month==8,"a"]
aaaam9<-aaaa[aaaa$month==9,"a"]; aaaam10<-aaaa[aaaa$month==10,"a"]
aaaam11<-aaaa[aaaa$month==11,"a"]; aaaam12<-aaaa[aaaa$month==12,"a"]
aa1[jjj2,"rx5d"]<-max(aaaam1,na.rm=T); aa1[jjj2+1,"rx5d"]<-max(aaaam2,na.rm=T)
aa1[jjj2+2,"rx5d"]<-max(aaaam3,na.rm=T); aa1[jjj2+3,"rx5d"]<-max(aaaam4,na.rm=T)
aa1[jjj2+4,"rx5d"]<-max(aaaam5,na.rm=T); aa1[jjj2+5,"rx5d"]<-max(aaaam6,na.rm=T)
aa1[jjj2+6,"rx5d"]<-max(aaaam7,na.rm=T); aa1[jjj2+7,"rx5d"]<-max(aaaam8,na.rm=T)
aa1[jjj2+8,"rx5d"]<-max(aaaam9,na.rm=T); aa1[jjj2+9,"rx5d"]<-max(aaaam10,na.rm=T)
aa1[jjj2+10,"rx5d"]<-max(aaaam11,na.rm=T); aa1[jjj2+11,"rx5d"]<-max(aaaam12,na.rm=T)
jjj2=jjj2+12}
aa1[,"rx5d"]<-aa1[,"rx5d"]+nacor[,"mnapr>3"]
ofile<-matrix(0,len,14)
# aa1<-as.data.frame(aa1)
dimnames(ofile)<-list(NULL,c("year","jan","feb","mar","apr","may","jun","jul","aug","sep","oct","nov","dec","annual"))
ofile<-as.data.frame(ofile)
for(j in years:yeare){
k<-j-years+1
ofile[k,1]<-j
ofile[k,2:13]<-aa1[aa1[,1]==j,"rx5d"]
ofile[k,14]<-max(ofile[k,2:13],na.rm=F)
}
# print(dim(rx5d))
nam1<-paste(outinddir,paste(ofilename,"_RX5day.csv",sep=""),sep="/")
write.table(ofile,file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
if(sum(is.na(ofile[,14]))>=(yeare-years+1-10)){
betahat<-NA
betastd<-NA
pvalue<-NA
}
else{
fit1<-lsfit(ofile[,1],ofile[,14])
out1<-ls.print(fit1,print.it=F)
pvalue<-round(as.numeric(out1$summary[1,6]),3)
betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
}
cat(file=namt,paste(latitude,longitude,"rx5day",years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
nam2<-paste(outjpgdir,paste(ofilename,"_RX5day.jpg",sep=""),sep="/")
jpeg(nam2,width=1024,height=768)
plotx(ofile[,1],ofile[,14],main=paste("RX5day",ofilename,sep=" "),xlab="Year",ylab="RX5day")
dev.off()
}
#----------- rx5d ends -----------------------------------------
#----------- extremedaytem -----------------------------------------
extremedaytem<-function(){
len<-yeare-years+1
aa1<-matrix(NA,12*len,6)
dimnames(aa1)<-list(NULL,c("year","month","txx","txn","tnx","tnn"))
aa1[,"year"]<-years:yeare
aa1[,"year"]<-mysort(aa1[,"year"],decreasing=F)
aa1[,"month"]<-1:12
jjj4=1
for (year in years:yeare){
aaaa<-dd[dd$year==year,]
aaaama1<-aaaa[aaaa$month==1,"tmax"]; aaaama2<-aaaa[aaaa$month==2,"tmax"]
aaaama3<-aaaa[aaaa$month==3,"tmax"]; aaaama4<-aaaa[aaaa$month==4,"tmax"]
aaaama5<-aaaa[aaaa$month==5,"tmax"]; aaaama6<-aaaa[aaaa$month==6,"tmax"]
aaaama7<-aaaa[aaaa$month==7,"tmax"]; aaaama8<-aaaa[aaaa$month==8,"tmax"]
aaaama9<-aaaa[aaaa$month==9,"tmax"]; aaaama10<-aaaa[aaaa$month==10,"tmax"]
aaaama11<-aaaa[aaaa$month==11,"tmax"]; aaaama12<-aaaa[aaaa$month==12,"tmax"]
aaaami1<-aaaa[aaaa$month==1,"tmin"]; aaaami2<-aaaa[aaaa$month==2,"tmin"]
aaaami3<-aaaa[aaaa$month==3,"tmin"]; aaaami4<-aaaa[aaaa$month==4,"tmin"]
aaaami5<-aaaa[aaaa$month==5,"tmin"]; aaaami6<-aaaa[aaaa$month==6,"tmin"]
aaaami7<-aaaa[aaaa$month==7,"tmin"]; aaaami8<-aaaa[aaaa$month==8,"tmin"]
aaaami9<-aaaa[aaaa$month==9,"tmin"]; aaaami10<-aaaa[aaaa$month==10,"tmin"]
aaaami11<-aaaa[aaaa$month==11,"tmin"]; aaaami12<-aaaa[aaaa$month==12,"tmin"]
aa1[jjj4,"txx"]<-max(aaaama1,na.rm=T); aa1[jjj4+1,"txx"]<-max(aaaama2,na.rm=T)
aa1[jjj4+2,"txx"]<-max(aaaama3,na.rm=T); aa1[jjj4+3,"txx"]<-max(aaaama4,na.rm=T)
aa1[jjj4+4,"txx"]<-max(aaaama5,na.rm=T); aa1[jjj4+5,"txx"]<-max(aaaama6,na.rm=T)
aa1[jjj4+6,"txx"]<-max(aaaama7,na.rm=T); aa1[jjj4+7,"txx"]<-max(aaaama8,na.rm=T)
aa1[jjj4+8,"txx"]<-max(aaaama9,na.rm=T); aa1[jjj4+9,"txx"]<-max(aaaama10,na.rm=T)
aa1[jjj4+10,"txx"]<-max(aaaama11,na.rm=T); aa1[jjj4+11,"txx"]<-max(aaaama12,na.rm=T)
aa1[jjj4,"txn"]<-min(aaaama1,na.rm=T); aa1[jjj4+1,"txn"]<-min(aaaama2,na.rm=T)
aa1[jjj4+2,"txn"]<-min(aaaama3,na.rm=T); aa1[jjj4+3,"txn"]<-min(aaaama4,na.rm=T)
aa1[jjj4+4,"txn"]<-min(aaaama5,na.rm=T); aa1[jjj4+5,"txn"]<-min(aaaama6,na.rm=T)
aa1[jjj4+6,"txn"]<-min(aaaama7,na.rm=T); aa1[jjj4+7,"txn"]<-min(aaaama8,na.rm=T)
aa1[jjj4+8,"txn"]<-min(aaaama9,na.rm=T); aa1[jjj4+9,"txn"]<-min(aaaama10,na.rm=T)
aa1[jjj4+10,"txn"]<-min(aaaama11,na.rm=T); aa1[jjj4+11,"txn"]<-min(aaaama12,na.rm=T)
aa1[jjj4,"tnx"]<-max(aaaami1,na.rm=T); aa1[jjj4+1,"tnx"]<-max(aaaami2,na.rm=T)
aa1[jjj4+2,"tnx"]<-max(aaaami3,na.rm=T); aa1[jjj4+3,"tnx"]<-max(aaaami4,na.rm=T)
aa1[jjj4+4,"tnx"]<-max(aaaami5,na.rm=T); aa1[jjj4+5,"tnx"]<-max(aaaami6,na.rm=T)
aa1[jjj4+6,"tnx"]<-max(aaaami7,na.rm=T); aa1[jjj4+7,"tnx"]<-max(aaaami8,na.rm=T)
aa1[jjj4+8,"tnx"]<-max(aaaami9,na.rm=T); aa1[jjj4+9,"tnx"]<-max(aaaami10,na.rm=T)
aa1[jjj4+10,"tnx"]<-max(aaaami11,na.rm=T); aa1[jjj4+11,"tnx"]<-max(aaaami12,na.rm=T)
aa1[jjj4,"tnn"]<-min(aaaami1);aa1[jjj4+1,"tnn"]<-min(aaaami2,na.rm=T)
aa1[jjj4+2,"tnn"]<-min(aaaami3,na.rm=T); aa1[jjj4+3,"tnn"]<-min(aaaami4,na.rm=T)
aa1[jjj4+4,"tnn"]<-min(aaaami5,na.rm=T); aa1[jjj4+5,"tnn"]<-min(aaaami6,na.rm=T)
aa1[jjj4+6,"tnn"]<-min(aaaami7,na.rm=T); aa1[jjj4+7,"tnn"]<-min(aaaami8,na.rm=T)
aa1[jjj4+8,"tnn"]<-min(aaaami9,na.rm=T); aa1[jjj4+9,"tnn"]<-min(aaaami10,na.rm=T)
aa1[jjj4+10,"tnn"]<-min(aaaami11,na.rm=T); aa1[jjj4+11,"tnn"]<-min(aaaami12,na.rm=T)
jjj4=jjj4+12}
exdaytem<-as.data.frame(aa1)
# midnacor<-nacor[nacor$year>=startyear,]
# midnacor<-midnacor[midnacor$year<=endyear,]
exdaytem[,"txx"]<-exdaytem[,"txx"]+nacor[,"mnatma>3"]
exdaytem[,"txn"]<-exdaytem[,"txn"]+nacor[,"mnatma>3"]
exdaytem[,"tnx"]<-exdaytem[,"tnx"]+nacor[,"mnatmi>3"]
exdaytem[,"tnn"]<-exdaytem[,"tnn"]+nacor[,"mnatmi>3"]
for(i in c("txx","txn","tnx","tnn")){
if (i=="txx") {ii<-"_TXx.csv";ij<-"_TXx.jpg";kk<-3;ik<-1;ki<-3}# ik=1, take max as yearly record
if (i=="txn") {ii<-"_TXn.csv";ij<-"_TXn.jpg";kk<-4;ik<-0;ki<-3}# ik=0, take min as yearly record
if (i=="tnx") {ii<-"_TNx.csv";ij<-"_TNx.jpg";kk<-5;ik<-1;ki<-4}# ki=3, take TMAX annual missing values
if (i=="tnn") {ii<-"_TNn.csv";ij<-"_TNn.jpg";kk<-6;ik<-0;ki<-4}# ki=4, take TMIN annual missing values
nam<-paste(outinddir,paste(ofilename,ii,sep=""),sep="/")
ofile<-matrix(0,len,14)
# ojpg<-rep(0,len)
dimnames(ofile)<-list(NULL,c("year","jan","feb","mar","apr","may","jun","jul","aug","sep","oct","nov","dec","annual"))
ofile<-as.data.frame(ofile)
for(j in years:yeare){
k<-j-years+1
ofile[k,1]<-j
ofile[k,2:13]<-exdaytem[exdaytem$year==j,kk]
if(ik==1) ofile[k,14]<-max(ofile[k,2:13],na.rm=T)
else ofile[k,14]<-min(ofile[k,2:13],na.rm=T)
}
ofile[,14]<-ofile[,14]+ynacor[,ki]
write.table(ofile,file=nam,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
if(sum(is.na(ofile[,14]))>=(yeare-years+1-10)){
betahat<-NA
betastd<-NA
pvalue<-NA
}
else{
fit1<-lsfit(ofile[,1],ofile[,14])
out1<-ls.print(fit1,print.it=F)
pvalue<-round(as.numeric(out1$summary[1,6]),3)
betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
}
cat(file=namt,paste(latitude,longitude,i,years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
nam2<-paste(outjpgdir,paste(ofilename,ij,sep=""),sep="/")
jpeg(nam2,width=1024,height=768)
plotx(ofile[,1],ofile[,14],main=paste(toupper(i),ofilename,sep=" "),xlab="Year",ylab=(paste(toupper(substr(i,1,2)),substr(i,3,3),sep="")))
dev.off()
}
}
#----------- extremedaytem ends -----------------------------------------
#----------- index646 -----------------------------------------
index646<-function(){
ys=yeare-years+1
b<-matrix(0,ncol=2,nrow=ys)
dimnames(b)<-list(NULL,c("year","sdii"))
b[,"year"]<-c(years:yeare)
b<-as.data.frame(b)
year=years
for (i in 1:ys){
mid<-dd[dd$year==year,"prcp"]
mid<-mid[mid>=1]
b[i,"sdii"]<-mean(mid,na.rm=T)
year=year+1 }
b[,"sdii"]<-b[,"sdii"]+ynacor[,"ynapr>15"]
nam1<-paste(outinddir,paste(ofilename,"_SDII.csv",sep=""),sep="/")
write.table(round(b,digit=1),file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
if(sum(is.na(b[,"sdii"]))>=(yeare-years+1-10)){
betahat<-NA
betastd<-NA
pvalue<-NA
}
else{
fit1<-lsfit(b[,"year"],b[,"sdii"])
out1<-ls.print(fit1,print.it=F)
pvalue<-round(as.numeric(out1$summary[1,6]),3)
betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
}
cat(file=namt,paste(latitude,longitude,"sdii",years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
nam2<-paste(outjpgdir,paste(ofilename,"_SDII.jpg",sep=""),sep="/")
jpeg(nam2,width=1024,height=768)
plotx(b[,1],b[,2],main=paste("SDII",ofilename,sep=" "),xlab="Year",ylab="SDII")
dev.off()
}
#----------- index646 ends -----------------------------------------
#----------- main1 -----------------------------------------
main1<-function(){
main<-tktoplevel()
tkfocus(main)
tkwm.title(main,"Calculating Climate Indices")
tkgrid(tklabel(main,text="Check desired indices",font=fontHeading1))
txt="It may take 5 minutes to compute all the indices, "
# if(prallna==1) tkgrid(tklabel(main, text="PRCP all missing, indices related to PRCP may not be calculated",font=fontHeading2))
# if(txallna==1|tnallna==1) tkgrid(tklabel(main,text="TMAX or TMIN all missing, indices related to TMAX and TMIN may not be calculated",font=fontHeading2))
#cb0 <- tkcheckbutton(main);cb0Val <- cbvalue0
cb1 <- tkcheckbutton(main); cb1Val <- cbvalue1
cb2 <- tkcheckbutton(main); cb2Val <- cbvalue2
cb3 <- tkcheckbutton(main); cb3Val <- cbvalue3
cb4 <- tkcheckbutton(main); cb4Val <- cbvalue4
cb5 <- tkcheckbutton(main); cb5Val <- cbvalue5
cb6 <- tkcheckbutton(main); cb6Val <- cbvalue6
cb7 <- tkcheckbutton(main); cb7Val <- cbvalue7
cb8 <- tkcheckbutton(main); cb8Val <- cbvalue8
cb9 <- tkcheckbutton(main); cb9Val <- cbvalue9
cb10 <- tkcheckbutton(main); cb10Val <- cbvalue10
cb11 <- tkcheckbutton(main); cb11Val <- cbvalue11
cb12 <- tkcheckbutton(main); cb12Val <- cbvalue12
cb13 <- tkcheckbutton(main); cb13Val <- cbvalue13
cb14 <- tkcheckbutton(main); cb14Val <- cbvalue14
cb15 <- tkcheckbutton(main); cb15Val <- cbvalue15
cb21 <- tkcheckbutton(main); cb21Val <- cbvalue21
#tkconfigure(cb0,variable=cb0Val)#,value=cb1Val)
tkconfigure(cb1,variable=cb1Val)#,value=cb1Val)
tkconfigure(cb2,variable=cb2Val)#,value=cb2Val)
tkconfigure(cb3,variable=cb3Val)#,value=cb3Val)#"dtr")
tkconfigure(cb4,variable=cb4Val)#,value=cb4Val)#"daysprcp10")
tkconfigure(cb5,variable=cb5Val)#,value=cb5Val)#"nordaytem1")
tkconfigure(cb6,variable=cb6Val)#,value=cb6Val)#"extremedays")
tkconfigure(cb7,variable=cb7Val)#,value=cb7Val)#"exceedance")
tkconfigure(cb8,variable=cb8Val)#,value=cb8Val)#"ind144hwd")
tkconfigure(cb9,variable=cb9Val)#,value=cb9Val)#"ind641cdd")
tkconfigure(cb10,variable=cb10Val)#,value=cb10Val)#"rx5d")
tkconfigure(cb11,variable=cb11Val)#,value=cb11Val)#"ind646")
tkconfigure(cb12,variable=cb12Val)#,value=cb12Val)#"ind695")
tkconfigure(cb13,variable=cb13Val)#,value=cb13Val)#"rx1d"
tkconfigure(cb14,variable=cb14Val)#,value=cb14Val)#"extreme day tem"
tkconfigure(cb15,variable=cb15Val)
tkconfigure(cb21,variable=cb21Val)
#tkgrid(tklabel(main,text="Select the indices you want to calculate:"))
#tkgrid(tklabel(main,text="ALL 26 indices!!"),cb0)
tkgrid(tklabel(main,text="SU25, FD0, TR20, ID0"),cb1)
tkgrid(tklabel(main,text="User Defined SU, FD, TR, ID"),cb21)
tkgrid(tklabel(main,text="GSL, growing season length"),cb2)#143
tkgrid(tklabel(main,text="TXx, TXn, TNx, TNn"),cb3)
#tkgrid(tklabel(main,text="TXn, TNx, TNn, following by same choice"),cb3)#extremedaytem
tkgrid(tklabel(main,text="TX10p, TX90p, TN10p, TN90p"),cb4)
#tkgrid(tklabel(main,text="TX90p, TN10p, TN90p, following by same choice"),cb4)#exceedance
tkgrid(tklabel(main,text="WSDI"),cb5)#hwfi
tkgrid(tklabel(main,text="CSDI"),cb6)#cwdi
#tkgrid(tklabel(main,text="Normal day temperature with user defined window size"),cb2)
tkgrid(tklabel(main,text="DTR"),cb7)#dtr
tkgrid(tklabel(main,text="Rx1day"),cb8)#rx1d
tkgrid(tklabel(main,text="Rx5day"),cb9)#rx5d
tkgrid(tklabel(main,text="SDII"),cb10)#index646
tkgrid(tklabel(main,text="R10mm"),cb11)#daysprcp10()
tkgrid(tklabel(main,text="R20mm"),cb12)#daysprcp20()
tkgrid(tklabel(main,text="Rnnmm"),cb13)#daysprcpn()
tkgrid(tklabel(main,text="CDD, CWD"),cb14)
#tkgrid(tklabel(main,text="CWD"),cb14)#641
#tkgrid(tklabel(main,text="daynortem "),cb5)
#tkgrid(tklabel(main,text="ind144hwd "),cb8)#144
tkgrid(tklabel(main,text="R95p, R99p, PRCPTOT"),cb15)#695
#tkgrid(tklabel(main,text="R99pTOT"),cb15)
#tkgrid(tklabel(main,text="Annual Days with PRCP>=95 percentile"),cb17)#r95ptot
#tkgrid(tklabel(main,text="Annual Days with PRCP>=99 percentile"),cb17)
#----------- OnOK -----------------------------------------
OnOK <- function(){
#filename<-tclvalue(tkgetSaveFile(filetypes="{{EXCEL Files} {.csv}} {{All files} *}"))
#if (!nchar(filename))
#tkmessageBox(message="No file was selected!")
#else tkmessageBox(message=paste("The results will be saved under",filename))
#nam<-substr(filename,start=1,stop=(nchar(filename)-4))
#assign("nam",nam,envir=.GlobalEnv)
# cbv0 <- as.character(tclvalue(cb0Val))
cbv1 <- as.character(tclvalue(cb1Val))
cbv21 <- as.character(tclvalue(cb21Val))
cbv2 <- as.character(tclvalue(cb2Val))
cbv3 <- as.character(tclvalue(cb3Val))
cbv4 <- as.character(tclvalue(cb4Val))
cbv5 <- as.character(tclvalue(cb5Val))
cbv6 <- as.character(tclvalue(cb6Val))
cbv7 <- as.character(tclvalue(cb7Val))
cbv8 <- as.character(tclvalue(cb8Val))
cbv9 <- as.character(tclvalue(cb9Val))
cbv10 <- as.character(tclvalue(cb10Val))
cbv11 <- as.character(tclvalue(cb11Val))
cbv12 <- as.character(tclvalue(cb12Val))
cbv13 <- as.character(tclvalue(cb13Val))
cbv14 <- as.character(tclvalue(cb14Val))
cbv15 <- as.character(tclvalue(cb15Val))
tkdestroy(main)
# if (cbv0==1) {cbv1<-1;cbv2<-1;cbv3<-1;cbv4<-1;cbv5<-1;
namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
cat(file=namt,paste("Lon","Lat","Indices","SYear","EYear","Slope","STD_of_Slope","P_Value",sep=","),fill=180,append=F)
if (cbv1==1) if(txallna==0&tnallna==0) extremedays()
if (cbv21==1) if(txallna==0&tnallna==0) extremedays(opt=1)
if (cbv2==1) if(txallna==0&tnallna==0) ind143gsl()
if (cbv3==1) if(txallna==0&tnallna==0) extremedaytem()
if (cbv4==1) if(txallna==0&tnallna==0) exceedance()
if (cbv5==1) if(txallna==0) hwfi()
if (cbv6==1) if(tnallna==0) cwdi()
if (cbv7==1) if(txallna==0&tnallna==0) dtr()
if (cbv8==1) if(prallna==0) rx1d()
if (cbv9==1) if(prallna==0) rx5d()
if (cbv10==1) if(prallna==0) index646()
if (cbv11==1) if(prallna==0) daysprcp10()
if (cbv12==1) if(prallna==0) daysprcp20()
if (cbv13==1) if(prallna==0) daysprcpn()
if (cbv14==1) if(prallna==0) {index641cdd();index641cwd()}
if (cbv15==1) if(prallna==0) r95ptot()
cbvalue1<-cb1Val;assign("cbvalue1",cbvalue1,envir=.GlobalEnv)
cbvalue2<-cb2Val;assign("cbvalue2",cbvalue2,envir=.GlobalEnv)
cbvalue3<-cb3Val;assign("cbvalue3",cbvalue3,envir=.GlobalEnv)
cbvalue4<-cb4Val;assign("cbvalue4",cbvalue4,envir=.GlobalEnv)
cbvalue5<-cb5Val;assign("cbvalue5",cbvalue5,envir=.GlobalEnv)
cbvalue6<-cb6Val;assign("cbvalue6",cbvalue6,envir=.GlobalEnv)
cbvalue7<-cb7Val;assign("cbvalue7",cbvalue7,envir=.GlobalEnv)
cbvalue8<-cb8Val;assign("cbvalue8",cbvalue8,envir=.GlobalEnv)
cbvalue9<-cb9Val;assign("cbvalue9",cbvalue9,envir=.GlobalEnv)
cbvalue10<-cb10Val;assign("cbvalue10",cbvalue10,envir=.GlobalEnv)
cbvalue11<-cb11Val;assign("cbvalue11",cbvalue11,envir=.GlobalEnv)
cbvalue12<-cb12Val;assign("cbvalue12",cbvalue12,envir=.GlobalEnv)
cbvalue13<-cb13Val;assign("cbvalue13",cbvalue13,envir=.GlobalEnv)
cbvalue14<-cb14Val;assign("cbvalue14",cbvalue14,envir=.GlobalEnv)
cbvalue15<-cb15Val;assign("cbvalue15",cbvalue15,envir=.GlobalEnv)
cbvalue21<-cb21Val;assign("cbvalue21",cbvalue21,envir=.GlobalEnv)
nstation<-tktoplevel()
tkwm.title(nstation,"Calculation Done")
tkfocus(nstation)
okk<-function(){tkdestroy(nstation);tkfocus(start1)}
textlabel0<-tklabel(nstation,text=" ")
textlabel1<-tklabel(nstation,text="Indices calculation completed",font=fontHeading1)
textlabel2<-tklabel(nstation,text=paste("Plots are in: ",outjpgdir,sep=" "),font=fontHeading1)
okk.but<-tkbutton(nstation,text=" OK ",command=okk,width=20)
tkgrid(textlabel0)
tkgrid(textlabel1)
tkgrid(textlabel2)
tkgrid.configure(textlabel1,sticky="w")
tkgrid.configure(textlabel2,sticky="w")
tkgrid.configure(textlabel0,sticky="e")
# cancell.but<-tkbutton(nstation,text=" NO ",command=cancell,width=15)
# tkgrid(textlabel2,okk.but,cancell.but,textlabel0)
tkgrid(okk.but,textlabel0)
# tkgrid.configure(cancell.but,sticky="w")
tkgrid(textlabel0)
}
#----------- OnOK ends -----------------------------------------
#----------- done2 -----------------------------------------
done2<-function(){
tkdestroy(main)
tkfocus(start1)
# return()
}
#----------- done2 ends -----------------------------------------
ok.but <-tkbutton(main,text=" OK ",command=OnOK,width=30)
cancel.but<-tkbutton(main,text="CANCEL",command=done2,width=30)
tkgrid(ok.but)
tkgrid(cancel.but)
tkgrid(tklabel(main,text="It may take more than 5 minutes to compute all the indices",font=fontHeading2))
tkgrid(tklabel(main,text="Please be patient, you will be informed once computations are done",font=fontHeading2))
tkgrid(tklabel(main,text="",font=fontHeading))#empty line
}
#----------- main1 ends -----------------------------------------
# End of Part II ( Functions of Calculating climate indecies )
##################################################################################
##################################################################################
# Part III
# Main program
# call getfile, read data file and store in dd.
##################################################################################
start1<-tktoplevel()
#----------- plotx -----------------------------------------
plotx<-
function (x,y,main="",xlab="",ylab="")
{
plot(x,y,xlab=xlab,ylab=ylab,type="b")
fit<-lsfit(x,y)
out<-ls.print(fit,print.it=F)
r2<-round(100*as.numeric(out$summary[1,2]),1)
pval<-round(as.numeric(out$summary[1,6]),3)
beta<-round(as.numeric(out$coef.table[[1]][2,1]),3)
betaerr<-round(as.numeric(out$coef.table[[1]][2,2]),3)
abline(fit,lwd=3)
xy<-cbind(x,y)
xy<-na.omit(xy)
lines(lowess(xy[,1],xy[,2]),lwd=3,lty=2)
subtit=paste("R2=",r2," p-value=",pval," Slope estimate=",beta," Slope error=",betaerr)
title(main=main)
title(sub=subtit,cex=0.5)
}
#----------- plotx ends -----------------------------------------
#----------- ts -----------------------------------------
ts<-function(ys=x[1,1],ye=x[nrow(x),1],x=dd)
{
#
# EDA function
#
par(mfrow=c(3,1))
xs<-x[(x[,1]>=ys)&(x[,1]<=ye),]
ts.plot(xs[,4])
title("Daily precipitation",xlab="day",ylab="precip")
ts.plot(xs[,5])
title("Daily maximum temperature",xlab="day",ylab="tmax")
ts.plot(xs[,6])
title("Daily minimum temperature",xlab="day",ylab="tmin")
par(mfrow=c(1,1))
cat(paste("Station series defined from ",x[1,1]," to ",x[nrow(x),1],"\n"))
cat(paste("Summary statistics for window from ",ys," to ", ye,"\n"))
}
#----------- ts ends -----------------------------------------
#----------- ?????????? -----------------------------------------
function (y1=x[1,1],y2=dd[nrow(x),1],m=1,v=4,x=dd)
{
#
# Little function to see how many NAs there are in month m
# in period y1 to y2 for variable v:
#
# Usage: nas(1960,1990,1,4)
#
xs<-x[(x[,1]>=y1)&(x[,1]<=y2)&(x[,2]==m),]
cat(paste("Years from ",y1,"-",y2," Month=",m," Variable=",v,"\n"))
cat(paste("Total number of days=",length(xs[,v])," Total missing=",sum(is.na(xs[,v])),"\n"))
}
#----------- ?????? ends -----------------------------------------
startss<-function(){
tkwm.title(start1,"RclimDex")
tkgrid(tklabel(start1,text=" RClimDex by Junior version BETA ",font=fontHeading))
tkgrid(tklabel(start1,text="",font=fontHeading))#empty line
tkgrid(tklabel(start1,text="",font=fontHeading))#empty line
#tkgrid(tklabel(start1,text="",font=fontHeading))#empty line
start.but<-tkbutton(start1,text="Load Data and Run QC",command=getfile,width=30)
cal.but<-tkbutton(start1,text=" Indices Calculation ",command=nastat,width=60)
cancel.but<-tkbutton(start1,text="Exit",command=done,width=30)
tkgrid(start.but)
tkgrid(cal.but)
tkgrid(cancel.but)
tkgrid(tklabel(start1,text="",font=fontHeading))
}#end of startss function
startss()
##################################################################################.
---
title: Variabilidad climática en el gradiente altitudinal en la vertiente del Caribe
  (5 msnm y 1400 msnm).
author: "Andrés Carranza Quirós,
        Stacy Chacón Fallas,
        Ana María Ocampo Cambronero,
        Argerie Oviedo Bolaños,
        Génesis Rodriguez Naranjo."
output:
  html_notebook:
      code_folding: hide
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(eval = FALSE, include = FALSE)
```
<div class=text-justify>
##**Resumen**##

Resumen: 
Costa Rica presenta un clima variable, con altas temperaturas, y abundantes precipitaciones. Los vientos provenientes del Caribe, generan variaciones en el comportamiento de diversos factores climáticos en el país. Esta investigación plantea analizar la variabilidad climática en los últimos 30 años en dos gradientes altitudinales en la vertiente del Caribe, para determinar los factores que producen cambios en las temperaturas y precipitación. Se trabajó una base de datos de precipitación (mm), temperatura máxima y mínima (°C) en dos estaciones, proporcionados por el Instituto Meteorológico Nacional. Mediante el programa R-ClimDex, se calcularon diversos índices climáticos, basados en un previo análisis de regresión lineal.Cada uno de los índices con valores significativos (P-value <0.05), se sometieron a la prueba de Shapiro.Wilk a los residuos para demostrar la normalidad de los datos con ayuda del programa R. Precipitaciones con mayor intensidad y temperaturas más bajas son los factores con más significancia. Dichas variaciones obedecen a los fenómenos ENOS, que generan lluvias en menos tiempo y más violentas. Los rayos del sol no penetran en su totalidad a causa de mucha nubosidad, por  lo que la temperatura baja. Esto supone la existencia de variabilidad climática, afectando a largo plazo los ecosistemas y las actividades humanas.

**Palabras clave:** Variabilidad climática, precipitación, temperatura, gradiente altitudinal, Vertiente del Caribe.


##**Introducción**##
Costa Rica es un país que se caracteriza porque gran parte del año presenta un clima caracterizado por sus altas temperaturas y abundantes lluvias representativo de un clima tropical. Por esta razón la variación más importante es la lluvia ya que afectan los diferentes sistemas de viento y la topografía (Lizano y Salas, 2001). Costa Rica comprende la vertiente del caribe la cual presenta temperaturas muy variadas de acuerdo con la altitud, siendo ésta la distancia vertical que existe sobre cualquier punto de la Tierra tomando como referencia el nivel del mar, por lo que ésta zona se encuentra directamente expuesta a los vientos alisios que llegan cargados de humedad al Caribe. Este parámetro produce una gran cantidad de variaciones en el comportamiento de diferentes componentes o factores climáticos (Retana y Villalobos, 2000). Estas variaciones reciben el nombre de variabilidad climática la cual es un fenómeno que afecta a una gran cantidad de sectores, desde las actividades humanas como en los diferentes entornos naturales que sufren importantes variaciones en la estabilidad de sus ecosistemas (Yáñez-Arancibia, Twilley y Lara-Domínguez, 1998).   
 
Existen varios factores a la hora de estudiar los efectos de la variabilidad climática sobre un entorno, donde la precipitación y la medición de las temperaturas máximas y mínimas ofrecen un claro panorama a la hora de determinar estos cambios (Puertas y Carvajal, 2008). La precipitación que es la caída de agua desde la atmósfera hasta la superficie terrestre, formando parte del ciclo del agua. La precipitación se genera por la condensación del agua, es decir, la formación de nubes por la acumulación de agua en la atmósfera. Otro factor importante es la temperatura atmosférica, la cual se ve reflejada cuando las radiaciones solares llegan a la superficie terrestre provocando que su energía caliente el suelo y así mismo las capas del aire que se encuentran en contacto. Este efecto se ve reflejado en el aumento de la temperatura a lo largo de los años ya que las radiaciones no son absorbidas por el suelo y se reflejan para escapar de nuevo a la atmósfera, sin embargo, se devuelven a la superficie terrestre debido al dióxido de carbono y el vapor (Alfaro y Amador, 1997). La temperatura atmosférica, comprende temperaturas máximas, que es la mayor temperatura del aire alcanzada en un lugar en un día (máxima diaria), en un mes (máxima mensual) o en un año (máxima anual), y temperaturas mínimas, que al contrario, es la menor temperatura registrada en un lugar a lo largo de un día, mes o año.  
 
Es importante comprender la diferencia entre variabilidad y cambio climático, la variabilidad según Alzate, Rojas, Mosquera y Ramón (2015) se puede definir como cambios o modificaciones en los componentes climáticos en periodos de tiempo cortos mientras que el cambio climático se refiere a diferencias climáticas a lo largo de grandes periodos y que presentan un estado irreversible a su condición previa. Debido a esto es que la variabilidad climática debe de ser analizada en rangos temporales pequeños como meses, ya que si se promedian las variables a lo largo de un año no se detectarán las diferencias. 
La variabilidad climática puede ser causada por oscilaciones térmicas, que son diferencias numéricas entre los valores máximos y mínimos de temperatura observados en un punto dado durante un período de tiempo. Una de estas oscilaciones es la Oscilación del sur (ENOS), que son los cambios interanuales, con una escala de tiempo de varios años (2 a 7 años), de las condiciones atmosféricas y temperaturas oceánicas fluctuantes sobre los océanos Pacifico e Indico ecuatoriales (Fernández y Ramírez, 1991). El Niño es la fase caliente del ENOS, y se refiere a la interacción climática océano-atmósfera a gran escala, asociada a un calentamiento periódico. Por otra parte, La Niña es la fase fría del ENOS asociada al Niño, es precedida y seguida por periodos en los cuales las temperaturas superficiales del mar son más bajas de lo normal en el pacífico central y oriental y los vientos alisios son muy fuertes (Fernández y Ramírez, 1991). 

En este contexto, es de gran importancia estudiar la variabilidad climática, ya que se pueden realizar perspectivas a corto y a mediano plazo, y con esto se podrían implementar acciones respecto a la adaptación de la variabilidad climática (Markus, 2017). El presente trabajo tiene como objetivo analizar la variabilidad climática en los últimos 30 años en dos gradientes altitudinales en la vertiente del Caribe (5 y 1400 msnm), para determinar los impactos que podrían producir los cambios en temperatura (máxima y mínima) y precipitación de ambas zonas. 
 
##**Objetivos**##

**Objetivo general**

Analizar la variabilidad climática en los últimos 30 años en dos gradientes altitudinales en la vertiente del Caribe (5 y 1400 msnm), para determinar los impactos que podrían generar los cambios en temperatura y precipitación de ambas zonas. 
 
**Objetivos específicos **

1-Determinar los principales cambios en la precipitación y temperatura (mínima y máxima) en dos altitudes de la vertiente Caribe (5 y 1400 msnm).  

2-Comparar la variabilidad climática entre las dos estaciones del Caribe a diferentes altitudes (5 y 1400 msnm). 

3-Generar perspectivas de los cambios en la precipitación y temperatura (máxima y mínima) de ambas zonas. 


##**Materiales y Métodos**##

En esta investigación se utilizó datos de precipitación (mm) y temperatura (máxima y mínima, °C) diarios de dos estaciones meteorológicas (Linda Vista y Limón) proporcionados por el Instituto Meteorológico Nacional (IMN, Cuadro 1). Ambas estaciones poseen el mismo periodo de medición de datos diarios entre 1980 y 2010 y se encuentran en la misma vertiente Caribe, pero difieren en altitud (Limón: 5 msnm; y Linda Vista: 1400 msnm).
![](cuadro-1.png)
La altitud entre ambas estaciones fue un factor determinante en esta investigación. Se tomaron en cuenta estaciones con poca similitud en términos de gradiente altitudinal, con el fin de determinar el comportamiento de las lluvias y la afectación de otros fenómenos en cada uno de las fronteras de la vertiente del Caribe. Las estaciones meteorológicas poseen un periodo de 30 años, que representa el mínimo de años requerido para el estudio de clima en una zona específica.

###################Figura1####################

Mediante el programa de R, se calculó el promedio mensual de la temperatura máxima, temperatura mínima y precipitación. Para poder calcular la temperatura promedio mensual de cada año, cada mes debe contener como mínimo el 50 % de la información completa. Con respecto a los datos de precipitación, se realiza una sumatoria, debe de existir mínimo 24 días que se refieren al 80% de la información completa por mes.
 
Este programa necesita de ciertos parámetros, para poder realizar un análisis estadístico. Se realizó una comparación de la información disponible durante los treinta años con de la primera década de los datos. Se calculó para los valores mínimos y máximos con el percentil 5 y 95 tanto de la temperatura máxima como de la temperatura mínima.  Se calculó el percentil 95 para los datos de precipitación.

Para poder describir la variabilidad mediante análisis estadísticos se utilizan diversas metodologías, las cuales estudian el comportamiento del clima en espacio y tiempo. Un ejemplo de ello, es la aplicación de un programa estadístico llamado RClimDex, el cual realiza un análisis estadístico de la variabilidad temporal de las precipitaciones, temperaturas máximas y mínimas. (Zhang y Yang, 2004). Este programa fue diseñado para dar seguimiento y detectar el cambio climático. Además se utiliza como plataforma de trabajo, el programa R por ser vigoroso y completo en lo que respecta al análisis estadístico y al mismo tiempo muy minucioso en la elaboración de gráficos. RClimDex facilita una interfaz para el cálculo de índices extremos climáticos con límites que pueden ser definidos por el usuario. Este programa permite calcular 27 índices (Zhang y Yang, 2004), dentro de los cuales solo 26 de ellos fueron útiles para la investigación realizada. 
Los índices utilizados en el programa RClimDex, se basan en valores de temperatura máxima diaria, temperatura mínima diaria y precipitación diaria, donde Tx representa los valores de temperatura máxima, Tn los valores de temperatura mínima, PRCP indica la cantidad de precipitación y RR define los días húmedos (días con lluvia). 
Los valores de referencia que se requieren para el análisis de los índices se obtuvieron a partir de los registros diarios de los primeros 10 años de estudio (1980-1990). Se calcularon los valores máximos de precipitación diaria que se registran con el percentil 90 que se refiere al 90% de las precipitaciones; se establece este valor con el fin de eliminar los valores extremos poco frecuentes Además de ello, se calculan los valores máximos y mínimos de la temperatura máxima y los valores máximos y mínimos de la temperatura mínima. Con ello, se presenta un resumen de los 26 índices de RClimDex, que se utilizaron para el análisis de esta investigación y se describen a continuación. Cabe destacar que uno de los índices con los que trabaja este programa, se utiliza para calcular temperaturas menores a los 0°C, por ello, no cumple con los objetivos de este trabajo, ya que Costa Rica al ser un país tropical, no cuenta con esas temperaturas.}

![](cuadro-2.png)
Análisis de datos
Los índices previamente mencionados se sometieron a análisis de regresión lineal (α=0.05). Todos los supuestos de regresión lineal fueron sometidos a prueba (normalidad de residuos: Shapiro Wilk, Waine (1991); e independencia de residuos). Los datos atípicos fueron previamente detectados y eliminados de todas las regresiones lineales. 
RClimDex genera diferentes tipos de figuras de cada uno de los índices calculados, sin embargo, para efectos de esta investigación fueron nuevamente realizados con ayuda de lenguaje de programa de R, versión 3.5.1 (RCoreTeam, 2018).  

##**Resultados**##

Como lo muestra el cuadro 2, se utilizaron 26 índices, dentro de los cuales solo 17 resultaron con valores significativos (resaltados en negrita en el cuadro). Los resultados del análisis de los datos significativos diarios de tres décadas realizado tanto para la estación 81003 (Limón) y la estación 73018 (Linda Vista, El Guarco) se muestran en los cuadros a continuación:
![](cuadro-3.png)

Todos los índices mencionados en el cuadro 3 y 4, demuestran que cumplen con valores significativos a la hora de realizar el análisis de regresión, con un p-value menor a 0.005 en todos los casos, lo que demuestra que hay diversos factores que afectan la linealidad de los datos, se nota claramente que existe una tendencia a la variabilidad climática, la mayoría de los índices muestran un comportamiento creciente con respecto al tiempo. Dicha información estadística se corrobora observando los gráficos de los principales índices para las estaciones de Limón y El Guarco.

En la figura 2 los índices de la estación 73018 (Linda Vista, El Guarco) se observa que la gráfica A muestra que durante las tres últimas décadas hubo un descenso en la precipitación alrededor de 16mm a 8 mm. En la gráfica B se muestra un aumento constante de la máxima precipitación que se está concentrando en caer en 5 días seguidos. En la gráfica C hubo un aumento de las precipitaciones mayores a 10 mm durante los últimos 30 año.En la gráfica C se mostró un aumento constante de las precipitaciones mayores a 20 mm durante los últimos 30 años.

![](figura-2.png)

```{r}
"SDII"
lm(sdii~year,data=X73018_RClimDex_SDII_sustituido)->x
summary(x)->y
y
visreg(x,main="SDII (Precipitación Diaria)",xlab="Tiempo (Días)",ylab="Precipitación (mm)")
shapiro.test(x$residuals)

"RX5DAY"
lm(annual~year,data=X73018_RClimDex_RX5day_sustituido)->x
summary(x)->y
y
visreg(x,main="RX5day(Máxima 
       precipitación 5 días seguidos)",xlab="Tiempo (Años)",ylab="Precipitación (mm)")
shapiro.test(x$residuals)

"R10mm"
lm(R10~year,data=X73018_RClimDex_R10mm_sustituido)->x
summary(x)->y
y
visreg(x,main="R10mm (Precipitacion > 10mm)",xlab="Tiempo (Años)",ylab="Precipitación (mm)")
shapiro.test(x$residuals)

"R20mm"
lm(R20~year,data=X73018_RClimDex_R20mm_sustituido)->x
summary(x)->y
y
visreg(x,main="R20mm (Precipitación > 20mm)",xlab="Tiempo (Años)",ylab="Precipitación (mm)")
shapiro.test(x$residuals)

```


En la figura 3 la precipitación anual en días muy húmedos de la gráfica A aumentó de manera constante ya que se observa un cambio alrededor de 100mm a 500mm durante los últimos 30 años. En la gráfica B muestra que conforme pasan los años los días secos consecutivos van disminuyendo de manera constante. En la gráfica C los días húmedos consecutivos aumentaron de manera constante durante los últimos 30 años. En la gráfica D la precipitación anual total aumentó alrededor de 1000 mm a 2000 mm de manera constante en los días húmedos durante los últimos 30 años.

![](figura-3.png)

```{r}
"R95P"
lm(r95p~year,data =X73018_RClimDex_R95p_sustituido)->x
summary(x)->y
y
visreg(x,main="R95P(Días muy húmedos)",xlab="Tiempo(años)",ylab="Precipitación anual(mm)")
shapiro.test(x$residuals)

"CDD"
lm(cdd~year,data=X73018_RClimDex_CDD_sustituidooo)->x
summary(x)->y
y
visreg(x,main="CDD(Días secos consecutivos)",xlab="Tiempo(años)",ylab="Días secos(días)")
shapiro.test(x$residuals)
influencePlot(x)
z<-X73018_RClimDex_CDD_sustituidooo[-c(1,3,4,31),]
lm(cdd~year,data = z)->z2
shapiro.test(z2$residuals)
summary(z2)
visreg(z2,main="Días secos consecutivos",xlab="Tiempo(años",ylab="Días secos(días)")



"CWD"
lm(cwd~year,data=X73018_RClimDex_CWD_sustituido)->x
summary(x)->y
y
visreg(x,main="CWD(Días húmedos consecutivos)",xlab="Tiempo(años)",ylab="Días húmedos(días)")
shapiro.test(x$residuals)


"PRCPTOT"
lm(prcptot~year,data =X73018_RClimDex_PRCPTOT_sustituir)->x
summary(x)->y
y
visreg(x,main="PCRPTOT(Precipitación total anual en los días húmedos)",xlab="Tiempo(años)",ylab="Precipitación(mm)")
shapiro.test(x$residuals)

```


En la figura 4 se muestran la cantidad de días fríos (Figura A) donde se puede observar una pendiente ascendente con respecto a las últimas décadas por lo que estos días aumentan cada vez más, de igual manera las noches caliente (Figura B) tienden a presentar una pendiente ascendente ya que pasó de tener solo una noche caliente en 1980 a 5 en 1992.

![](figura-4.png)

```{r}
"TX10P(Días Fríos)"
lm(annual~year,data=X73018_RClimDex_TX10P_sustituido)->x
summary(x)->y
y
visreg(x)
plot(x)
shapiro.test(x$residuals)
influencePlot(x)
x2<-X73018_RClimDex_TX10P_sustituido[-c(12,27,28),]
lm(annual~year,data=x2)->x3
shapiro.test(x3$residuals)
summary(x3)
visreg(x3)

"TN90P (Noches Calientes)"
lm(annual~year,data=X73018_RClimDex_TN90P_sustituido)->x
summary(x)->y
y
visreg(x,main="TX10P (Noches Calientes)",xlab="Tiempo (Años)",ylab="Noches Calientes (Días)")
shapiro.test(x$residuals)

```


En la figura 5 la precipitación de la estación 81003 (Limón) muestra que la intensidad de diaria de la precipitación de la gráfica A  aumenta hasta alrededor de 22 mm/día durante los últimos 30 años. En el caso de la gráfica B y C la precipitación máxima aumentó en 1 día y en 5 días respectivamente durante un periodo de 30 años. En la gráfica C se muestra un aumento de los días muy húmedos con respecto a los últimos 30 años.

![](figura-5.png)

```{r}
"RX1day(Cantaidad máxima de precipitación en 1 día)"
datosP<-lm(annual~year, data = X81003_RClimDex_RX1day_1_ )
datosP
summary(datosP)
shapiro.test (datosP$residuals)
graf<-visreg(datosP,"year", partial=F, main= "RX1day(Cantaidad máxima de precipitación en 1 día)", xlab= "Tiempo(Años)", ylab="Precipitación(mm)")
graf

```


En la figura 6 los índices de precipitación para la estación 18003 (Limón) muestran en la gráfica A un aumento del número de días mayores a 43 mm con respecto a los últimos 30 años y en el caso de la gráfica B se observa que los días extremadamente tienen un incremento con respecto a los últimos 30 años.


![](figura-6.png)

```{r}
"R43(Número de días>43mm)"
dat<-lm(Rnn~year, data = X81003_RClimDex_R43_515mm)
dat
summary(dat)
shapiro.test (dat$residuals)
graf<-visreg(dat,"year", partial=F, main= "R43(Número de días>43mm)", xlab= "Tiempo(Años)", ylab="Días>43mm(días)")
graf

 "R99p(Días extremadamente húmedos)"
datosl<-lm(r99p~year, data = X81003_RClimDex_R99p)
datosl
summary(datosl)
shapiro.test (datosl$residuals)
graf<-visreg(datosl,"year", partial=F, main= "R99p(Días extremadamente húmedos)", xlab= "Tiempo(Años)", ylab="Precipitación(mm)")
graf

```


En la figura 7 se muestra en la gráfica A que la temperatura diurna es menor bajando un grado, en la gráfica B sin embargo los días fríos han disminuido, y las noches calientes aumentado en la (Figura C). En la figura D se observa una disminución de los días calientes de 5 a 1.

![](figura-7.png)

```{r}
"DTR(Temperatura Diurna)"
datos1<-lm(annual~year, data = X81003_RClimDex_DTR_usar)
datos1
summary(datos1)
shapiro.test (datos1$residuals)
graf<-visreg(datos1,"year", partial=F, main= "DTR(Temperatura Diurna)", xlab= "Tiempo(días)", ylab="Temperatura(°C)")
graf

"TN90P(Noches calientes)"
datos8<-lm(annual~year, data = X81003_RClimDex_TN90P)
datos8
summary(datos8)
shapiro.test (datos8$residuals)
graf<-visreg(datos8,"year", partial=F, main= "TN90P(Noches calientes)", xlab= "Tiempo(Años)", ylab="Tiempo(días)")
graf

"TX90P(Días calientes)"
datis<-lm(annual~year, data = X81003_RClimDex_TX90P)
datis
summary(datis)
shapiro.test (datis$residuals)
graf<-visreg(datis,"year", partial=F, main= "TX90P(Días calientes)", xlab= "Tiempo(Años)", ylab="Tiempo(días)")
graf


```


Finalmente en la figura 8 se observa que en la figura A hay una disminución en las temperaturas máximas, esto confirmado por la figura B donde cada vez son menos los días con temperaturas mayores a 32° pasando de 20 en 1980 a 10 en el 2010.

![](figura-8.png)

```{r}
"SU32(Días de verano>32)"
datosA<-lm(su~year, data = X81003_RClimDex_SU32_4)
datosA
summary(datosA)
shapiro.test (datosA$residuals)
graf<-visreg(datosA,"year", partial=F, main= "SU32(Días de verano>32)", xlab= "Tiempo(Años)", ylab="Días>32mm(días)")
graf

```


##**Discusión**##
Para la temperatura en la estación de Linda Vista, El Guarco (73018) (Figura 4) podemos observar una tendencia en las temperaturas a disminuir, este aumento de temperaturas bajas en esta región del país puede ser causado por el aumento en la nubosidad que a su vez genera una prolongación en las temperaturas mínimas y una disminución de las temperaturas máximas (Gómez y Fernández 1996, Fernández y Ramírez 1991),las cuales según nuestros resultados coinciden con estas conclusiones. De igual manera la estación localizada en Limón presenta las mismas tendencias a aumentar las temperaturas mínimas (Figuras 7 y 8) por lo que podemos concluir que a partir de los datos recolectados de ambas estaciones que una gran parte de la vertiente del Caribe está presentando una tendencia hacia temperaturas más bajas, esto se debe a que las nubes influyen en la calidez del clima, de esta manera las nubes que se encuentran bajas provocan una disminución de la temperatura ya que son más densas y no permiten que los rayos del Sol penetren en su totalidad.(Marvel, 2017)

Un aumento en la nubosidad a causa de las precipitaciones genera un fenómeno que explica el comportamiento de las noches a calentarse cada vez más. La nubosidad al reflejar la radiación, tiene un doble efecto sobre el aumento o disminución de las temperaturas en la superficie terrestre. Por el día, una alta concentración de nubes reflejan la radiación solar que incide a la superficie produciendo una sensación de frescura, mientras que en la noche, la nubosidad se encarga de reflejar la radiación infrarroja emitida desde la superficie de la tierra, generando noches más calientes. A su vez, las propias nubes llegan a calentarse durante el proceso y emiten parte de esa radiación a la superficie. (Zúñiga y Crespo, 2010)

Posteriormente al analizar los datos obtenidos con respecto a la precipitación de la estación de Linda Vista, El Guarco (Figuras 2 y 3) podemos ver que las precipitaciones diarias tienden a disminuir, esto puede ser explicado  ya que ahora las precipitaciones se concentran en un número de días menor (Figura 2B), por lo que aunque diariamente hay menos precipitaciones, esto se repone en una cantidad de días menor de lluvias. En la estación localizada en Limón se observan las mismos conclusiones (Figuras 5 y 6) con la excepción de que además las lluvias diarias también aumentan lo que significa que las precipitaciones en esta región han aumentado significativamente. Fernández y Ramírez (1991) explican que estas observaciones realizadas en el Caribe obedecen a comportamientos propios del fenómenos del ENOS el cual entre los meses de julio y agosto se observa una tendencia a mucha precipitación con una inclinación a ser cortas y violentas e incluso la aparición de temporales debido al aumento de los vientos alisios sobre esta región.

Se calculó, a partir de las regresiones obtenidas, cuánto cambiarían las variables tanto de temperatura máxima y mínima como de precipitación al cabo de 5 y 10 años (Cuadro 3). Se notó que aunque los cambios van a seguir la misma tendencia, estos a corto y mediano plazo no son una gran amenaza ya que son irrelevantes y no corresponden a un cambio brusco de las condiciones climáticas por lo que también se puede inferir que estas condiciones no van a generar un cambio climático como tal. Por otro lado si analizamos los cambios a largo plazo, podrían generar cierto riesgo al alterar de manera muy significativa el ambiente físico. Se puede observar evidencia de que existen cambios poco relevantes (Cuadro 4) ya que son solo diez años de diferencia pero cuando hablamos de 40 años o más, la variabilidad climática será más visibles. 

Podemos concluir que existe variabilidad climática en la vertiente del caribe lo cual puede suponer una problemática a largo plazo tanto para el ecosistema como para las actividades humanas, especialmente para los sectores más vulnerables como lo son el sector agrícola y el ecoturismo (Amador y Alfaro, 2009). En el caso de la primera situaciones como las inundaciones causan además de la pérdida de zonas de cultivo, que el agua de mar sature el suelo y esta se filtre hacia los mantos acuíferos los cuales son utilizados para el riego de los cultivos, algo indispensable para la agricultura (Altieri y Nicholls, 2009). En el caso del ecoturismo Díaz (2012) menciona que la cantidad de ganancias en los entornos naturales con gran atracción turística pueden llegar a verse influenciados negativamente, llegando a tener pérdidas económicas hasta de un 30% si estos se empiezan a ver afectados por la variabilidad climática. Con esto es pertinente realizar mayores observaciones con respecto a las medidas necesarias para disminuir los impactos que estos fenómenos causan. 


##**Referencias**##

Yáñez-Arancibia, A., Twilley, R. R., y Lara-Domínguez, A. L. (1998). Los ecosistemas de manglar frente al cambio climático global. Madera y Bosques, 4(2), 3-19. 

Puertas Orozco, O. L., y Carvajal Escobar, Y. (2008). Incidence of El Niño southern oscillation in the precipitation and the temperature of the air in Colombia, using Climate Explorer. Ingeniería y Desarrollo, (23), 104-118. 

Alzate, D., Rojas, E., Mosquera, J., y Ramón, J. (2015). Cambio climático y variabilidad climática para el periodo 1981-2010 en las cuencas de los ríos Zulia y pamplonita, norte de Santander–Colombia. Revista Luna Azul, (40). 
 
Lizano, O y Salas, D. (2001). Variaciones geomorfológicas en los últimos 50 años en la isla Damas, Quepos, Costa Rica. Revista Biología Tropical. 49(2):171-177. 

Retana, J y Villalobos, R. (2000). Caracterización pluviométrica de la fase cálida de ENOS en Costa Rica basado en probabilidades de ocurrencia de eventos en tres escenarios: seco, normal y lluvioso. Tópicos Meteorológicos y Oceanográficos. 7(2):124-130. 
 
Amador, J.A. y A. “Ciclones tropicales y sociedad: Una aproximación al enfoque científico de estos fenómenos atmosféricos como referente para la investigación social en desastres”, en Concepciones y Representaciones de la Naturaleza y la Ciencia en América Latina, editado por R.Viales, J. Amador y F.J. Solano. San José: Editorial de la Universidad de Costa Rica, 2009, 159-179. 
 
Fallas, J.C y R. Oviedo. “Temporales”. Cap. III. En: Fenómenos atmosféricos y cambio climático, visión centroamericana. Instituto Meteorológico Nacional, San José, Costa Rica, (2003): 38. 
 
Fernández, W., y Ramírez, P. (1991). El Niño, la Oscilación del Sur y sus efectos en Costa Rica: una revisión. Tecnología en Marcha, 11(1), 3-10. 

R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Waine, D. (1991). Bioestadística, base para el análisis de las ciencias de la salud. Mexico., México DF. LIMUSA. Recuperado de http://www.academia.edu/17988752/Bioestadistica_Base_para_el_analisis_de_las_ciencias_de_la_salud

Zhang, X., y Yang, F. (2004). RClimDex (1.0). Manual de Usuario. Canadá.

N/A. S/F. Universidad Nacional de Costa Rica. Recuperado de http://www.repositorio.una.ac.cr/bitstream/handle/11056/7520/estaciones_imn_2008.216.png?sequence=1&isAllowed=y

Altieri, M. A., & Nicholls, C. I. (2009). Cambio climático y agricultura campesina: impactos y respuestas adaptativas. LEISA revista de agroecología, 14, 5-8.

Gómez, I. E., y Fernández, W. (1996). Variación interanual de la temperatura en Costa Rica. Top. Meteor. Oceanogr, 3(1), 27-44.

Amador, J. A., y Alfaro, E. J. (2009). Métodos de reducción de escala: aplicaciones al tiempo, clima, variabilidad climática y cambio climático. Revibec: revista iberoamericana de economía ecológica, 11, 39-52.

Moreno-Díaz, M. L. (2012). Actividades socioeconómicas en el Parque Nacional Isla del Coco, Costa Rica y posibles efectos de la variabilidad climática. Revista de Biología Tropical, 60(3), 113-129.

Zúñiga, I., y Crespo, E. (2010) Meteorología y climatología. Recuperado de https://books.google.co.cr/books?id=E6iXJ2QZiQ4C&pg=PA63&dq=radiacion+infrarroja+nocturna+calor+en+las+noches+nubosidad&hl=es&sa=X&ved=0ahUKEwiGlub67a_eAhUkwlkKHb7OB-gQ6AEIKzAB#v=onepage&q=radiacion%20infrarroja%20nocturna%20calor%20en%20las%20noches%20nubosidad&f=false


##**Anexos**##
Anexo 1. Precipitación mensual estación 81003 (Limón)

![](anexo-1.png)

Anexo2. Precipitación mensual estación  73018 (Linda Vista, El Guarco)

![](anexo-2.png)

Anexo 3.Temperatura mensual estación 81003 (Limón)

![](anexo-3.png)
Anexo 4.Instalación de paquetes requeridos para la organización de datos
```{r eval=FALSE, include=TRUE}
install.packages("reshape")
install.packages("splitstackshape")
install.packages("plyr")
install.packages("ggplot2")
install.packages("xts")
install.packages("dygraphs")
```

Anexo 5.Organización de la base de datos
```{r eval=FALSE, include=TRUE}
#####################################################################
## Script para organizar Información Climática INAMHI 2016
## Credits: Junior Pastor PÉREZ-MOLINA
## Date: 2017/10/08
#####################################################################
rm(list = ls()) #Remove all objects
graphics.off()  #Remove all graphics
cat("\014")     #Remove script in windows console
if(!grepl("Organización información climática", getwd())){x= cat(prompt = "Please set the working directory to the project folder ")}
#####################################################################


#####################################################################
## Cargar los paquetes requeridos para organizar Información Climática
#####################################################################
# install.packages("reshape")
library(reshape)
# install.packages("splitstackshape")
library(splitstackshape)
# install.packages("plyr")
library("plyr", lib.loc="~/R/win-library/3.4")
# install.packages("ggplot2")
library(ggplot2)
# install.packages("xts")
library(xts) 
# install.packages("dygraphs")
library(dygraphs)
#####################################################################


#####################################################################
## PRECIPITACION    ->   Loading database 
#####################################################################
Precipitacion<-read.delim("Data/PRCP.txt",header=FALSE,sep="\t",dec=".")
names(Precipitacion)<-c("codigo","year","mes","d1","d2","d3","d4","d5","d6","d7","d8","d9","d10","d11","d12","d13","d14","d15","d16","d17","d18","d19","d20","d21","d22","d23","d24","d25","d26","d27","d28","d29","d30","d31")
library(reshape)
Precipitacion <- melt(Precipitacion, id=c("codigo","year","mes"))
Precipitacion$value<-as.numeric(as.character(Precipitacion$value))
names(Precipitacion)<-c("codigo","year","month","day","PRCP")
Precipitacion<-data.frame(Precipitacion)
Precipitacion$day<-as.character(Precipitacion$day)
Precipitacion$day<-gsub("d", "", Precipitacion$day)
Precipitacion$day<-as.numeric(Precipitacion$day)
Precipitacion$date<-paste(Precipitacion$year,Precipitacion$month,Precipitacion$day, sep="-")
Precipitacion$date<-as.Date(Precipitacion$date, format = "%Y-%m-%d")
Precipitacion<-Precipitacion[order(Precipitacion$codigo,Precipitacion$year, Precipitacion$month, Precipitacion$day),]
Precipitacion$PRCP<-replace(Precipitacion$PRCP, is.na(Precipitacion$PRCP), -99.9)
Precipitacion$date2<-Precipitacion$date
Precipitacion$date2<-ifelse(is.na(Precipitacion$date2),1,0)
Precipitacion[Precipitacion$date2==1,]<-NA
Precipitacion<-Precipitacion[order(Precipitacion$codigo,Precipitacion$year, Precipitacion$month, Precipitacion$day),]
Precipitacion$date2<-NULL
Precipitacion<-na.omit(Precipitacion)
Precipitacion_ok<-data.frame()
for(i in unique(Precipitacion$codigo)){
    sub<-Precipitacion[Precipitacion$codigo==i,]
    date<-data.frame(seq(as.Date(paste(format(as.Date(min(na.omit(sub$date)), format="%Y/%m/%d"),"%Y"),"-01-01"),format = "%Y -%m-%d"),
                         as.Date(paste(format(as.Date(max(na.omit(sub$date)), format="%Y/%m/%d"),"%Y"),"-12-31"),format = "%Y -%m-%d"),
                         by="day"))
    names(date)<-c("date")
    date$date<-as.Date(date$date, format = "%Y-%m-%d")
    sub2<-merge(date,sub, all=TRUE)
    sub2$codigo<-c(i)
    Precipitacion_ok <- rbind(Precipitacion_ok,data.frame(sub2))
}
Precipitacion_ok$date2<-Precipitacion_ok$date
library(splitstackshape)
Precipitacion_ok<-cSplit(Precipitacion_ok, "date2", "-")
Precipitacion_ok<-data.frame(Precipitacion_ok[,2],Precipitacion_ok[,7],Precipitacion_ok[,8],Precipitacion_ok[,9],Precipitacion_ok[,6])
names(Precipitacion_ok)<-c("codigo","year","month","day","PCPT")
#####################################################################


#####################################################################
## Tmax             ->   Loading database 
#####################################################################
Tmax<-read.delim("Data/TMAX.txt",header=FALSE,sep="\t",dec=".")
names(Tmax)<-c("codigo","year","mes","d1","d2","d3","d4","d5","d6","d7","d8","d9","d10","d11","d12","d13","d14","d15","d16","d17","d18","d19","d20","d21","d22","d23","d24","d25","d26","d27","d28","d29","d30","d31")
library(reshape)
Tmax <- melt(Tmax, id=c("codigo","year","mes"))
Tmax$value<-as.numeric(as.character(Tmax$value))
names(Tmax)<-c("codigo","year","month","day","Tmax")
Tmax<-data.frame(Tmax)
Tmax$day<-as.character(Tmax$day)
Tmax$day<-gsub("d", "", Tmax$day)
Tmax$day<-as.numeric(Tmax$day)
Tmax$date<-paste(Tmax$year,Tmax$month,Tmax$day, sep="-")
Tmax$date<-as.Date(Tmax$date, format = "%Y-%m-%d")
Tmax<-Tmax[order(Tmax$codigo,Tmax$year, Tmax$month, Tmax$day),]
Tmax$Tmax<-replace(Tmax$Tmax, is.na(Tmax$Tmax), -99.9)
Tmax$date2<-Tmax$date
Tmax$date2<-ifelse(is.na(Tmax$date2),1,0)
Tmax[Tmax$date2==1,]<-NA
Tmax<-Tmax[order(Tmax$codigo,Tmax$year, Tmax$month, Tmax$day),]
Tmax$date2<-NULL
Tmax<-na.omit(Tmax)
Tmax_ok<-data.frame()
for(i in unique(Tmax$codigo)){
    sub<-Tmax[Tmax$codigo==i,]
    date<-data.frame(seq(as.Date(paste(format(as.Date(min(na.omit(sub$date)), format="%Y/%m/%d"),"%Y"),"-01-01"),format = "%Y -%m-%d"),
                         as.Date(paste(format(as.Date(max(na.omit(sub$date)), format="%Y/%m/%d"),"%Y"),"-12-31"),format = "%Y -%m-%d"),
                         by="day"))
    names(date)<-c("date")
    date$date<-as.Date(date$date, format = "%Y-%m-%d")
    sub2<-merge(date,sub, all=TRUE)
    sub2$codigo<-c(i)
    Tmax_ok <- rbind(Tmax_ok,data.frame(sub2))
}
Tmax_ok$date2<-Tmax_ok$date
library(splitstackshape)
Tmax_ok<-cSplit(Tmax_ok, "date2", "-")
Tmax_ok<-data.frame(Tmax_ok[,2],Tmax_ok[,7],Tmax_ok[,8],Tmax_ok[,9],Tmax_ok[,6])
names(Tmax_ok)<-c("codigo","year","month","day","Tmax")
#####################################################################


#####################################################################
## Tmin             ->   Loading database 
#####################################################################
Tmin<-read.delim("Data/TMIN.txt",header=FALSE,sep="\t",dec=".")
names(Tmin)<-c("codigo","year","mes","d1","d2","d3","d4","d5","d6","d7","d8","d9","d10","d11","d12","d13","d14","d15","d16","d17","d18","d19","d20","d21","d22","d23","d24","d25","d26","d27","d28","d29","d30","d31")
library(reshape)
Tmin <- melt(Tmin, id=c("codigo","year","mes"))
Tmin$value<-as.numeric(as.character(Tmin$value))
names(Tmin)<-c("codigo","year","month","day","Tmin")
Tmin<-data.frame(Tmin)
Tmin$day<-as.character(Tmin$day)
Tmin$day<-gsub("d", "", Tmin$day)
Tmin$day<-as.numeric(Tmin$day)
Tmin$date<-paste(Tmin$year,Tmin$month,Tmin$day, sep="-")
Tmin$date<-as.Date(Tmin$date, format = "%Y-%m-%d")
Tmin<-Tmin[order(Tmin$codigo,Tmin$year, Tmin$month, Tmin$day),]
Tmin$Tmin<-replace(Tmin$Tmin, is.na(Tmin$Tmin), -99.9)
Tmin$date2<-Tmin$date
Tmin$date2<-ifelse(is.na(Tmin$date2),1,0)
Tmin[Tmin$date2==1,]<-NA
Tmin<-Tmin[order(Tmin$codigo,Tmin$year, Tmin$month, Tmin$day),]
Tmin$date2<-NULL
Tmin<-na.omit(Tmin)
Tmin_ok<-data.frame()
for(i in unique(Tmin$codigo)){
    sub<-Tmin[Tmin$codigo==i,]
    date<-data.frame(seq(as.Date(paste(format(as.Date(min(na.omit(sub$date)), format="%Y/%m/%d"),"%Y"),"-01-01"),format = "%Y -%m-%d"),
                         as.Date(paste(format(as.Date(max(na.omit(sub$date)), format="%Y/%m/%d"),"%Y"),"-12-31"),format = "%Y -%m-%d"),
                         by="day"))
    names(date)<-c("date")
    date$date<-as.Date(date$date, format = "%Y-%m-%d")
    sub2<-merge(date,sub, all=TRUE)
    sub2$codigo<-c(i)
    Tmin_ok <- rbind(Tmin_ok,data.frame(sub2))
}
Tmin_ok$date2<-Tmin_ok$date
library(splitstackshape)
Tmin_ok<-cSplit(Tmin_ok, "date2", "-")
Tmin_ok<-data.frame(Tmin_ok[,2],Tmin_ok[,7],Tmin_ok[,8],Tmin_ok[,9],Tmin_ok[,6])
names(Tmin_ok)<-c("codigo","year","month","day","Tmin")
#####################################################################



#####################################################################
## PCPT_Tmax_Tmin   ->   Loading database 
#####################################################################
PCPT_Tmax<-merge(Precipitacion,  Tmax, by=c("date","codigo", "year", "month", "day"), all=TRUE)
PCPT_Tmax_Tmin<-merge(PCPT_Tmax, Tmin, by=c("date","codigo", "year", "month", "day"), all=TRUE)
PCPT_Tmax_Tmin_ok<-data.frame()
for(i in unique(PCPT_Tmax_Tmin$codigo)){
    sub<-PCPT_Tmax_Tmin[PCPT_Tmax_Tmin$codigo==i,]
    date<-data.frame(seq(as.Date(paste(format(as.Date(min(na.omit(sub$date)), format="%Y/%m/%d"),"%Y"),"-01-01"),format = "%Y -%m-%d"),
                         as.Date(paste(format(as.Date(max(na.omit(sub$date)), format="%Y/%m/%d"),"%Y"),"-12-31"),format = "%Y -%m-%d"),
                         by="day"))
    names(date)<-c("date")
    date$date<-as.Date(date$date, format = "%Y-%m-%d")
    sub2<-merge(date,sub, all=TRUE)
    sub2$codigo<-c(i)
    PCPT_Tmax_Tmin_ok <- rbind(PCPT_Tmax_Tmin_ok,data.frame(sub2))
}
PCPT_Tmax_Tmin_ok$date2<-PCPT_Tmax_Tmin_ok$date
library(splitstackshape)
PCPT_Tmax_Tmin_ok<-cSplit(PCPT_Tmax_Tmin_ok, "date2", "-")
PCPT_Tmax_Tmin_ok<-data.frame(PCPT_Tmax_Tmin_ok[,2],PCPT_Tmax_Tmin_ok[,9],PCPT_Tmax_Tmin_ok[,10],PCPT_Tmax_Tmin_ok[,11],PCPT_Tmax_Tmin_ok[,6],PCPT_Tmax_Tmin_ok[,7],PCPT_Tmax_Tmin_ok[,8])
names(PCPT_Tmax_Tmin_ok)<-c("codigo","year","month","day","PCPT","Tmax","Tmin")
#####################################################################


#####################################################################
## Save database for RClimDex
#####################################################################
pb <- winProgressBar(title="Hola, :-) .. espera solo un poco estoy creado los RClimDex txt ... ", label="0% done", min=0, max=100, initial=0, width = 900)
for(i in unique(PCPT_Tmax_Tmin_ok$codigo)){
    sub<-PCPT_Tmax_Tmin_ok[PCPT_Tmax_Tmin_ok$codigo==i,]
    sub<-data.frame(sub)
    sub$PCPT<-replace(sub$PCPT, is.na(sub$PCPT), -99.9)
    sub$Tmax<-replace(sub$Tmax, is.na(sub$Tmax), -99.9)
    sub$Tmin<-replace(sub$Tmin, is.na(sub$Tmin), -99.9)
    write.table(sub[,c(-1)],paste("Results/Estaciones/",i,"_RClimDex.txt"),sep="\t",dec=".", col.names = F, row.names = F)
    for (ii in 1:100){
        Sys.sleep(0.0005) # slow down the code for illustration purposes
        info <- sprintf("%d%% done", round((ii/100)*100))
        setWinProgressBar(pb, ii/(100)*100, label=info)
    }
}
close(pb)
#####################################################################


#####################################################################
## Database... PCPT_Tmax_Tmin_ok
#####################################################################
PCPT_Tmax_Tmin_ok$PCPT<-replace(PCPT_Tmax_Tmin_ok$PCPT, PCPT_Tmax_Tmin_ok$PCPT==-99.9, NA)
PCPT_Tmax_Tmin_ok$Tmax<-replace(PCPT_Tmax_Tmin_ok$Tmax, PCPT_Tmax_Tmin_ok$Tmax==-99.9, NA)
PCPT_Tmax_Tmin_ok$Tmin<-replace(PCPT_Tmax_Tmin_ok$Tmin, PCPT_Tmax_Tmin_ok$Tmin==-99.9, NA)
PCPT_Tmax_Tmin_ok$date<-as.Date(paste(PCPT_Tmax_Tmin_ok$year, PCPT_Tmax_Tmin_ok$month, PCPT_Tmax_Tmin_ok$day, sep="-"))
PCPT_Tmax_Tmin_ok$year_month<-paste(PCPT_Tmax_Tmin_ok$year, PCPT_Tmax_Tmin_ok$month, "1", sep="-")
library("plyr", lib.loc="~/R/win-library/3.4")
##########################
#---- (50% completa Temperatura mensual) ----#
#---- (80% completa Precipitación mensual) ----#
##########################
COMPLETA<-15      # (15/30)*100= 50% INFORMACIÓN COMPLETA TEMPERATURA MENSUAL
COMPLETA_PCPT<-24 # (24/30)*100= 80% INFORMACIÓN COMPLETA PRECIPITACIÓN MENSUAL
library(plyr)
str(PCPT_Tmax_Tmin_ok)
PCPT_Tmax_Tmin_ok$PCPT<-as.numeric(PCPT_Tmax_Tmin_ok$PCPT)
PCPT_Tmax_Tmin_ok$Tmax<-as.numeric(PCPT_Tmax_Tmin_ok$Tmax)
PCPT_Tmax_Tmin_ok$Tmin<-as.numeric(PCPT_Tmax_Tmin_ok$Tmin)

PCPT_Tmax_Tmin_sum<-ddply(PCPT_Tmax_Tmin_ok, c("codigo","year_month"), summarise, 
                PCPT_sum  = ifelse(length(na.omit(PCPT))>COMPLETA_PCPT, sum(na.omit(PCPT)),""),
                Tmax_max  = ifelse(length(na.omit(Tmax))>COMPLETA, max(na.omit(Tmax)),""),
                Tmax_mean = ifelse(length(na.omit(Tmax))>COMPLETA, mean(na.omit(Tmax)),""),
                Tmax_min = ifelse(length(na.omit(Tmax))>COMPLETA, min(na.omit(Tmax)),""),
                Tmin_max = ifelse(length(na.omit(Tmin))>COMPLETA, max(na.omit(Tmin)),""),
                Tmin_mean = ifelse(length(na.omit(Tmin))>COMPLETA, mean(na.omit(Tmin)),""),
                Tmin_min  = ifelse(length(na.omit(Tmin))>COMPLETA, min(na.omit(Tmin)),""))
PCPT_Tmax_Tmin_sum$year_month<-as.Date(PCPT_Tmax_Tmin_sum$year_month, format = "%Y-%m-%d")
##########################
#---- (80% completa Precipitación anual) ----#
##########################
COMPLETA<-292 #(292/365)*100= 80% INFORMACIÓN COMPLETA PRECIPITACION ANUAL
PCPT_anual<-ddply(PCPT_Tmax_Tmin_ok, c("codigo","year"), summarise, 
                  PCPT_sum= ifelse(length(na.omit(PCPT))>COMPLETA, sum(na.omit(PCPT)),""))
PCPT_anual$year<-as.numeric(PCPT_anual$year)
write.table(PCPT_Tmax_Tmin_sum,"Results/PCPT_Tmax_Tmin_sum.txt",sep="\t",dec=".", col.names = T, row.names = F)
write.table(PCPT_anual,"Results/PCPT_anual.txt",sep="\t",dec=".", col.names = T, row.names = F)
#####################################################################

```

Anexo 6.Creación de gráficos de precipitación y temperatura mensual
```{r eval=FALSE, include=TRUE}
## Smart graphics 
#####################################################################
####################################
Nombre_estacion<-"81003"   #########PCPT_anual PCPT_Tmax_Tmin_sum
####################################
PCPT_anual<-read.delim("Results/PCPT_anual.txt",header=TRUE,sep="\t",dec=".")
PCPT_anual$year<-as.numeric(PCPT_anual$year)
PCPT_anual$year<-as.Date(paste(PCPT_anual$year,"-01-01"), format = "%Y -%m-%d")
PCPT_Tmax_Tmin_sum<-read.delim("Results/PCPT_Tmax_Tmin_sum.txt",header=TRUE,sep="\t",dec=".")
PCPT_Tmax_Tmin_sum$year_month<-as.Date(PCPT_Tmax_Tmin_sum$year_month, format = "%Y-%m-%d")
#####################################################################
library(ggplot2)
library(xts) 
library(dygraphs)
# Function 
presAnnotation <- function(dygraph, x, text) {
    dygraph %>%
        dyAnnotation(x, text, attachAtBottom = TRUE, width = 40)
}
#####################################################################
## Temperatura escala mensual   ---- (50% completa) ----
#####################################################################
sub1<-PCPT_Tmax_Tmin_sum[PCPT_Tmax_Tmin_sum$codigo==Nombre_estacion,]
Tmax_max<- xts(sub1$Tmax_max,order.by=(sub1$year_month),tz="GMT")
Tmax_mean<- xts(sub1$Tmax_mean,order.by=(sub1$year_month),tz="GMT")
Tmax_min<- xts(sub1$Tmax_min,order.by=(sub1$year_month),tz="GMT")
Tmin_max<- xts(sub1$Tmin_max,order.by=(sub1$year_month),tz="GMT")
Tmin_mean<- xts(sub1$Tmin_mean,order.by=(sub1$year_month),tz="GMT")
Tmin_min<- xts(sub1$Tmin_min,order.by=(sub1$year_month),tz="GMT")
stocks1 <- cbind(Tmax_max,Tmax_mean,Tmax_min,Tmin_max,Tmin_mean,Tmin_min)
dygraph(stocks1,ylab=("Temperature (°C)"), main=paste("Estación: ", Nombre_estacion)) %>%
    dySeries("..1",label="Temp. max maximum (°C)", drawPoints = TRUE, pointSize = 3) %>%
    dySeries("..2",label="Temp. max promedio (°C)", drawPoints = TRUE, pointSize = 3) %>%
    dySeries("..3",label="Temp. max minimum (°C)", drawPoints = TRUE, pointSize = 3) %>%
    dySeries("..4",label="Temp. min maximum (°C)", drawPoints = TRUE, pointSize = 3) %>%
    dySeries("..5",label="Temp. min promedio (°C)", drawPoints = TRUE, pointSize = 3) %>%
    dySeries("..6",label="Temp. min minimum (°C)", drawPoints = TRUE, pointSize = 3) %>%
    dyOptions(colors = c("darkred","red","darksalmon","skyblue","orange","yellow")) %>%
    dyHighlight(highlightSeriesOpts = list(strokeWidth = 1))%>%
    dyLegend(width = 400)%>%
    dyRangeSelector()
#####################################################################
## Precipitación total mensual  ---- (80% completa) ----
#####################################################################
PCPT<- xts(sub1$PCPT_sum,order.by=(sub1$year_month),tz="GMT")
stocks1 <- cbind(PCPT)
dygraph(stocks1,ylab=("Precipitación total mensual (mm)"),main=paste("Estación: ", Nombre_estacion)) %>%
    dySeries(label="Precipitación total mensual (mm)", stepPlot = TRUE, fillGraph = TRUE, color = "blue") %>%
    dyOptions(colors = c("blue"), fillGraph = TRUE, fillAlpha = 0.4) %>%
    dyHighlight(highlightSeriesOpts = list(strokeWidth = 1))%>%
    dyLegend(width = 400)%>%
    dyRangeSelector()
#####################################################################
## Precipitación total anual    ---- (80% completa) ----
#####################################################################
sub2<-PCPT_anual[PCPT_anual$codigo==Nombre_estacion,]
PCPT<- xts(sub2$PCPT_sum,order.by=(sub2$year),tz="GMT")
stocks1 <- cbind(PCPT)
dygraph(stocks1,ylab=("Precipitación total anual (mm)"), main=paste("Estación: ", Nombre_estacion)) %>%
    dySeries(label="Precipitación total anual (mm)", stepPlot = TRUE, fillGraph = TRUE, color = "blue") %>%
    dyOptions(colors = c("blue"), fillGraph = TRUE, fillAlpha = 0.4) %>%
    dyHighlight(highlightSeriesOpts = list(strokeWidth = 1))%>%
    dyLegend(width = 400)%>%
    dyRangeSelector()
#####################################################################


#####################################################################
## Graphics PDF
#####################################################################
pdf(file="Results/Fig. Temperatura mensual.pdf", width = 10, height = 6)
par(mfrow=c(1,1),mgp = c(1.5,0.5,0), mar = c(3,3,4,1.5))
for (i in unique(PCPT_Tmax_Tmin_sum$codigo)){
    sub<-PCPT_Tmax_Tmin_sum[PCPT_Tmax_Tmin_sum$codigo==i,]
    sub<-sub[order(sub$codigo,sub$year_month),]
    sub$Tmax_max[!is.finite(sub$Tmax_max)]  <- NA
    sub$Tmax_mean[!is.finite(sub$Tmax_mean)] <- NA
    sub$Tmax_min[!is.finite(sub$Tmax_min)]  <- NA
    sub$Tmin_max[!is.finite(sub$Tmin_max)]  <- NA
    sub$Tmin_mean[!is.finite(sub$Tmin_mean)] <- NA
    sub$Tmin_min[!is.finite(sub$Tmin_min)]  <- NA
    par(xpd=FALSE)
    plot(sub$year_month, sub$Tmax_max, type = "b", pch=19, col="darkred", ylim=c(ifelse(min(na.omit(sub$Tmin_min))=="Inf",-1,min(na.omit(sub$Tmin_min))), ifelse(max(na.omit(sub$Tmax_max))=="-Inf",-1,max(na.omit(sub$Tmax_max)))), 
         main=paste("Estacion= ", i), ylab="Temperatura mensual (°C)", xlab="")
    points(sub$year_month, sub$Tmax_mean, type = "b", pch=19, col="red")
    points(sub$year_month, sub$Tmax_min, type = "b", pch=19, col="darksalmon")
    points(sub$year_month, sub$Tmin_max, type = "b", pch=19, col="skyblue")
    points(sub$year_month, sub$Tmin_mean, type = "b", pch=19, col="orange")
    points(sub$year_month, sub$Tmin_min, type = "b", pch=19, col="yellow")
    par(xpd=TRUE)
    legend("top", inset = c(0, -0.08), "", c("Tmax_max","Tmax_mean","Tmax_min","Tmin_max","Tmin_mean","Tmin_min"), pch=c(19),col=c("darkred","red","darksalmon","skyblue","orange","yellow"),h=TRUE, text.col = c("darkred","red","darksalmon","skyblue","orange","yellow"),merge = F, bg = NULL,bty='n')
}
dev.off()
#####################################################################
pdf(file="Results/Fig. Precipitacion total mensual.pdf", width = 10, height = 6)
par(mfrow=c(1,1),mgp = c(1.5,0.5,0), mar = c(3,3,4,1.5))
for (i in unique(PCPT_Tmax_Tmin_sum$codigo)){
    sub<-PCPT_Tmax_Tmin_sum[PCPT_Tmax_Tmin_sum$codigo==i,]
    sub<-sub[order(sub$codigo,sub$year_month),]
    sub$PCPT_sum[!is.finite(sub$PCPT_sum)]  <- NA
    sub$Tmax_max[!is.finite(sub$Tmax_max)]  <- NA
    sub$Tmax_mean[!is.finite(sub$Tmax_mean)] <- NA
    sub$Tmax_min[!is.finite(sub$Tmax_min)]  <- NA
    sub$Tmin_max[!is.finite(sub$Tmin_max)]  <- NA
    sub$Tmin_mean[!is.finite(sub$Tmin_mean)] <- NA
    sub$Tmin_min[!is.finite(sub$Tmin_min)]  <- NA
    par(xpd=FALSE)
    plot(sub$year_month, sub$PCPT_sum, type = "h", pch=19, col="blue", ylim=c(ifelse(min(na.omit(sub$PCPT_sum))=="Inf",0,0), ifelse(max(na.omit(sub$PCPT_sum))=="-Inf",0,max(na.omit(sub$PCPT_sum)))), 
         main=paste("Estacion= ", i), ylab="Precipitacion total mensual (mm)", xlab="", lwd=3)
    points(sub$year_month, sub$PCPT_sum, type="l", col="red", lty=3,lwd=1)
    abline(h=0, col="black", lty=1, lwd=2)
    par(xpd=TRUE)
    legend("top", inset = c(0, -0.08), "", c("Precipitación total mensual"),lty=1, lwd=3,col=c("blue"),h=TRUE, text.col = c("blue"),merge = F, bg = NULL,bty='n')
}
dev.off()
#####################################################################
pdf(file="Results/Fig. Precipitacion total anual.pdf", width = 10, height = 6)
par(mfrow=c(1,1),mgp = c(1.5,0.5,0), mar = c(3,3,4,1.5))
for (i in unique(PCPT_anual$codigo)){
    sub<-PCPT_anual[PCPT_anual$codigo==i,]
    sub<-sub[order(sub$codigo,sub$year),]
    sub$PCPT_sum[!is.finite(sub$PCPT_sum)]  <- NA
    par(xpd=FALSE)
    plot(sub$year, sub$PCPT_sum, type = "h", pch=19, col="blue", ylim=c(ifelse(min(na.omit(sub$PCPT_sum))=="Inf",0,0), ifelse(max(na.omit(sub$PCPT_sum))=="-Inf",0,max(na.omit(sub$PCPT_sum)))), 
         main=paste("Estacion= ", i), ylab="Precipitacion total anual (mm)", xlab="", lwd=3)
    points(sub$year, sub$PCPT_sum, type="l", col="red", lty=3,lwd=1)
    abline(h=0, col="black", lty=1, lwd=2)
    par(xpd=TRUE)
    legend("top", inset = c(0, -0.08), "", c("Precipitación total mensual"),lty=1, lwd=3,col=c("blue"),h=TRUE, text.col = c("blue"),merge = F, bg = NULL,bty='n')
}
dev.off()
#####################################################################


#####################################################################
## Save database summary (monthly and year) for each station
#####################################################################
pb <- winProgressBar(title="Hola de nuevo, :-) .. espera solo un poco estoy creado database para cada estación meteorológica en formato.txt ... ", label="0% done", min=0, max=100, initial=0, width = 900)
for(i in unique(PCPT_Tmax_Tmin_sum$codigo)){
    sub<-PCPT_Tmax_Tmin_sum[PCPT_Tmax_Tmin_sum$codigo==i,]
    sub<-sub[order(sub$codigo,sub$year_month),]
    sub$Tmax_max[!is.finite(sub$Tmax_max)]  <- NA
    sub$Tmax_mean[!is.finite(sub$Tmax_mean)] <- NA
    sub$Tmax_min[!is.finite(sub$Tmax_min)]  <- NA
    sub$Tmin_max[!is.finite(sub$Tmin_max)]  <- NA
    sub$Tmin_mean[!is.finite(sub$Tmin_mean)] <- NA
    sub$Tmin_min[!is.finite(sub$Tmin_min)]  <- NA
    write.table(sub,paste("Results/Estaciones datos resumidos/",i,"_database_mensual.txt"),sep="\t",dec=".", col.names = T, row.names = F)
    for (ii in 1:100){
        Sys.sleep(0.0005) # slow down the code for illustration purposes
        info <- sprintf("%d%% done", round((ii/100)*100))
        setWinProgressBar(pb, ii/(100)*100, label=info)
    }
}
close(pb)
#####################################################################
pb <- winProgressBar(title="Hola de nuevo, Soy yo otra vez :-) .. espera solo un poco estoy creado database para cada estación meteorológica en formato.txt ... ", label="0% done", min=0, max=100, initial=0, width = 900)
for(i in unique(PCPT_anual$codigo)){
    sub<-PCPT_anual[PCPT_anual$codigo==i,]
    sub<-sub[order(sub$codigo,sub$year),]
    sub$PCPT_sum[!is.finite(sub$PCPT_sum)]  <- NA
    write.table(sub,paste("Results/Estaciones datos resumidos/",i,"_database_anual.txt"),sep="\t",dec=".", col.names = T, row.names = F)
    for (ii in 1:100){
        Sys.sleep(0.0005) # slow down the code for illustration purposes
        info <- sprintf("%d%% done", round((ii/100)*100))
        setWinProgressBar(pb, ii/(100)*100, label=info)
    }
}
close(pb)
```

Anexo 7.Instalación de paquetes requeridos por RClimdex
```{r eval=FALSE, include=TRUE}
install.packages("cluster")
install.packages("tcltk") 
install.packages("Rcmdr")
```

Anexo 8.Código para generar el software RClimdex
```{r eval=FALSE, include=TRUE}
##################################################################################
#"Script RClimdex" 
# Rewritten: Junior Pastor PÉREZ-MOLINA
# Small modifications to adapt the analysis of climate variability in the tropics
# Date modified: "Tuesday May 31st, 2016" 
##################################################################################
rm(list = ls()) #Remove all objects
graphics.off()  #Remove all graphics
cat("\014")     #Remove script in windows console
##################################################################################


##################################################################################
# Credicts:
# Climate Indecies Calculation software
# R language with TCL/TK package
# Programmed by Yujun Ouyang,Mar,2004
# rewritten by Yang Feng, July 2004
##################################################################################
# version 1.0, 2004-10-14
# modified, 2006-01-24, 
# change .Internal(cbind) to cbind
# change .Internal(rbind) to rbind
# modified 2007-03-23
# change .Internal(rep(0,n)) to rep(0,n)
# modified, 2007-11-26,
# get rid of .Internal on some functions: min(...), sort(...), round(...)
#            max(...), also get rid of dig=.. part from sort(...) function
#            change decrease=... part to decreasing=... at sort(...)
# modified, 2008-05-05,
# output TMAX mean value and TMIN mean value in nastat() function
# modified, 2008-05-06,
# add a random series on TMAX and TMIN in exceedance rate function
# modified thresholds in TN10p calculation, add an 1e-5 item to avoid 
# computational error like 3.60000 > 3.6000, two functions involved:
# nordaytem1() and exceedance()
# modified, 2008-06-16
# change all sort() to mysort(), deal with different version, also combined
# different levels threshold()
##################################################################################


##################################################################################
# Part I
# General functions & TCL/TK functions
##################################################################################
library(cluster)
require(tcltk)
mysort<- if(getRversion()<='2.4.1') function(x,decreasing){.Internal(sort(x,decreasing=decreasing))} else function(x,decreasing){sort.int(x,decreasing=decreasing)}
fontHeading <- tkfont.create(family="times",size=40,weight="bold",slant="italic")
fontHeading1<-tkfont.create(family="times",size=20,weight="bold")
fontHeading2<-tkfont.create(family="times",size=14,weight="bold")
fontTextLabel <- tkfont.create(family="times",size=12)
fontFixedWidth <- tkfont.create(family="courier",size=12)
# initial value for check box
cbvalue1<-tclVar("1");  cbvalue2<-tclVar("1");  cbvalue3<-tclVar("1")
cbvalue4<-tclVar("1");  cbvalue5<-tclVar("1");  cbvalue6<-tclVar("1")
cbvalue7<-tclVar("1");  cbvalue8<-tclVar("1");  cbvalue9<-tclVar("1")
cbvalue10<-tclVar("1"); cbvalue11<-tclVar("1"); cbvalue12<-tclVar("1")
cbvalue13<-tclVar("1"); cbvalue14<-tclVar("1"); cbvalue15<-tclVar("1")
cbvalue16<-tclVar("0"); cbvalue17<-tclVar("0"); cbvalue18<-tclVar("0")
cbvalue19<-tclVar("0"); cbvalue21<-tclVar("1")
#  initial value for parameters
stations<-tclVar(paste(""));   stdt<-tclVar(paste("3"))
Entry1<-tclVar(paste("1961")); Entry2<-tclVar(paste("1990"))
#Entry3<-tclVar(paste("5"))
Entry4<-tclVar(paste("0"))
Entry5<-tclVar(paste("0"))
Entry6<-tclVar(paste("25"));   Entry7<-tclVar(paste("0"))
Entry8<-tclVar(paste("20"));   Entry9<-tclVar(paste("0"))
#Entry10<-tclVar(paste("10")); Entry11<-tclVar(paste("5"))
Entry12<-tclVar(paste("25"))
dayim<-as.integer(c(31,28,31,30,31,30,31,31,30,31,30,31))
crt<-3;      flag=F
treshold=5;  winsize=5
uu<-25;      lu<-20
ul<-0;       ll<-0
title1<-"Plot of Ind143";   title2<-"Ind143";   title3<-"Years"

#----------- frc -----------------------------------------
frc<-function(dd,year,month,item){
    
    a<-dd[dd$year==year & dd$month==month,item]
    a<-a[a>-99]
    frc<-length(a)/rdim(year,month)
}#end
#----------- frc ends -----------------------------------------

#----------- done -----------------------------------------
done<-function(){tkdestroy(start1)}
#----------- done ends -----------------------------------------

#----------- percentile -----------------------------------------
percentile<-function(n,x,pctile){
    x1<-x[is.na(x)==F]
    n1<-length(x1)
    a<-mysort(x1,decreasing=F)
    b<-n1*pctile+0.3333*pctile+0.3333
    bb<-trunc(b)
    percentile<-a[bb]+(b-bb)*(a[bb+1]-a[bb]) 
}#end
#----------- percentile ends -----------------------------------------

#----------- pplotts -----------------------------------------
pplotts<-function(var="prcp",type="h",tit=NULL){
    if(var=="dtr"){
        ymax<-max(dd[,"tmax"]-dd[,"tmin"],na.rm=T)
        ymin<-0
    }
    else if(var=="prcp"){
        ymax<-max(dd[,var],na.rm=T)
        ymin<-0
    }
    else{
        ymax<-max(dd[,var],na.rm=T)+1
        ymin<-min(dd[,var],na.rm=T)-1
    }
    if(is.na(ymax)|is.na(ymin)|(ymax==-Inf)|(ymin==-Inf)){
        ymax<-100
        ymin<-(-100)
    }
    par(mfrow=c(4,1))
    par(mar=c(3.1,2.1,2.1,2.1))
    for(i in seq(years,yeare,10)){
        at<-rep(1,10)
        #   if(i>yeare)
        for(j in (i+1):min(i+9,yeare+1)){
            if(leapyear(j)) at[j-i+1]<-at[j-i]+366
            else at[j-i+1]<-at[j-i]+365
        }
        if(var=="dtr")
            ttmp<-dd[dd$year>=i&dd$year<=min(i+9,yeare),"tmax"]-dd[dd$year>=i&dd$year<=min(i+9,yeare),"tmin"]
        else ttmp<-dd[dd$year>=i&dd$year<=min(i+9,yeare),var]
        plot(1:length(ttmp),ttmp,type=type,col="blue",xlab="",ylab="",xaxt="n",xlim=c(1,3660),ylim=c(ymin,ymax))
        abline(h=0)
        tt<-seq(1,length(ttmp))
        tt<-tt[is.na(ttmp)==T]
        axis(side=1,at=at,labels=c(i:(i+9)))
        for(k in 1:10) abline(v=at[k],col="yellow")
        lines(tt,rep(0,length(tt)),type="p",col="red")
        title(paste("Station: ",tit,", ",i,"~",min(i+9,yeare),",  ",var,sep=""))
    }
}
#----------- pplotts ends -----------------------------------------

#----------- ind143gsl -----------------------------------------
ind143gsl<-function(){
    if (latitude<0) south=T else south=F
    if (latitude<0) eyear=yeare-1 else eyear=yeare
    threshold<-5
    a<-eyear-years+1
    b<-rep(0,a)
    b<-cbind(b,b)
    colnames(b)<-c("year","gsl")
    i=1
    for (year in years:eyear) {
        b[i,"year"]<-year
        if(south){
            gslstart<-dd[dd$year==year&dd$month>6,]
            gslstart<-(gslstart[,"tmax"]+gslstart[,"tmin"])/2
            gslend<-dd[dd$year==(year+1)&dd$month<7,]
            gslend<-(gslend[,"tmax"]+gslend[,"tmin"])/2
        }
        else{
            gslstart<-dd[dd$year==year&dd$month<7,]
            gslstart<-(gslstart[,"tmax"]+gslstart[,"tmin"])/2
            gslend<-dd[dd$year==year&dd$month>6,]
            gslend<-(gslend[,"tmax"]+gslend[,"tmin"])/2
        }
        beginday<-0
        count=0
        for(step in 1:length(gslstart)){
            if(is.na(gslstart[step])==F){
                if(gslstart[step]>threshold) count<-count+1
                else count<-0
            }
            else count<-0
            if(count>5){
                beginday<-step-5
                break
            }
        }
        
        #    if(beginday==0){
        #      b[i,"gsl"]<-0
        #      break
        #    }
        
        endday<-0
        count<-0
        for(step in 1:length(gslend)){
            if(is.na(gslend[step])==F){
                if(gslend[step]<threshold) count<-count+1
                else count<-0
            }
            else count<-0
            if(count>5){
                endday<-step-5
                break
            }
        }
        
        if(sum(is.na(gslstart))+sum(is.na(gslend))>15)  b[i,"gsl"]<-NA
        else{
            if(beginday==0)
                b[i,"gsl"]<-0
            else {
                if(endday==0)  b[i,"gsl"]<-length(gslend)+length(gslstart)-beginday
                else b[i,"gsl"]<-endday+length(gslstart)-beginday
            }
        }
        
        i=i+1
    } 
    b<-as.data.frame(b)
    nam1<-paste(outinddir,paste(ofilename,"_GSL.csv",sep=""),sep="/")
    write.table(b,file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
    
    namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
    if(sum(is.na(b[,"gsl"]))>=(yeare-years+1-10)){
        betahat<-NA
        betastd<-NA
        pvalue<-NA
    }
    else{
        fit1<-lsfit(b[,1],b[,"gsl"])
        out1<-ls.print(fit1,print.it=F)
        pvalue<-round(as.numeric(out1$summary[1,6]),3)
        betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
        betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
    }
    cat(file=namt,paste(latitude,longitude,"gsl",years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
    
    nam2<-paste(outjpgdir,paste(ofilename,"_GSL.jpg",sep=""),sep="/")
    jpeg(file=nam2,width=1024,height=768)
    plotx(b[,1],b[,"gsl"],main=paste("GSL",ofilename,sep="   "),xlab="Year",ylab="GSL")
    dev.off()
}
#----------- ind143gsl ends -----------------------------------------

#----------- dataext -----------------------------------------
# seems this function is never used...
dataext<-function(dd,year,month,day,item){
    Dataext<-dd[dd$year==year & dd$month==month & dd$day==day,item]
}
#----------- dataext ends -----------------------------------------

# ----------- leapyear -----------------------------------------
# check if 'year' is a leap year
# returns T or F
leapyear<-function(year){
    remainder400 <-trunc(year-400*trunc(year/400))
    remainder100 <-trunc(year-100*trunc(year/100))
    remainder4 <-trunc(year-4*trunc(year/4))
    if (remainder400 == 0) leapyear = T
    else{
        if(remainder100 == 0) leapyear = F
        else{
            if(remainder4 == 0) leapyear = T
            else leapyear = F
        }
    }
}
# ----------- leapyear ends -----------------------------------------

# ----------- rdim -----------------------------------------
# day # in a month
#
rdim<-function(year,month) {
    a<-leapyear(year) 
    if (month==1) rdim<-31
    else if (month==3) rdim<-31
    else if (month==4) rdim<-30
    else if (month==5) rdim<-31
    else if (month==6) rdim<-30
    else if (month==7) rdim<-31
    else if (month==8) rdim<-31
    else if (month==9) rdim<-30
    else if (month==10) rdim<-31
    else if (month==11) rdim<-30
    else if (month==12) rdim<-31
    else if (a==T & month==2) rdim<-29
    else rdim<-28  
}
# ----------- rdim ends -----------------------------------------

# ----------- qcontrol -----------------------------------------
qcontrol<-function(){
    tkmessageBox(message=paste("Data QC(",ofilename,") may take a few minutes, click OK to continue.",sep=""))
    # output records of problematic like prcp <0 and NA
    ddu<-duplicated(dd[,c("year","month","day")])
    if(sum(ddu)>0){
        nam1<-paste(outlogdir,paste(ofilename,"dupliQC.csv",sep=""),sep="/")
        msg=paste("Date duplicated found in original data file, please check:",nam1,sep=" ")
        tkmessageBox(message=msg)
        ddu2<-dd[duplicated(dd[,c("year","month","day")])==T,c("year", "month", "day")]
        nam1<-paste(outlogdir,paste(ofilename,"dupliQC.csv",sep=""),sep="/")
        write.table(ddu2,file=nam1,append=F,quote=F,sep=", ",row.names=F)
        tkdestroy(start1)
        stop(paste("QC stopped due to duplicated date, please check ",nam1,sep=""))
    }
    
    mid<-dd[is.na(dd$prcp)==F,]  # choose non-MISSING PRCP
    mid<-mid[mid$prcp<0,]        # Is there unreasonable PRCP?
    #dd[is.na(dd$prcp)==F & dd$prcp<0,"prcp"]<-NA
    nam1<-paste(outlogdir,paste(ofilename,"_prcpQC.csv",sep=""),sep="/")
    write.table(mid,file=nam1,append=F,quote=F,sep=", ",row.names=F)
    if (dim(mid)[1]>0) tkmessageBox(message=paste("Errors in prcp, please check the log file",nam1,sep=" "))
    # output plots for PRCP
    nam1<-paste(outlogdir,paste(ofilename,"_prcpPLOT.pdf",sep=""),sep="/")
    pdf(file=nam1)
    ttmp<-dd[dd$prcp>=1,"prcp"]
    ttmp<-ttmp[is.na(ttmp)==F]
    if(length(ttmp)>30){
        hist(ttmp,main=paste("Histogram for Station:",ofilename," of PRCP>=1mm",sep=""),breaks=c(seq(0,20,2),max(30,ttmp)),xlab="",col="green",freq=F)
        lines(density(ttmp,bw=0.2,from=1),col="red")
    }
    pplotts(var="prcp",tit=ofilename)
    dev.off()
    nam1<-paste(outlogdir,paste(ofilename,"_tmaxPLOT.pdf",sep=""),sep="/")
    pdf(file=nam1)
    pplotts(var="tmax",type="l",tit=ofilename)
    dev.off()
    nam1<-paste(outlogdir,paste(ofilename,"_tminPLOT.pdf",sep=""),sep="/")
    pdf(file=nam1)
    pplotts(var="tmin",type="l",tit=ofilename)
    dev.off()
    nam1<-paste(outlogdir,paste(ofilename,"_dtrPLOT.pdf",sep=""),sep="/")
    pdf(file=nam1)
    pplotts(var="dtr",type="l",tit=ofilename)
    dev.off()
    
    #par(mfrow=c(1,1))
    # output problematic temperature like tmax < tmin
    mm<-dd[,"tmax"]-dd[,"tmin"]
    dd<-cbind(dd,mm)
    dimnames(dd)[[2]][7]<-"dtr"
    #output "log" file review
    temiss<-dd
    temiss<-temiss[is.na(temiss[,"tmax"])==F&is.na(temiss[,"tmin"])==F,]
    #  temiss<-temiss[is.na(temiss[,6])==F,]
    temiss<-temiss[temiss[,7]<=0|temiss[,5]<=(-70)|temiss[,5]>=70|temiss[,6]<=(-70)|temiss[,6]>=70,]
    dimnames(temiss)[[2]][7]<-"tmax-tmin"
    nam1<-paste(outlogdir,paste(ofilename,"_tempQC.csv",sep=""),sep="/")
    write.table(temiss,file=nam1,append=F,quote=F,sep=", ",row.names=F)
    if (dim(temiss)[1]>0) {
        tkmessageBox(message=paste("Errors in temperature, please check the log file",nam1,sep=" "))
        # records with abs(tmax)>=70, abs(tmin)>=70 set to NA
        dd[is.na(dd[,5])==F & abs(dd[,5])>=70,5]<-NA     # This is different from Fclimdex code  !!!!!!!!!!!!!!!!!!!!!!!!!!!!
        dd[is.na(dd[,6])==F & abs(dd[,6])>=70,6]<-NA
        # records with tmax < tmin are set to NA
        dd[is.na(dd[,5])==F & is.na(dd[,6])==F & dd[,"dtr"]<0,c("tmax","tmin")]<-NA
        #   dd[is.na(dd[,5])==F & dd[,"mm"]<0,"tmin"]<-NA
    }
    #  dd<-dd[,-7]
    
    # output problematic temperature which is out of 3 standard diviation (temp only)
    ys<-yeare-years+1
    
    tmaxm<-matrix(0,ys,365)
    tminm<-matrix(0,ys,365)
    tdtrm<-matrix(0,ys,365)
    
    tmaxstd<-rep(0,365)
    tminstd<-rep(0,365)
    tdtrstd<-rep(0,365)
    
    tmaxmean<-rep(0,365)
    tminmean<-rep(0,365)
    tdtrmean<-rep(0,365)
    
    for(i in 1:ys)
        tmaxm[i,]<-dd[dd[,"year"]==(i+years-1)&(dd[,"month"]*100+dd[,"day"]!=229),"tmax"]
    for(i in 1:365){
        tmaxstd[i]<-sqrt(var(tmaxm[,i],na.rm=T))
        tmaxmean[i]<-mean(tmaxm[,i],na.rm=T)
    }
    
    for(i in 1:ys)
        tminm[i,]<-dd[dd[,"year"]==(i+years-1)&(dd[,"month"]*100+dd[,"day"]!=229),"tmin"]
    for(i in 1:365){
        tminstd[i]<-sqrt(var(tminm[,i],na.rm=T))
        tminmean[i]<-mean(tminm[,i],na.rm=T)
    }
    
    for(i in 1:ys)
        tdtrm[i,]<-dd[dd[,"year"]==(i+years-1)&(dd[,"month"]*100+dd[,"day"]!=229),"dtr"]
    for(i in 1:365){
        tdtrstd[i]<-sqrt(var(tdtrm[,i],na.rm=T))
        tdtrmean[i]<-mean(tdtrm[,i],na.rm=T)
    }
    
    tmaxstdleap<-rep(0,366)
    tmaxstdleap[1:59]<-tmaxstd[1:59]
    tmaxstdleap[60]<-tmaxstd[59]
    tmaxstdleap[61:366]<-tmaxstd[60:365]
    
    tmaxmeanleap<-rep(0,366)
    tmaxmeanleap[1:59]<-tmaxmean[1:59]
    tmaxmeanleap[60]<-tmaxmean[59]
    tmaxmeanleap[61:366]<-tmaxmean[60:365]
    
    tminstdleap<-rep(0,366)
    tminstdleap[1:59]<-tminstd[1:59]
    tminstdleap[60]<-tminstd[59]
    tminstdleap[61:366]<-tminstd[60:365]
    
    tminmeanleap<-rep(0,366)
    tminmeanleap[1:59]<-tminmean[1:59]
    tminmeanleap[60]<-tminmean[59]
    tminmeanleap[61:366]<-tminmean[60:365]
    
    tdtrstdleap<-rep(0,366)
    tdtrstdleap[1:59]<-tdtrstd[1:59]
    tdtrstdleap[60]<-tdtrstd[59]
    tdtrstdleap[61:366]<-tdtrstd[60:365]
    
    tdtrmeanleap<-rep(0,366)
    tdtrmeanleap[1:59]<-tdtrmean[1:59]
    tdtrmeanleap[60]<-tdtrmean[59]
    tdtrmeanleap[61:366]<-tdtrmean[60:365]
    
    tmp<-matrix(0,dim(dd)[1],6)
    dimnames(tmp)<-list(NULL,c("tmaxlow","tmaxup","tminlow","tminup","dtrlow","dtrup"))
    
    idx<-0
    for(i in years:yeare){
        if(leapyear(i)==T){
            tmp[(idx+1):(idx+366),1]<-tmaxmeanleap-crt*tmaxstdleap
            tmp[(idx+1):(idx+366),2]<-tmaxmeanleap+crt*tmaxstdleap
            tmp[(idx+1):(idx+366),3]<-tminmeanleap-crt*tminstdleap
            tmp[(idx+1):(idx+366),4]<-tminmeanleap+crt*tminstdleap
            tmp[(idx+1):(idx+366),5]<-tdtrmeanleap-crt*tdtrstdleap
            tmp[(idx+1):(idx+366),6]<-tdtrmeanleap+crt*tdtrstdleap
            idx<-idx+366
        }
        else{
            tmp[(idx+1):(idx+365),1]<-tmaxmean-crt*tmaxstd
            tmp[(idx+1):(idx+365),2]<-tmaxmean+crt*tmaxstd
            tmp[(idx+1):(idx+365),3]<-tminmean-crt*tminstd
            tmp[(idx+1):(idx+365),4]<-tminmean+crt*tminstd
            tmp[(idx+1):(idx+365),5]<-tdtrmean-crt*tdtrstd
            tmp[(idx+1):(idx+365),6]<-tdtrmean+crt*tdtrstd
            idx<-idx+365
        }
    }
    
    odata<-cbind(dd,tmp)
    
    odata<-odata[is.na(odata[,"tmax"])==F,]
    odata<-odata[is.na(odata[,"tmin"])==F,]
    odata<-odata[is.na(odata[,"dtr"])==F,]
    o1data<-odata[odata[,5]<odata[,8]|odata[,5]>odata[,9]|odata[,6]<odata[,10]|odata[,6]>odata[,11]|odata[,7]<odata[,12]|odata[,7]>odata[,13],]
    #o2data<-odata[odata[,5]>odata[,8],]
    #o3data<-odata[odata[,6]<odata[,9],]
    #o4data<-odata[odata[,6]>odata[,10],]
    
    #write.table(errstdo,file=nam1,append=F,quote=F,sep=",",row.names=F)
    if (dim(o1data)[1] > 0){
        nam1<-paste(outlogdir,paste(ofilename,"_tepstdQC.csv",sep=""),sep="/")
        tkmessageBox(message=paste("Outliers found, please check the log file: ",nam1,sep=""))
        ofile<-cbind(o1data[,c(1,2,3,8,5,9,10,6,11,12,7,13)])
        write.table(round(ofile,digit=2),file=nam1,append=F,quote=F,sep=",",row.names=F)
    }
    
    dd<-dd[,c("year","month","day","prcp","tmax","tmin")];  assign("dd",dd,envir=.GlobalEnv)
    
    namcal<-paste(nama,"indcal.csv",sep="")
    assign("namcal",namcal,envir=.GlobalEnv)
    write.table(dd,file=namcal,append=F,quote=F,sep=",",row.names=F,na="-99.9")
    
    tkmessageBox(message=paste("If you have checked data(", namcal,"), click OK to continue.",sep=""))
}
# ------------ qcontrol ends --------------------------------

# ---------------------------------------------------------
# NASTST
nastat<-function(){
    dd <- read.table(namcal,header=T,sep=",",na.strings="-99.9",colClasses=rep("real",6))
    assign("dd",dd,envir=.GlobalEnv)
    # NA statistics
    nast<-rep(0,12)
    nast<-array(nast,c(1,12,12,(yeare-years+1)))
    dimnames(nast)<-list(NULL,c("ynapr","ynatma","ynatmi","napr","natma","natmi","mnapr>3","mnatma>3","mnatmi>3","ynapr>15","ynatma>15","ynatmi>15"),NULL,NULL)
    ys<-yeare-years+1                    
    year=years
    aa1<-matrix(NA,12*ys,4)   # monthly
    dimnames(aa1)<-list(NULL,c("year","month","tmaxm","tminm"))
    aa1[,"year"]<-years:yeare
    aa1[,"year"]<-mysort(aa1[,"year"],decreasing=F)    # Is this step necessary???
    aa1[,"month"]<-1:12
    aa2<-matrix(NA,ys,3)      # annual
    dimnames(aa2)<-list(NULL,c("year","tmaxm","tminm"))
    aa2[,"year"]<-years:yeare
    for (i in 1:(yeare-years+1)){
        month<-1;     midvalue1<-dd[dd$year==year,]   # data for each year
        aa2[i,"tmaxm"]<-mean(midvalue1[,"tmax"],na.rm=T)  # annual mean of Tmax, Tmin
        aa2[i,"tminm"]<-mean(midvalue1[,"tmin"],na.rm=T)
        for (j in 1:12){
            midvalue2<-midvalue1[midvalue1$month==month,]  # data for each month
            aa1[(i-1)*12+j,"tmaxm"]<-mean(midvalue2[,"tmax"],na.rm=T)  # monthly mean of Tmax, Tmin
            aa1[(i-1)*12+j,"tminm"]<-mean(midvalue2[,"tmin"],na.rm=T)
            nast[1,"ynapr",j,i]<-dim(midvalue1[is.na(midvalue1$prcp),])[1]  # annual missing days
            if (nast[1,"ynapr",j,i]>15) nast[1,"ynapr>15",j,i]<-NA
            nast[1,"ynatma",j,i]<-dim(midvalue1[is.na(midvalue1$tmax),])[1]
            if (nast[1,"ynatma",j,i]>15) nast[1,"ynatma>15",j,i]<-NA
            nast[1,"ynatmi",j,i]<-dim(midvalue1[is.na(midvalue1$tmin),])[1]
            if (nast[1,"ynatmi",j,i]>15) nast[1,"ynatmi>15",j,i]<-NA
            nast[1,"napr",j,i]<-dim(midvalue2[is.na(midvalue2$prcp),])[1]  # monthly missing days
            if (nast[1,"napr",j,i]>3) nast[1,"mnapr>3",j,i]<-NA
            nast[1,"natma",j,i]<-dim(midvalue2[is.na(midvalue2$tmax),])[1]
            if (nast[1,"natma",j,i]>3) nast[1,"mnatma>3",j,i]<-NA
            nast[1,"natmi",j,i]<-dim(midvalue2[is.na(midvalue2$tmin),])[1]
            if (nast[1,"natmi",j,i]>3) nast[1,"mnatmi>3",j,i]<-NA
            month=month+1 
        }
        year=year+1      
    }
    nasto<-t(nast[,,,1])
    for ( i in 2:(yeare-years+1)){
        nasto<-rbind(nasto,t(nast[,,,i])) }
    nastout<-matrix(0,(yeare-years+1)*12,2)
    dimnames(nastout)<-list(NULL,c("year","month"))                
    nastout<-as.data.frame(nastout)
    nastout[,"year"]<-years:yeare
    nastout[,"year"]<-mysort(nastout[,"year"],decreasing=F)
    nastout[,"month"]<-1:12
    
    nastout<-cbind(nastout,nasto);    assign("nastout",nastout,envir=.GlobalEnv)
    nastatistic<-nastout[,1:8]
    
    nacor<-nastout[,-(3:8)]
    ynacor<-matrix(0,ys,4)
    dimnames(ynacor)<-list(NULL,c("year","ynapr>15","ynatma>15","ynatmi>15"))
    ynacor[,"year"]<-years:yeare
    ynacor<-as.data.frame(ynacor)
    for (year in years:yeare){
        ynacor[ynacor$year==year,"ynapr>15"]<-nacor[nacor$year==year & nacor$month==1,"ynapr>15"]
        ynacor[ynacor$year==year,"ynatma>15"]<-nacor[nacor$year==year & nacor$month==1,"ynatma>15"]
        ynacor[ynacor$year==year,"ynatmi>15"]<-nacor[nacor$year==year & nacor$month==1,"ynatmi>15"]
    }
    nacor<-nacor[,1:5]
    assign("nacor",nacor,envir=.GlobalEnv)
    assign("ynacor",ynacor,envir=.GlobalEnv)
    if(sum(is.na(ynacor[,"ynapr>15"])==F)==0) prallna<-1
    else prallna<-0
    if(sum(is.na(ynacor[,"ynatma>15"])==F)==0) txallna<-1
    else txallna<-0
    if(sum(is.na(ynacor[,"ynatmi>15"])==F)==0) tnallna<-1
    else tnallna<-0
    assign("prallna",prallna,envir=.GlobalEnv)
    assign("txallna",txallna,envir=.GlobalEnv)
    assign("tnallna",tnallna,envir=.GlobalEnv)
    assign("nastatistic",nastatistic,envir=.GlobalEnv)
    nam1<-paste(outlogdir,paste(ofilename,"_nastatistic.csv",sep=""),sep="/")           # Output the result
    cat(file=nam1,"TITLE,YEAR,JAN,FEB,MAR,APR,MAY,JUN,JUL,AUG,SEP,OCT,NOV,DEC,ANN\n")
    for(year in years:yeare)
        for(i in 1:3) {
            if(i==1) tit<-"PRCP"
            if(i==2) tit<-"TMAX"
            if(i==3) tit<-"TMIN"
            line<-paste(tit,year,sep=",")
            for(mon in 1:12)
                line<-paste(line,nastatistic[nastatistic$year==year&nastatistic$month==mon,i+5],sep=",")
            line<-paste(line,nastatistic[nastatistic$year==year&nastatistic$month==1,i+2],sep=",")
            cat(file=nam1,line,fill=100,append=T)
        }
    #   write.table(nastatistic,file=nam1,append=F,quote=F,sep=", ",row.names=F)
    aa1[,"tmaxm"]<-aa1[,"tmaxm"]+nacor[,"mnatma>3"]
    aa1[,"tminm"]<-aa1[,"tminm"]+nacor[,"mnatmi>3"]
    aa2[,"tmaxm"]<-aa2[,"tmaxm"]+ynacor[,"ynatma>15"]
    aa2[,"tminm"]<-aa2[,"tminm"]+ynacor[,"ynatmi>15"]
    ofile1<-paste(outinddir,paste(ofilename,"_TMAXmean.csv",sep=""),sep="/")
    odata<-matrix(0,ys,14)
    dimnames(odata)<-list(NULL,c("year","jan","feb","mar","apr","may","jun","jul","aug","sep","oct","nov","dec","annual"))
    odata[,1]<-years:yeare
    odata[,14]<-aa2[,"tmaxm"]
    for(i in 1:ys) odata[i,2:13]<-aa1[((i-1)*12+1):(i*12),"tmaxm"]
    write.table(round(odata,2),file=ofile1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
    odata[,14]<-aa2[,"tminm"]
    for(i in 1:ys) odata[i,2:13]<-aa1[((i-1)*12+1):(i*12),"tminm"]
    ofile1<-paste(outinddir,paste(ofilename,"_TMINmean.csv",sep=""),sep="/")
    write.table(round(odata,2),file=ofile1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
    parameter()
}
# -------------- nastat ends ------------------------------------------

# -----------------------------------------------------------------
# getfile
# first step is to open a data file, and read them into "dd".
#
getfile<-function() {
    name <- tclvalue(tkgetOpenFile(filetypes="{{TEXT Files} {.txt}} {{All files} *}"))
    if (name=="") return();
    dd <- read.table(name,header=F,col.names=c("year","month","day","prcp","tmax","tmin"),colClasses=rep("real",6))
    nama<-substr(name,start=1,stop=(nchar(name)-4))
    outdirtmp<-strsplit(name,"/")[[1]]
    if(length(outdirtmp)<=2){  # for Windiws
        outinddir<-paste(strsplit(name,":")[[1]][1],"indices",sep=":/")
        outlogdir<-paste(strsplit(name,":")[[1]][1],"log",sep=":/")
        outjpgdir<-paste(strsplit(name,":")[[1]][1],"plots",sep=":/")
        outtrddir<-paste(strsplit(name,":")[[1]][1],"trend",sep=":/")
    }
    else{                   # Unix/Linux
        outdir<-outdirtmp[1]
        for(i in 2:(length(outdirtmp)-1))
            outdir<-paste(outdir,outdirtmp[i],sep="/")
        outinddir<-paste(outdir,"indices",sep="/")
        outlogdir<-paste(outdir,"log",sep="/")
        outjpgdir<-paste(outdir,"plots",sep="/")
        outtrddir<-paste(outdir,"trend",sep="/")
    }
    ofilename<-substr(outdirtmp[length(outdirtmp)],start=1,stop=(nchar(outdirtmp[length(outdirtmp)])-4))
    # create output folders
    if(!file.exists(outinddir)) dir.create(outinddir)
    if(!file.exists(outlogdir)) dir.create(outlogdir)
    if(!file.exists(outjpgdir)) dir.create(outjpgdir)
    if(!file.exists(outtrddir)) dir.create(outtrddir)
    
    #     dimnames(dd)<-list(NULL,c("year","month","day","prcp","tmax","tmin"))
    assign("nama",nama,envir=.GlobalEnv)
    assign("outinddir",outinddir,envir=.GlobalEnv)
    assign("outlogdir",outlogdir,envir=.GlobalEnv)
    assign("outjpgdir",outjpgdir,envir=.GlobalEnv)
    assign("outtrddir",outtrddir,envir=.GlobalEnv)
    assign("ofilename",ofilename,envir=.GlobalEnv)
    # dd<-dd[dd$tmax!=-99.9,]
    # dd$year<-dd$year+40 # just for the test data
    # replace missing value with NA
    dd[dd$prcp<=(-99.),"prcp"]<-NA
    dd[dd$tmax<=(-99.),"tmax"]<-NA
    dd[dd$tmin<=(-99.),"tmin"]<-NA
    # replace missing records
    ddd<-matrix(NA,365,6)
    dddl<-matrix(NA,366,6)
    dimnames(ddd)<-list(NULL,c("year","month","day","prcp","tmax","tmin"))
    dimnames(dddl)<-list(NULL,c("year","month","day","prcp","tmax","tmin"))
    ddd[1:31,"month"]<-1;     ddd[1:31,"day"]<-c(1:31);    ddd[32:59,"month"]<-2;    ddd[32:59,"day"]<-c(1:28)
    ddd[60:90,"month"]<-3;    ddd[60:90,"day"]<-c(1:31);   ddd[91:120,"month"]<-4;   ddd[91:120,"day"]<-c(1:30)
    ddd[121:151,"month"]<-5;  ddd[121:151,"day"]<-c(1:31); ddd[152:181,"month"]<-6;  ddd[152:181,"day"]<-c(1:30)
    ddd[182:212,"month"]<-7;  ddd[182:212,"day"]<-c(1:31); ddd[213:243,"month"]<-8;  ddd[213:243,"day"]<-c(1:31)
    ddd[244:273,"month"]<-9;  ddd[244:273,"day"]<-c(1:30); ddd[274:304,"month"]<-10; ddd[274:304,"day"]<-c(1:31)
    ddd[305:334,"month"]<-11; ddd[305:334,"day"]<-c(1:30); ddd[335:365,"month"]<-12; ddd[335:365,"day"]<-c(1:31)
    
    dddl[1:31,"month"]<-1;     dddl[1:31,"day"]<-c(1:31);    dddl[32:60,"month"]<-2;    dddl[32:60,"day"]<-c(1:29)
    dddl[61:91,"month"]<-3;    dddl[61:91,"day"]<-c(1:31);   dddl[92:121,"month"]<-4;   dddl[92:121,"day"]<-c(1:30)
    dddl[122:152,"month"]<-5;  dddl[122:152,"day"]<-c(1:31); dddl[153:182,"month"]<-6;  dddl[153:182,"day"]<-c(1:30)
    dddl[183:213,"month"]<-7;  dddl[183:213,"day"]<-c(1:31); dddl[214:244,"month"]<-8;  dddl[214:244,"day"]<-c(1:31)
    dddl[245:274,"month"]<-9;  dddl[245:274,"day"]<-c(1:30); dddl[275:305,"month"]<-10; dddl[275:305,"day"]<-c(1:31)
    dddl[306:335,"month"]<-11; dddl[306:335,"day"]<-c(1:30); dddl[336:366,"month"]<-12; dddl[336:366,"day"]<-c(1:31)
    
    years<-dd[1,1];yeare<-dd[dim(dd)[1],1]   # star and end years
    if (leapyear(years)) dddd<-dddl else dddd<-ddd
    dddd[,"year"]<-years
    for (year in years:yeare){                  # year loop start
        if (leapyear(year)) dddd1<-dddl else dddd1<-ddd
        dddd1[,"year"]<-year
        if (year!=years) dddd<-rbind(dddd,dddd1)
    }                                       # year loop end
    
    dddd<-as.data.frame(dddd)
    dddd2<-merge(dddd,dd,by=c("year","month","day"),all.x=T)
    dddd2<-dddd2[,-(4:6)]
    dimnames(dddd2)[[2]]<-c("year","month","day","prcp","tmax","tmin")
    tmporder<-dddd2[,"year"]*10000+dddd2[,"month"]*100+dddd2[,"day"]
    dd<-dddd2[order(tmporder),]
    
    assign("years",years,envir=.GlobalEnv)
    assign("yeare",yeare,envir=.GlobalEnv)
    assign("dd",dd,envir=.GlobalEnv)
    
    tkmessageBox(message=paste("Data(",ofilename,") loaded, click OK to continue.",sep=""))
    
    # enter station name and the times of stadard deviation
    infor1<-tktoplevel()
    tkfocus(infor1)
    tkgrab.set(infor1)
    tkwm.title(infor1,"Set Parameters for Data QC")
    
    textEntry1<-stations;        textEntry2<-stdt
    
    textEntryWidget1<-tkentry(infor1,width=30,textvariable=textEntry1)
    textEntryWidget2<-tkentry(infor1,width=30,textvariable=textEntry2)
    
    #     tkgrid(tklabel(infor1,text="!!Enter parameters please",font=fontHeading1))
    tkgrid(tklabel(infor1,text="                  Station name or code:"),textEntryWidget1)
    tkgrid(tklabel(infor1,text="Criteria(number of Standard Deviation):"),textEntryWidget2)
    
    ok1<-function(){
        station<-as.character(tclvalue(textEntry1));    assign("station",station,envir=.GlobalEnv)
        crt<-as.numeric(tclvalue(textEntry2));          assign("crt",crt,envir=.GlobalEnv)
        tkgrab.release(infor1)
        tkdestroy(infor1)
        stations<-textEntry1;      assign("stations",stations,envir=.GlobalEnv)
        stdt<-textEntry2;          assign("stdt",stdt,envir=.GlobalEnv)
        qcontrol();                tkfocus(start1)
    }# end of ok
    
    cancel1<-function(){
        tkmessageBox(message="You have to enter these parameters before you can move on.")
        tkfocus(infor1)}# end of cancel1
    
    ok1.but<-    tkbutton(infor1,text="    OK    ",command=ok1)
    cancel1.but<-tkbutton(infor1,text="  CANCEL  ",command=cancel1)
    tkgrid(ok1.but,cancel1.but)
    
}
# end of getfile
#------------------------------------------------------------

# -----------------------------------------------------------
# parameter
#
parameter<-function(){
    infor<-tktoplevel()
    tkfocus(infor)
    tkgrab.set(infor)
    tkwm.title(infor,"Set Parameter Values")
    
    textEntry1<-Entry1
    textEntry2<-Entry2
    #     textEntry3<-Entry3
    textEntry4<-Entry4
    textEntry5<-Entry5
    textEntry6<-Entry6;textEntry7<-Entry7
    textEntry8<-Entry8;textEntry9<-Entry9
    #     textEntry10<-Entry10;textEntry11<-Entry11
    textEntry12<-Entry12
    
    textEntryWidget1<-tkentry(infor,width=20,textvariable=textEntry1)
    textEntryWidget2<-tkentry(infor,width=20,textvariable=textEntry2)
    #     textEntryWidget3<-tkentry(infor,width=20,textvariable=textEntry3)
    textEntryWidget4<-tkentry(infor,width=20,textvariable=textEntry4)
    textEntryWidget5<-tkentry(infor,width=20,textvariable=textEntry5)
    textEntryWidget6<-tkentry(infor,width=20,textvariable=textEntry6)
    textEntryWidget7<-tkentry(infor,width=20,textvariable=textEntry7)
    textEntryWidget8<-tkentry(infor,width=20,textvariable=textEntry8)
    textEntryWidget9<-tkentry(infor,width=20,textvariable=textEntry9)
    #     textEntryWidget10<-tkentry(infor,width=20,textvariable=textEntry10)
    #     textEntryWidget11<-tkentry(infor,width=20,textvariable=textEntry11)
    textEntryWidget12<-tkentry(infor,width=20,textvariable=textEntry12)
    
    tkgrid(tklabel(infor,text="User defined parameters for Indices Calculation",font=fontHeading1))
    tkgrid(tklabel(infor,text="First year of base period"),textEntryWidget1)
    tkgrid(tklabel(infor,text="Last year of base period"),textEntryWidget2)
    tkgrid(tklabel(infor,text="Latitude of this station location"),textEntryWidget4)
    tkgrid(tklabel(infor,text="Longitude of this station location"),textEntryWidget5)
    tkgrid(tklabel(infor,text="User defined upper threshold of daily maximum temperature"),textEntryWidget6)
    tkgrid(tklabel(infor,text="User defined lower threshold of daily maximum temperature"),textEntryWidget7)
    tkgrid(tklabel(infor,text="User defined upper threshold of daily minimum temperature"),textEntryWidget8)
    tkgrid(tklabel(infor,text="User defined lower threshold of daily minimum temperature"),textEntryWidget9)
    tkgrid(tklabel(infor,text="User defined daily precipitation threshold"),textEntryWidget12)
    
    #----------- OK1 -----------------------------------------    
    ok1<-function(){
        #       tkmessageBox(message="This process may take 2 mins to initialize the data. Please wait until the window disapear!")
        startyear<-as.numeric(tclvalue(textEntry1));  assign("startyear",startyear,envir=.GlobalEnv)
        endyear<-as.numeric(tclvalue(textEntry2));    assign("endyear",endyear,envir=.GlobalEnv)
        if(startyear<years|endyear>yeare){
            if(startyear<years) msg<-paste("Input base period start:", startyear," less then start year of data:", years, sep=" ")
            else msg<-paste("Input base period end:", endyear," greater then end year of data:", yeare, sep=" ")
            tkmessageBox(message=msg)
            tkfocus(infor)
            return()
        }
        #       winsize<-as.numeric(tclvalue(textEntry3));   assign("winsize",winsize,envir=.GlobalEnv)
        latitude<-as.numeric(tclvalue(textEntry4));   assign("latitude",latitude,envir=.GlobalEnv)
        longitude<-as.numeric(tclvalue(textEntry5));  assign("longitude",longitude,envir=.GlobalEnv)
        #       threshold<-as.numeric(tclvalue(textEntry5)); assign("threshold",threshold,envir=.GlobalEnv)
        uuu<-as.numeric(tclvalue(textEntry6));        assign("uuu",uuu,envir=.GlobalEnv)
        ulu<-as.numeric(tclvalue(textEntry7));        assign("uul",ulu,envir=.GlobalEnv)
        uul<-as.numeric(tclvalue(textEntry8));        assign("ulu",uul,envir=.GlobalEnv)
        ull<-as.numeric(tclvalue(textEntry9));        assign("ull",ull,envir=.GlobalEnv)
        #       up<-as.numeric(tclvalue(textEntry10));       assign("up",up,envir=.GlobalEnv)
        #       lp<-as.numeric(tclvalue(textEntry11));       assign("lp",lp,envir=.GlobalEnv)
        nn<-as.numeric(tclvalue(textEntry12));        assign("nn",nn,envir=.GlobalEnv)
        startpoint<-startyear-1;   assign("startpoint",startpoint,envir=.GlobalEnv)
        endpoint<-endyear+1;       assign("endpoint",endpoint,envir=.GlobalEnv)
        nordaytem1()
        tkgrab.release(infor)
        tkdestroy(infor)
        Entry1<-textEntry1;   assign("Entry1",Entry1,envir=.GlobalEnv)
        Entry2<-textEntry2;   assign("Entry2",Entry2,envir=.GlobalEnv)
        #       Entry3<-textEntry3;  assign("Entry3",Entry3,envir=.GlobalEnv)
        Entry4<-textEntry4;   assign("Entry4",Entry4,envir=.GlobalEnv)
        Entry5<-textEntry5;   assign("Entry5",Entry5,envir=.GlobalEnv)
        Entry6<-textEntry6;   assign("Entry6",Entry6,envir=.GlobalEnv)
        Entry7<-textEntry7;   assign("Entry7",Entry7,envir=.GlobalEnv)
        Entry8<-textEntry8;   assign("Entry8",Entry8,envir=.GlobalEnv)
        Entry9<-textEntry9;   assign("Entry9",Entry9,envir=.GlobalEnv)
        #       Entry10<-textEntry10; assign("Entry10",Entry10,envir=.GlobalEnv)
        #       Entry11<-textEntry11; assign("Entry11",Entry11,envir=.GlobalEnv)
        Entry12<-textEntry12;  assign("Entry12",Entry12,envir=.GlobalEnv)
        main1()
    }
    #----------- OK1 ends -----------------------------------------  
    
    #----------- cancel1 -----------------------------------------  
    cancel1<-function(){
        #       tkmessageBox(message="Please enter these parameters before you can move forward!!")
        #       tkfocus(infor)
        tkdestroy(infor)
        #       tkdestroy(main)
        #       tkfocus(start1)
        return()
    }
    #----------- cancel1 ends -----------------------------------------  
    
    ok1.but<-    tkbutton(infor,text="    OK    ",command=ok1)
    cancel1.but<-tkbutton(infor,text="  CANCEL  ",command=cancel1)
    tkgrid(ok1.but,cancel1.but)
}
#----------- parameter ends -----------------------------------------  
# End of Part I (general functions & TCL/TK functions
##################################################################################


##################################################################################
# Part II
# Functions of calculating climate indecies
##################################################################################
#----------- hwfi -----------------------------------------  
hwfi<-function(){
    if (flag==T) return()
    hwfi<-matrix(0,(yeare-years+1),2)
    dimnames(hwfi)[[2]]<-c("year","wsdi")
    hwfi[,"year"]<-years:yeare
    for (year in years:yeare) {
        if(leapyear(year)){
            aa<-rep(0,366)
            aa[1:59]<-aas[,"pcmax90"][1:59]
            aa[60]<-aa[59]
            aa[61:366]<-aas[,"pcmax90"][60:365]
        }
        else aa<-aas[,"pcmax90"]
        bb<-dd[dd$year==year,"tmax"]
        if(length(aa)!=length(bb)) stop("ERROR in WSDI, check data!")
        midval<-bb-aa
        ylen<-length(aa)
        ycnt<-0
        icnt<-0
        for(i in 1:ylen){
            if(is.na(midval[i])==F&midval[i]>0)
                icnt<-icnt+1
            else{
                if(icnt>=6) ycnt<-ycnt+icnt
                icnt<-0
            }
            if(i==ylen&icnt>=6) ycnt<-ycnt+icnt
        }
        hwfi[year-years+1,2]<-ycnt
    }
    hwfi<-as.data.frame(hwfi)
    hwfi[,"wsdi"]<-hwfi[,"wsdi"]+ynacor[,"ynatma>15"]  
    nam1<-paste(outinddir,paste(ofilename,"_WSDI.csv",sep=""),sep="/")
    write.table(hwfi,file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
    
    namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
    if(sum(is.na(hwfi[,"wsdi"]))>=(yeare-years+1-10)){
        betahat<-NA
        betastd<-NA
        pvalue<-NA
    }
    else{
        fit1<-lsfit(hwfi[,"year"],hwfi[,"wsdi"])
        out1<-ls.print(fit1,print.it=F)
        pvalue<-round(as.numeric(out1$summary[1,6]),3)
        betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
        betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
    }
    cat(file=namt,paste(latitude,longitude,"wsdi",years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
    
    nam2<-paste(outjpgdir,paste(ofilename,"_WSDI.jpg",sep=""),sep="/")
    jpeg(nam2,width=1024,height=768)
    plotx(hwfi[,1],hwfi[,2], main=paste("WSDI",ofilename,sep="   "),ylab="WSDI",xlab="Year")
    dev.off()
} 
# ------------ hwfi ends ------------------------------------

# ------------- cwdi ----------------------------------------
cwdi<-function(){
    if (flag==T) return()
    cwdi<-matrix(0,(yeare-years+1),2)
    dimnames(cwdi)[[2]]<-c("year","csdi")
    cwdi[,"year"]<-years:yeare
    for (year in years:yeare) {
        if(leapyear(year)){
            aa<-rep(0,366)
            aa[1:59]<-aas[,"pcmin10"][1:59]
            aa[60]<-aa[59]
            aa[61:366]<-aas[,"pcmin10"][60:365]
        }
        else aa<-aas[,"pcmin10"]
        bb<-dd[dd$year==year,"tmin"]
        if(length(aa)!=length(bb)) stop("ERROR in CWDI, check data!")
        midval<-aa-bb
        ylen<-length(aa)
        ycnt<-0
        icnt<-0
        for(i in 1:ylen){
            if(is.na(midval[i])==F&midval[i]>0)
                icnt<-icnt+1
            else{
                if(icnt>=6) ycnt<-ycnt+icnt
                icnt<-0
            }
            if(i==ylen&icnt>=6) ycnt<-ycnt+icnt
        }
        cwdi[year-years+1,2]<-ycnt
    }
    cwdi<-as.data.frame(cwdi)
    cwdi[,"csdi"]<-cwdi[,"csdi"]+ynacor[,"ynatmi>15"]  
    nam1<-paste(outinddir,paste(ofilename,"_CSDI.csv",sep=""),sep="/")
    write.table(cwdi,file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
    
    namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
    if(sum(is.na(cwdi[,"csdi"]))>=(yeare-years+1-10)){
        betahat<-NA
        betastd<-NA
        pvalue<-NA
    }
    else{
        fit1<-lsfit(cwdi[,"year"],cwdi[,"csdi"])
        out1<-ls.print(fit1,print.it=F)
        pvalue<-round(as.numeric(out1$summary[1,6]),3)
        betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
        betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
    }
    cat(file=namt,paste(latitude,longitude,"csdi",years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
    
    nam2<-paste(outjpgdir,paste(ofilename,"_CSDI.jpg",sep=""),sep="/")
    jpeg(nam2,width=1024,height=768)
    plotx(cwdi[,1],cwdi[,2],main=paste("CSDI",ofilename,sep="   "),ylab="CSDI",xlab="Year")
    dev.off()
} 
# ----------------- cwdi ends -------------------------------------------

#----------- r95ptot -----------------------------------------
r95ptot<-function(){
    prcptmp<-dd[dd$year>=startyear&dd$year<=endyear&dd$prcp>=1,"prcp"]
    prcptmp<-prcptmp[is.na(prcptmp)==F]
    len<-length(prcptmp)
    prcp95<-percentile(len,prcptmp,0.95)
    prcp99<-percentile(len,prcptmp,0.99)
    
    ys<-yeare-years+1
    
    dp<-matrix(0,ys,4)
    dimnames(dp)<-list(NULL,c("year","r95p","r99p","prcptot"))
    dp[,"year"]<-years:yeare
    for(i in years:yeare){
        dp[(i-years+1),"r95p"]<-sum(dd[dd$year==i&dd$prcp>prcp95,"prcp"],na.rm=T)
        dp[(i-years+1),"r99p"]<-sum(dd[dd$year==i&dd$prcp>prcp99,"prcp"],na.rm=T)
        dp[(i-years+1),"prcptot"]<-sum(dd[dd$year==i&dd$prcp>=1,"prcp"],na.rm=T)
    }
    dp[,"r95p"]<-round(dp[,"r95p"],1)+ynacor[,"ynapr>15"]
    dp[,"r99p"]<-round(dp[,"r99p"],1)+ynacor[,"ynapr>15"]
    dp[,"prcptot"]<-round(dp[,"prcptot"],1)+ynacor[,"ynapr>15"]
    dp<-as.data.frame(dp)
    nam1<-paste(outinddir,paste(ofilename,"_R95p.csv",sep=""),sep="/")
    nam2<-paste(outinddir,paste(ofilename,"_R99p.csv",sep=""),sep="/")
    nam3<-paste(outinddir,paste(ofilename,"_PRCPTOT.csv",sep=""),sep="/")
    write.table(dp[,c("year","r95p")],file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
    write.table(dp[,c("year","r99p")],file=nam2,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
    write.table(dp[,c("year","prcptot")],file=nam3,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
    
    namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
    for(i in c("r95p","r99p","prcptot")){
        if(sum(is.na(dp[,i]))>=(yeare-years+1-10)){
            betahat<-NA
            betastd<-NA
            pvalue<-NA
        }
        else{
            fit1<-lsfit(dp[,"year"],dp[,i])
            out1<-ls.print(fit1,print.it=F)
            pvalue<-round(as.numeric(out1$summary[1,6]),3)
            betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
            betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
        }
        cat(file=namt,paste(latitude,longitude,i,years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
    }
    
    nam4<-paste(outjpgdir,paste(ofilename,"_R95p.jpg",sep=""),sep="/")
    jpeg(nam4,width=1024,height=768)
    plotx(dp[,1],dp[,"r95p"],main=paste("R95p",ofilename,sep="   "),xlab="Year",ylab="R95p")
    dev.off()
    nam5<-paste(outjpgdir,paste(ofilename,"_R99p.jpg",sep=""),sep="/")
    jpeg(nam5,width=1024,height=768)
    plotx(dp[,1],dp[,"r99p"],main=paste("R99p",ofilename,sep="   "),xlab="Year",ylab="R99p")
    dev.off()
    nam6<-paste(outjpgdir,paste(ofilename,"_PRCPTOT.jpg",sep=""),sep="/")
    jpeg(nam6,width=1024,height=768)
    plotx(dp[,1],dp[,"prcptot"],main=paste("PRCPTOT",ofilename,sep="   "),xlab="Year",ylab="PRCPTOT")
    dev.off()
}
#----------- r95ptot ends -----------------------------------------

#----------- daysprcp20 -----------------------------------------
daysprcp20<-function(){
    ys<-yeare-years+1
    R20<-rep(0,ys)
    yearss<-c(years:yeare)
    target<-as.data.frame(cbind(yearss,R20))
    for (year in years:yeare){
        mid<-dd[dd$year==year,"prcp"]
        mid<-mid[is.na(mid)==F]
        target[target$yearss==year,"R20"]<-length(mid[mid>=20])}# end for
    dimnames(target)[[2]][1]<-"year"
    target[,"R20"]<-target[,"R20"]+ynacor[,"ynapr>15"]
    nam1<-paste(outinddir,paste(ofilename,"_R20mm.csv",sep=""),sep="/")
    write.table(target,file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
    
    namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
    if(sum(is.na(target[,"R20"]))>=(yeare-years+1-10)){
        betahat<-NA
        betastd<-NA
        pvalue<-NA
    }
    else{
        fit1<-lsfit(target[,"year"],target[,"R20"])
        out1<-ls.print(fit1,print.it=F)
        pvalue<-round(as.numeric(out1$summary[1,6]),3)
        betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
        betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
    }
    cat(file=namt,paste(latitude,longitude,"r20mm",years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
    
    nam2<-paste(outjpgdir,paste(ofilename,"_R20mm.jpg",sep=""),sep="/")
    jpeg(nam2,width=1024,height=768)
    plotx(target[,1],target[,2],main=paste("R20mm",ofilename,sep="   "),xlab="Year",ylab="R20mm")
    dev.off()
}
#----------- daysprcp20 ends -----------------------------------------

#----------- daysprcpn -----------------------------------------
daysprcpn<-function(){
    ys<-yeare-years+1
    Rnn<-rep(0,ys)
    yearss<-c(years:yeare)
    target<-as.data.frame(cbind(yearss,Rnn))
    for (year in years:yeare){
        mid<-dd[dd$year==year,"prcp"]
        mid<-mid[is.na(mid)==F]
        target[target$yearss==year,"Rnn"]<-length(mid[mid>=nn])
    }
    dimnames(target)[[2]][1]<-"year"
    target[,"Rnn"]<-target[,"Rnn"]+ynacor[,"ynapr>15"]
    
    nam1<-paste(outinddir,paste(ofilename,"_R",as.character(nn),"mm.csv",sep=""),sep="/")
    write.table(target,file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
    
    namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
    if(sum(is.na(target[,"Rnn"]))>=(yeare-years+1-10)){
        betahat<-NA
        betastd<-NA
        pvalue<-NA
    }
    else{
        fit1<-lsfit(target[,1],target[,"Rnn"])
        out1<-ls.print(fit1,print.it=F)
        pvalue<-round(as.numeric(out1$summary[1,6]),3)
        betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
        betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
    }
    cat(file=namt,paste(latitude,longitude,paste("R",as.character(nn),"mm",sep=""),years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
    
    nam2<-paste(outjpgdir,paste(ofilename,"_R",as.character(nn),"mm.jpg",sep=""),sep="/")
    jpeg(nam2,width=1024,height=768)
    plotx(target[,1],target[,2],main=paste("R",as.character(nn),"mm",ofilename,sep="   "),xlab="Year",ylab="Rnnmm")
    dev.off()
}
#----------- daysprcpn ends -----------------------------------------

#----------- nordaytem1 -----------------------------------------
nordaytem1<-function(){  # initialize data
    # normal temp
    
    daynorm<-dd[dd$year>=startyear,]
    daynorm<-daynorm[daynorm$year<=endyear,] # initialize daynorm matrix
    daynor<-daynorm              # create target matrix
    nn<-dd[dd$year==startpoint,]
    nn<-nn[nn$month==12,]
    nn<-nn[nn$day>(31-round(winsize/2)),]
    daynorm<-rbind(nn,daynorm)
    nn<-dd[dd$year==endpoint,]
    nn<-nn[nn$month==1,]
    nn<-nn[nn$day<=round(winsize/2),]
    daynorm<-rbind(daynorm,nn)
    
    daynorm1<-daynorm[,-4]
    daynorm1[daynorm1$month==2 & daynorm1$day==29,]<--99
    daynorm1<-daynorm1[daynorm1$year!=-99,]
    dayt<-daynorm1[,c("tmax","tmin")]
    
    ddtem<-dd[,-4]
    ddtem[ddtem$month==2 & ddtem$day==29,]<--99
    ddtem<-ddtem[ddtem$year!=-99,]
    assign("ddtem",ddtem,envir=.GlobalEnv)
    
    a<-matrix(-99,5,5)
    dimnames(a)[[2]]<-c("year","month","day","tmax","tmin")
    ddtemt<-rbind(a,ddtem);    assign("ddtemt",ddtemt,envir=.GlobalEnv)
    
    ys<-endyear-startyear+1
    window<-matrix(0,winsize,2)
    windows<-array(window,c(winsize,2,366,ys))
    dimnames(windows)<-list(NULL,c("tmax","tmin"),NULL,NULL)
    
    i=winsize-round(winsize/2,digits=0)
    i1=round(winsize/2,digits=0)
    daynormm<-daynorm[,c("tmax","tmin")]
    daynormm<-as.matrix(daynormm)
    year<-startyear
    
    for (k in 1:ys){
        if (leapyear(year)==T) jj<-366 else {jj<-365;   windows[,,366,k]<--99 }
        year<-year+1
        for (j in 1:jj){
            windows[,,j,k]<-daynormm[(i-i1):(i+i1),]
            i=i+1 }}
    
    mwindows<-colMeans(windows,na.rm=T)
    tmax<-mwindows["tmax",,];    tmax<-tmax[tmax!=-99]
    tmin<-mwindows["tmin",,];    tmin<-tmin[tmin!=-99]
    daynor[,"tmax"]<-tmax;       daynor[,"tmin"]<-tmin
    
    a<-rep(0,nrow(daynor))
    a<-(daynor[,"tmax"]+daynor[,"tmin"])/2
    daytemave<-a    
    daynor<-cbind(daynor,daytemave)
    
    # output the result to globe enviroment
    assign("daynor",daynor,envir=.GlobalEnv)
    assign("daynorm",daynorm,envir=.GlobalEnv)
    assign("daynorm1",daynorm1,envir=.GlobalEnv)
    assign("dayt",dayt,envir=.GlobalEnv)
    
    # prcp percentile 95% and 99%
    prcpnorm<-dd[dd$year>=startyear,]
    prcpnorm<-prcpnorm[prcpnorm$year<=endyear,] # initialize prcpnorm matrix
    nnp<-dd[dd$year==startpoint,]
    nnp<-nnp[nnp$month==12,]
    nnp<-nnp[nnp$day>29,]
    prcpnorm<-rbind(nnp,prcpnorm)
    nnp<-dd[dd$year==endpoint,]
    nnp<-nnp[nnp$month==1,]
    nnp<-nnp[nnp$day<=2,]
    prcpnorm<-rbind(prcpnorm,nnp)
    prcpnorm<-prcpnorm[,1:4]
    # remove Feb 29
    prcpnorm[prcpnorm$month==2 & prcpnorm$day==29,]<--99
    prcpnorm<-prcpnorm[prcpnorm$year!=-99,]
    assign("prcpnorm",prcpnorm,envir=.GlobalEnv)
    
    ys<-endyear-startyear+1 
    
    aasp<-matrix(NA,365,3)
    dimnames(aasp)<-list(NULL,c("day","prcp95","prcp99"))
    aasp[,"day"]<-1:365
    
    msp<-5*ys
    prcpnorm<-as.matrix(prcpnorm)
    pwindow<-matrix(0,5,1)
    pwindows<-array(pwindow,c(5,1,365,ys)) #array used to store all windows
    ip=3
    ip1=2
    for (k in 1:ys){
        for (j in 1:365){
            
            pwindows[,,j,k]<-prcpnorm[(ip-ip1):(ip+ip1),"prcp"]
            ip=ip+1}}
    
    prcpwin<-matrix(0,msp,2)
    prcpwin[,2]<-1:ys
    prcpwin[,2]<-mysort(prcpwin[,2],decreasing=F)
    
    prcpwins<-array(prcpwin,c(msp,2,365)) 
    
    for (j in 1:365){
        for (i in 1:ys){
            prcpwins[prcpwins[,2,j]==i,1,j]<-pwindows[,,j,i]}}
    #assign("exwins",exwins,envir=.GlobalEnv)
    
    for (i in 1:365){
        assp<-prcpwins[,,i]
        if(sum(is.na(assp[,1])==F)>=1)
            aasp[i,"prcp95"]<-percentile(msp,assp[,1],0.95)
        else aasp[i,"prcp95"]<-NA
        if(sum(is.na(assp[,1])==F)>=1)
            aasp[i,"prcp99"]<-percentile(msp,assp[,1],0.99)
        else aasp[i,"prcp99"]<-NA
    }
    assign("aasp",aasp,envir=.GlobalEnv)
    
    aas<-matrix(NA,365,5)
    dimnames(aas)<-list(NULL,c("day","pcmax10","pcmax90","pcmin10","pcmin90"))
    aas[,"day"]<-1:365
    
    ms<-winsize*ys
    dayt<-as.matrix(dayt)
    window<-matrix(0,winsize,2)
    windows<-array(window,c(winsize,2,365,ys)) #array used to store all windows
    i=winsize-round(winsize/2,digits=0)
    i1=round(winsize/2,digits=0)
    for (k in 1:ys){
        for (j in 1:365){
            
            windows[,,j,k]<-dayt[(i-i1):(i+i1),]
            i=i+1}}
    
    exwin<-matrix(0,ms,3)
    exwin[,3]<-1:ys
    exwin[,3]<-mysort(exwin[,3],decreasing=F)
    assign("exwin",exwin,envir=.GlobalEnv)
    #indd<-exwin[exwin[,3]!=ys,3]
    exwins<-array(exwin,c(ms,3,365)) # array for bootstrap
    
    for (j in 1:365){
        for (i in 1:ys){
            exwins[exwins[,3,j]==i,1:2,j]<-windows[,,j,i]}}
    assign("exwins",exwins,envir=.GlobalEnv)
    
    for ( i in 1:365){
        ass<-exwins[,,i]
        ass1<-ass[,1]
        ass2<-ass[,2]
        kgb1<-length(ass1[is.na(ass1)])
        kgb2<-length(ass2[is.na(ass2)])
        if (kgb1>37.5 | kgb2>37.5) {flag=T;break}}#150*0.25=37.5
    
    assign("flag",flag,envir=.GlobalEnv)
    if (flag==T) tkmessageBox(message="More than 25% data missing, Exceedance rate, HWDI,CWDI will not be calculated!!")
    if (flag==T) return()
    for (i in 1:365){
        ass<-exwins[,,i]
        # if(i == 363) {
        # ttmp<-ms
        # assign("ttmp",ttmp,envir=.GlobalEnv)
        # }
        itmp<-percentile(ms,ass[,1],c(0.1,0.9))
        aas[i,"pcmax10"]<-itmp[1]-1e-5
        aas[i,"pcmax90"]<-itmp[2]+1e-5
        itmp<-percentile(ms,ass[,2],c(0.1,0.9))
        aas[i,"pcmin10"]<-itmp[1]-1e-5
        aas[i,"pcmin90"]<-itmp[2]+1e-5  }
    
    assign("aas",aas,envir=.GlobalEnv)# matrix to store 10 and 90 percentile
    # exceedance rate before 1961 and after 2000
    before<-dd[dd$year<startyear,]
    after<-dd[dd$year>endyear,]
    
    # dataframe store the before monthly exceedance rate
    ys1<-startyear-years;ys2<-yeare-endyear
    bmonex<-matrix(NA,ys1*12,6)
    dimnames(bmonex)<-list(NULL,c("year","month","tx10p","tx90p","tn10p","tn90p"))
    bmonex[,"month"]<-rep(1:12,ys1)
    bmonex[,"year"]<-years:(startyear-1)
    bmonex[,"year"]<-mysort(bmonex[,"year"],decreasing=F)
    bmonex<-as.data.frame(bmonex)
    
    # dataframe store the after monthly exceedance rate
    amonex<-matrix(NA,ys2*12,6)
    dimnames(amonex)<-list(NULL,c("year","month","tx10p","tx90p","tn10p","tn90p"))
    amonex[,"month"]<-rep(1:12,ys2)
    amonex[,"year"]<-(endyear+1):yeare
    amonex[,"year"]<-mysort(amonex[,"year"],decreasing=F)
    amonex<-as.data.frame(amonex)
    
    # dataframe store yearly exceedance rate (before and after)
    yearex<-c(years:(startyear-1));   txg10p<-rep(0,length(yearex))
    txg90p<-rep(0,length(yearex));    tng10p<-rep(0,length(yearex))
    tng90p<-rep(0,length(yearex))
    bd<-as.data.frame(cbind(yearex,txg10p,txg90p,tng10p,tng90p))
    colnames(bd)[1]<-"year"
    
    yearex<-c((endyear+1):yeare);     txg10p<-rep(0,length(yearex))
    txg90p<-rep(0,length(yearex));    tng10p<-rep(0,length(yearex))
    tng90p<-rep(0,length(yearex))
    ad<-as.data.frame(cbind(yearex,txg10p,txg90p,tng10p,tng90p))
    colnames(ad)[1]<-"year"
    
    year=years;jjj6=1
    for (i in 1:ys1){
        midvalue<-ddtem[ddtem$year==year,]
        exmax10<-midvalue[,4]-aas[,2]
        exmax10m1<-exmax10[1:31];      exmax10m2<-exmax10[32:59];    exmax10m3<-exmax10[60:90]
        exmax10m4<-exmax10[91:120];    exmax10m5<-exmax10[121:151];  exmax10m6<-exmax10[152:181]
        exmax10m7<-exmax10[182:212];   exmax10m8<-exmax10[213:243];  exmax10m9<-exmax10[244:273]
        exmax10m10<-exmax10[274:304];  exmax10m11<-exmax10[305:334]; exmax10m12<-exmax10[335:365]
        
        exmax90<-midvalue[,4]-aas[,3]
        exmax90m1<-exmax90[1:31];      exmax90m2<-exmax90[32:59];    exmax90m3<-exmax90[60:90]
        exmax90m4<-exmax90[91:120];    exmax90m5<-exmax90[121:151];  exmax90m6<-exmax90[152:181]
        exmax90m7<-exmax90[182:212];   exmax90m8<-exmax90[213:243];  exmax90m9<-exmax90[244:273]
        exmax90m10<-exmax90[274:304];  exmax90m11<-exmax90[305:334]; exmax90m12<-exmax90[335:365]
        
        exmin10<-midvalue[,5]-aas[,4]
        exmin10m1<-exmin10[1:31];      exmin10m2<-exmin10[32:59];    exmin10m3<-exmin10[60:90]
        exmin10m4<-exmin10[91:120];    exmin10m5<-exmin10[121:151];  exmin10m6<-exmin10[152:181]
        exmin10m7<-exmin10[182:212];   exmin10m8<-exmin10[213:243];  exmin10m9<-exmin10[244:273]
        exmin10m10<-exmin10[274:304];  exmin10m11<-exmin10[305:334]; exmin10m12<-exmin10[335:365]
        
        exmin90<-midvalue[,5]-aas[,5]
        exmin90m1<-exmin90[1:31];      exmin90m2<-exmin90[32:59];    exmin90m3<-exmin90[60:90]
        exmin90m4<-exmin90[91:120];    exmin90m5<-exmin90[121:151];  exmin90m6<-exmin90[152:181]
        exmin90m7<-exmin90[182:212];   exmin90m8<-exmin90[213:243];  exmin90m9<-exmin90[244:273]
        exmin90m10<-exmin90[274:304];  exmin90m11<-exmin90[305:334]; exmin90m12<-exmin90[335:365]
        
        bd[i,"txg10p"]<-length(exmax10[exmax10<0&is.na(exmax10)==F])
        bd[i,"txg90p"]<-length(exmax90[exmax90>0&is.na(exmax90)==F])
        bd[i,"tng10p"]<-length(exmin10[exmin10<0&is.na(exmin10)==F])
        bd[i,"tng90p"]<-length(exmin90[exmin90>0&is.na(exmin90)==F])
        
        bmonex[jjj6,"tx10p"]<-length(exmax10m1[exmax10m1<0&is.na(exmax10m1)==F])
        bmonex[jjj6,"tx90p"]<-length(exmax90m1[exmax90m1>0&is.na(exmax90m1)==F])
        bmonex[jjj6,"tn10p"]<-length(exmin10m1[exmin10m1<0&is.na(exmin10m1)==F])
        bmonex[jjj6,"tn90p"]<-length(exmin90m1[exmin90m1>0&is.na(exmin90m1)==F])
        
        bmonex[jjj6+1,"tx10p"]<-length(exmax10m2[exmax10m2<0&is.na(exmax10m2)==F])
        bmonex[jjj6+1,"tx90p"]<-length(exmax90m2[exmax90m2>0&is.na(exmax90m2)==F])
        bmonex[jjj6+1,"tn10p"]<-length(exmin10m2[exmin10m2<0&is.na(exmin10m2)==F])
        bmonex[jjj6+1,"tn90p"]<-length(exmin90m2[exmin90m2>0&is.na(exmin90m2)==F])   
        
        bmonex[jjj6+2,"tx10p"]<-length(exmax10m3[exmax10m3<0&is.na(exmax10m3)==F])
        bmonex[jjj6+2,"tx90p"]<-length(exmax90m3[exmax90m3>0&is.na(exmax90m3)==F])
        bmonex[jjj6+2,"tn10p"]<-length(exmin10m3[exmin10m3<0&is.na(exmin10m3)==F])
        bmonex[jjj6+2,"tn90p"]<-length(exmin90m3[exmin90m3>0&is.na(exmin90m3)==F])
        
        bmonex[jjj6+3,"tx10p"]<-length(exmax10m4[exmax10m4<0&is.na(exmax10m4)==F])
        bmonex[jjj6+3,"tx90p"]<-length(exmax90m4[exmax90m4>0&is.na(exmax90m4)==F])
        bmonex[jjj6+3,"tn10p"]<-length(exmin10m4[exmin10m4<0&is.na(exmin10m4)==F])
        bmonex[jjj6+3,"tn90p"]<-length(exmin90m4[exmin90m4>0&is.na(exmin90m4)==F])
        
        bmonex[jjj6+4,"tx10p"]<-length(exmax10m5[exmax10m5<0&is.na(exmax10m5)==F])
        bmonex[jjj6+4,"tx90p"]<-length(exmax90m5[exmax90m5>0&is.na(exmax90m5)==F])
        bmonex[jjj6+4,"tn10p"]<-length(exmin10m5[exmin10m5<0&is.na(exmin10m5)==F])
        bmonex[jjj6+4,"tn90p"]<-length(exmin90m5[exmin90m5>0&is.na(exmin90m5)==F])
        
        bmonex[jjj6+5,"tx10p"]<-length(exmax10m6[exmax10m6<0&is.na(exmax10m6)==F])
        bmonex[jjj6+5,"tx90p"]<-length(exmax90m6[exmax90m6>0&is.na(exmax90m6)==F])
        bmonex[jjj6+5,"tn10p"]<-length(exmin10m6[exmin10m6<0&is.na(exmin10m6)==F])
        bmonex[jjj6+5,"tn90p"]<-length(exmin90m6[exmin90m6>0&is.na(exmin90m6)==F])
        
        bmonex[jjj6+6,"tx10p"]<-length(exmax10m7[exmax10m7<0&is.na(exmax10m7)==F])
        bmonex[jjj6+6,"tx90p"]<-length(exmax90m7[exmax90m7>0&is.na(exmax90m7)==F])
        bmonex[jjj6+6,"tn10p"]<-length(exmin10m7[exmin10m7<0&is.na(exmin10m7)==F])
        bmonex[jjj6+6,"tn90p"]<-length(exmin90m7[exmin90m7>0&is.na(exmin90m7)==F])
        
        bmonex[jjj6+7,"tx10p"]<-length(exmax10m8[exmax10m8<0&is.na(exmax10m8)==F])
        bmonex[jjj6+7,"tx90p"]<-length(exmax90m8[exmax90m8>0&is.na(exmax90m8)==F])
        bmonex[jjj6+7,"tn10p"]<-length(exmin10m8[exmin10m8<0&is.na(exmin10m8)==F])
        bmonex[jjj6+7,"tn90p"]<-length(exmin90m8[exmin90m8>0&is.na(exmin90m8)==F])
        
        bmonex[jjj6+8,"tx10p"]<-length(exmax10m9[exmax10m9<0&is.na(exmax10m9)==F])
        bmonex[jjj6+8,"tx90p"]<-length(exmax90m9[exmax90m9>0&is.na(exmax90m9)==F])
        bmonex[jjj6+8,"tn10p"]<-length(exmin10m9[exmin10m9<0&is.na(exmin10m9)==F])
        bmonex[jjj6+8,"tn90p"]<-length(exmin90m9[exmin90m9>0&is.na(exmin90m9)==F])
        
        bmonex[jjj6+9,"tx10p"]<-length(exmax10m10[exmax10m10<0&is.na(exmax10m10)==F])
        bmonex[jjj6+9,"tx90p"]<-length(exmax90m10[exmax90m10>0&is.na(exmax90m10)==F])
        bmonex[jjj6+9,"tn10p"]<-length(exmin10m10[exmin10m10<0&is.na(exmin10m10)==F])
        bmonex[jjj6+9,"tn90p"]<-length(exmin90m10[exmin90m10>0&is.na(exmin90m10)==F])
        
        bmonex[jjj6+10,"tx10p"]<-length(exmax10m11[exmax10m11<0&is.na(exmax10m11)==F])
        bmonex[jjj6+10,"tx90p"]<-length(exmax90m11[exmax90m11>0&is.na(exmax90m11)==F])
        bmonex[jjj6+10,"tn10p"]<-length(exmin10m11[exmin10m11<0&is.na(exmin10m11)==F])
        bmonex[jjj6+10,"tn90p"]<-length(exmin90m11[exmin90m11>0&is.na(exmin90m11)==F])
        
        bmonex[jjj6+11,"tx10p"]<-length(exmax10m12[exmax10m12<0&is.na(exmax10m12)==F])
        bmonex[jjj6+11,"tx90p"]<-length(exmax90m12[exmax90m12>0&is.na(exmax90m12)==F])
        bmonex[jjj6+11,"tn10p"]<-length(exmin10m12[exmin10m12<0&is.na(exmin10m12)==F])
        bmonex[jjj6+11,"tn90p"]<-length(exmin90m12[exmin90m12>0&is.na(exmin90m12)==F])
        
        if(leapyear(year)){
            if(dd[dd$year==year&dd$month==2&dd$day==29,"tmax"]>aas[59,"pcmax90"]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,"tmax"])==F&is.na(aas[59,"pcmax90"])==F)
                bmonex[jjj6+1,"tx90p"]<-bmonex[jjj6+1,"tx90p"]+1
            if(dd[dd$year==year&dd$month==2&dd$day==29,"tmax"]<aas[59,"pcmax10"]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,"tmax"])==F&is.na(aas[59,"pcmax10"])==F)
                bmonex[jjj6+1,"tx10p"]<-bmonex[jjj6+1,"tx10p"]+1
            if(dd[dd$year==year&dd$month==2&dd$day==29,"tmin"]>aas[59,"pcmin90"]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,"tmin"])==F&is.na(aas[59,"pcmin90"])==F)
                bmonex[jjj6+1,"tn90p"]<-bmonex[jjj6+1,"tn90p"]+1
            if(dd[dd$year==year&dd$month==2&dd$day==29,"tmin"]<aas[59,"pcmin10"]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,"tmin"])==F&is.na(aas[59,"pcmin10"])==F)
                bmonex[jjj6+1,"tn10p"]<-bmonex[jjj6+1,"tn10p"]+1
        }
        
        jjj6<-jjj6+12
        year=year+1     
    }
    
    year=endyear+1;jjj6=1
    for (i in 1:ys2){
        midvalue<-ddtem[ddtem$year==year,]
        exmax10<-midvalue[,4]-aas[,2]
        exmax10m1<-exmax10[1:31];     exmax10m2<-exmax10[32:59];    exmax10m3<-exmax10[60:90]
        exmax10m4<-exmax10[91:120];   exmax10m5<-exmax10[121:151];  exmax10m6<-exmax10[152:181]
        exmax10m7<-exmax10[182:212];  exmax10m8<-exmax10[213:243];  exmax10m9<-exmax10[244:273]
        exmax10m10<-exmax10[274:304]; exmax10m11<-exmax10[305:334]; exmax10m12<-exmax10[335:365]
        
        exmax90<-midvalue[,4]-aas[,3]
        exmax90m1<-exmax90[1:31];     exmax90m2<-exmax90[32:59];    exmax90m3<-exmax90[60:90]
        exmax90m4<-exmax90[91:120];   exmax90m5<-exmax90[121:151];  exmax90m6<-exmax90[152:181]
        exmax90m7<-exmax90[182:212];  exmax90m8<-exmax90[213:243];  exmax90m9<-exmax90[244:273]
        exmax90m10<-exmax90[274:304]; exmax90m11<-exmax90[305:334]; exmax90m12<-exmax90[335:365]
        
        exmin10<-midvalue[,5]-aas[,4]
        exmin10m1<-exmin10[1:31];     exmin10m2<-exmin10[32:59];    exmin10m3<-exmin10[60:90]
        exmin10m4<-exmin10[91:120];   exmin10m5<-exmin10[121:151];  exmin10m6<-exmin10[152:181]
        exmin10m7<-exmin10[182:212];  exmin10m8<-exmin10[213:243];  exmin10m9<-exmin10[244:273]
        exmin10m10<-exmin10[274:304]; exmin10m11<-exmin10[305:334]; exmin10m12<-exmin10[335:365]
        
        exmin90<-midvalue[,5]-aas[,5]
        exmin90m1<-exmin90[1:31];     exmin90m2<-exmin90[32:59];    exmin90m3<-exmin90[60:90]
        exmin90m4<-exmin90[91:120];   exmin90m5<-exmin90[121:151];  exmin90m6<-exmin90[152:181]
        exmin90m7<-exmin90[182:212];  exmin90m8<-exmin90[213:243];  exmin90m9<-exmin90[244:273]
        exmin90m10<-exmin90[274:304]; exmin90m11<-exmin90[305:334]; exmin90m12<-exmin90[335:365]
        
        ad[i,"txg10p"]<-length(exmax10[exmax10<0&is.na(exmax10)==F])
        ad[i,"txg90p"]<-length(exmax90[exmax90>0&is.na(exmax90)==F])
        ad[i,"tng10p"]<-length(exmin10[exmin10<0&is.na(exmin10)==F])
        ad[i,"tng90p"]<-length(exmin90[exmin90>0&is.na(exmin90)==F])
        
        amonex[jjj6,"tx10p"]<-length(exmax10m1[exmax10m1<0&is.na(exmax10m1)==F])
        amonex[jjj6,"tx90p"]<-length(exmax90m1[exmax90m1>0&is.na(exmax90m1)==F])
        amonex[jjj6,"tn10p"]<-length(exmin10m1[exmin10m1<0&is.na(exmin10m1)==F])
        amonex[jjj6,"tn90p"]<-length(exmin90m1[exmin90m1>0&is.na(exmin90m1)==F])
        
        amonex[jjj6+1,"tx10p"]<-length(exmax10m2[exmax10m2<0&is.na(exmax10m2)==F])
        amonex[jjj6+1,"tx90p"]<-length(exmax90m2[exmax90m2>0&is.na(exmax90m2)==F])
        amonex[jjj6+1,"tn10p"]<-length(exmin10m2[exmin10m2<0&is.na(exmin10m2)==F])
        amonex[jjj6+1,"tn90p"]<-length(exmin90m2[exmin90m2>0&is.na(exmin90m2)==F])   
        
        amonex[jjj6+2,"tx10p"]<-length(exmax10m3[exmax10m3<0&is.na(exmax10m3)==F])
        amonex[jjj6+2,"tx90p"]<-length(exmax90m3[exmax90m3>0&is.na(exmax90m3)==F])
        amonex[jjj6+2,"tn10p"]<-length(exmin10m3[exmin10m3<0&is.na(exmin10m3)==F])
        amonex[jjj6+2,"tn90p"]<-length(exmin90m3[exmin90m3>0&is.na(exmin90m3)==F])
        
        amonex[jjj6+3,"tx10p"]<-length(exmax10m4[exmax10m4<0&is.na(exmax10m4)==F])
        amonex[jjj6+3,"tx90p"]<-length(exmax90m4[exmax90m4>0&is.na(exmax90m4)==F])
        amonex[jjj6+3,"tn10p"]<-length(exmin10m4[exmin10m4<0&is.na(exmin10m4)==F])
        amonex[jjj6+3,"tn90p"]<-length(exmin90m4[exmin90m4>0&is.na(exmin90m4)==F])
        
        amonex[jjj6+4,"tx10p"]<-length(exmax10m5[exmax10m5<0&is.na(exmax10m5)==F])
        amonex[jjj6+4,"tx90p"]<-length(exmax90m5[exmax90m5>0&is.na(exmax90m5)==F])
        amonex[jjj6+4,"tn10p"]<-length(exmin10m5[exmin10m5<0&is.na(exmin10m5)==F])
        amonex[jjj6+4,"tn90p"]<-length(exmin90m5[exmin90m5>0&is.na(exmin90m5)==F])
        
        amonex[jjj6+5,"tx10p"]<-length(exmax10m6[exmax10m6<0&is.na(exmax10m6)==F])
        amonex[jjj6+5,"tx90p"]<-length(exmax90m6[exmax90m6>0&is.na(exmax90m6)==F])
        amonex[jjj6+5,"tn10p"]<-length(exmin10m6[exmin10m6<0&is.na(exmin10m6)==F])
        amonex[jjj6+5,"tn90p"]<-length(exmin90m6[exmin90m6>0&is.na(exmin90m6)==F])
        
        amonex[jjj6+6,"tx10p"]<-length(exmax10m7[exmax10m7<0&is.na(exmax10m7)==F])
        amonex[jjj6+6,"tx90p"]<-length(exmax90m7[exmax90m7>0&is.na(exmax90m7)==F])
        amonex[jjj6+6,"tn10p"]<-length(exmin10m7[exmin10m7<0&is.na(exmin10m7)==F])
        amonex[jjj6+6,"tn90p"]<-length(exmin90m7[exmin90m7>0&is.na(exmin90m7)==F])
        
        amonex[jjj6+7,"tx10p"]<-length(exmax10m8[exmax10m8<0&is.na(exmax10m8)==F])
        amonex[jjj6+7,"tx90p"]<-length(exmax90m8[exmax90m8>0&is.na(exmax90m8)==F])
        amonex[jjj6+7,"tn10p"]<-length(exmin10m8[exmin10m8<0&is.na(exmin10m8)==F])
        amonex[jjj6+7,"tn90p"]<-length(exmin90m8[exmin90m8>0&is.na(exmin90m8)==F])
        
        amonex[jjj6+8,"tx10p"]<-length(exmax10m9[exmax10m9<0&is.na(exmax10m9)==F])
        amonex[jjj6+8,"tx90p"]<-length(exmax90m9[exmax90m9>0&is.na(exmax90m9)==F])
        amonex[jjj6+8,"tn10p"]<-length(exmin10m9[exmin10m9<0&is.na(exmin10m9)==F])
        amonex[jjj6+8,"tn90p"]<-length(exmin90m9[exmin90m9>0&is.na(exmin90m9)==F])
        
        amonex[jjj6+9,"tx10p"]<-length(exmax10m10[exmax10m10<0&is.na(exmax10m10)==F])
        amonex[jjj6+9,"tx90p"]<-length(exmax90m10[exmax90m10>0&is.na(exmax90m10)==F])
        amonex[jjj6+9,"tn10p"]<-length(exmin10m10[exmin10m10<0&is.na(exmin10m10)==F])
        amonex[jjj6+9,"tn90p"]<-length(exmin90m10[exmin90m10>0&is.na(exmin90m10)==F])
        
        amonex[jjj6+10,"tx10p"]<-length(exmax10m11[exmax10m11<0&is.na(exmax10m11)==F])
        amonex[jjj6+10,"tx90p"]<-length(exmax90m11[exmax90m11>0&is.na(exmax90m11)==F])
        amonex[jjj6+10,"tn10p"]<-length(exmin10m11[exmin10m11<0&is.na(exmin10m11)==F])
        amonex[jjj6+10,"tn90p"]<-length(exmin90m11[exmin90m11>0&is.na(exmin90m11)==F])
        
        amonex[jjj6+11,"tx10p"]<-length(exmax10m12[exmax10m12<0&is.na(exmax10m12)==F])
        amonex[jjj6+11,"tx90p"]<-length(exmax90m12[exmax90m12>0&is.na(exmax90m12)==F])
        amonex[jjj6+11,"tn10p"]<-length(exmin10m12[exmin10m12<0&is.na(exmin10m12)==F])
        amonex[jjj6+11,"tn90p"]<-length(exmin90m12[exmin90m12>0&is.na(exmin90m12)==F])
        
        if(leapyear(year)){
            if(dd[dd$year==year&dd$month==2&dd$day==29,"tmax"]>aas[59,"pcmax90"]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,"tmax"])==F&is.na(aas[59,"pcmax90"])==F)
                amonex[jjj6+1,"tx90p"]<-amonex[jjj6+1,"tx90p"]+1
            if(dd[dd$year==year&dd$month==2&dd$day==29,"tmax"]<aas[59,"pcmax10"]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,"tmax"])==F&is.na(aas[59,"pcmax10"])==F)
                amonex[jjj6+1,"tx10p"]<-amonex[jjj6+1,"tx10p"]+1
            if(dd[dd$year==year&dd$month==2&dd$day==29,"tmin"]>aas[59,"pcmin90"]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,"tmin"])==F&is.na(aas[59,"pcmin90"])==F)
                amonex[jjj6+1,"tn90p"]<-amonex[jjj6+1,"tn90p"]+1
            if(dd[dd$year==year&dd$month==2&dd$day==29,"tmin"]<aas[59,"pcmin10"]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,"tmin"])==F&is.na(aas[59,"pcmin10"])==F)
                amonex[jjj6+1,"tn10p"]<-amonex[jjj6+1,"tn10p"]+1
        }
        
        jjj6<-jjj6+12
        year=year+1   }
    bdm<-merge(bmonex,bd,by="year");  assign("bdm",bdm,envir=.GlobalEnv)
    adm<-merge(amonex,ad,by="year");  assign("adm",adm,envir=.GlobalEnv)
    
} # end of nordaytem1 function
#----------- nordaytem1 ends -----------------------------------------

#----------- nordaytem -----------------------------------------
nordaytem<-function(){
    nam1<-paste(nama,"_DAYNOR.csv",sep="")
    write.table(daynor,file=nam1,append=F,quote=F,sep=", ",row.names=F)
}
#----------- nordaytem ends -----------------------------------------

#----------- dtr -----------------------------------------
dtr<-function(){# day temperature range(monthly average) 
    len<-yeare-years+1
    aa1<-matrix(NA,12*len,3)
    dimnames(aa1)<-list(NULL,c("year","month","dtr"))
    aa1[,"year"]<-years:yeare
    aa1[,"year"]<-mysort(aa1[,"year"],decreasing=F)
    aa1[,"month"]<-1:12
    temrange<-dd[,"tmax"]-dd[,"tmin"]
    temrange<-cbind(dd[,1:2],temrange)
    jjj1<-1
    for (year in years:yeare){    # start year loop
        temrange1<-temrange[temrange$year==year,]
        temrangem1<-temrange1[temrange1$month==1,"temrange"]
        temrangem2<-temrange1[temrange1$month==2,"temrange"]
        temrangem3<-temrange1[temrange1$month==3,"temrange"]
        temrangem4<-temrange1[temrange1$month==4,"temrange"]
        temrangem5<-temrange1[temrange1$month==5,"temrange"]
        temrangem6<-temrange1[temrange1$month==6,"temrange"]
        temrangem7<-temrange1[temrange1$month==7,"temrange"]
        temrangem8<-temrange1[temrange1$month==8,"temrange"]
        temrangem9<-temrange1[temrange1$month==9,"temrange"]
        temrangem10<-temrange1[temrange1$month==10,"temrange"]
        temrangem11<-temrange1[temrange1$month==11,"temrange"]
        temrangem12<-temrange1[temrange1$month==12,"temrange"]
        aa1[jjj1,3]<-mean(temrangem1,na.rm=T);     aa1[jjj1+1,3]<-mean(temrangem2,na.rm=T)
        aa1[jjj1+2,3]<-mean(temrangem3,na.rm=T);   aa1[jjj1+3,3]<-mean(temrangem4,na.rm=T)
        aa1[jjj1+4,3]<-mean(temrangem5,na.rm=T);   aa1[jjj1+5,3]<-mean(temrangem6,na.rm=T)
        aa1[jjj1+6,3]<-mean(temrangem7,na.rm=T);   aa1[jjj1+7,3]<-mean(temrangem8,na.rm=T)
        aa1[jjj1+8,3]<-mean(temrangem9,na.rm=T);   aa1[jjj1+9,3]<-mean(temrangem10,na.rm=T)
        aa1[jjj1+10,3]<-mean(temrangem11,na.rm=T); aa1[jjj1+11,3]<-mean(temrangem12,na.rm=T)
        jjj1<-jjj1+12}               #end of year loop
    
    aa1[,"dtr"]<-aa1[,"dtr"]+nacor[,"mnatma>3"]+nacor[,"mnatmi>3"]
    ofile<-matrix(0,len,14)
    dimnames(ofile)<-list(NULL,c("year","jan","feb","mar","apr","may","jun","jul","aug","sep","oct","nov","dec","annual"))
    ofile<-as.data.frame(ofile)
    for(j in years:yeare){
        k<-j-years+1
        ofile[k,1]<-j
        ofile[k,2:13]<-round(aa1[aa1[,"year"]==j,"dtr"],digit=2)
        ofile[k,14]<-round(mean(t(ofile[k,2:13]),na.rm=T),digit=2)
    }
    ofile[,14]<-ofile[,14]+ynacor[,"ynatma>15"]+ynacor[,"ynatmi>15"]
    nam1<-paste(outinddir,paste(ofilename,"_DTR.csv",sep=""),sep="/")
    write.table(ofile,file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
    
    namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
    if(sum(is.na(ofile[,14]))>=(yeare-years+1-10)){
        betahat<-NA
        betastd<-NA
        pvalue<-NA
    }
    else{
        fit1<-lsfit(ofile[,1],ofile[,14])
        out1<-ls.print(fit1,print.it=F)
        pvalue<-round(as.numeric(out1$summary[1,6]),3)
        betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
        betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
    }
    cat(file=namt,paste(latitude,longitude,"dtr",years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
    
    nam2<-paste(outjpgdir,paste(ofilename,"_DTR.jpg",sep=""),sep="/")
    jpeg(nam2,width=1024,height=768)
    plotx(ofile[,1],ofile[,14],main=paste("DTR",ofilename,sep="   "),xlab="Year",ylab="DTR")
    dev.off()
} # end of dtr
#----------- dtr ends -----------------------------------------

#----------- daysprcp10 -----------------------------------------  
daysprcp10<-function(){
    ys<-yeare-years+1
    R10<-rep(0,ys)
    yearss<-c(years:yeare)
    target<-as.data.frame(cbind(yearss,R10))
    for (year in years:yeare){
        mid<-dd[dd$year==year,"prcp"]
        mid<-mid[is.na(mid)==F]
        target[target$yearss==year,"R10"]<-length(mid[mid>=10])}
    dimnames(target)[[2]][1]<-"year"
    target[,"R10"]<-target[,"R10"]+ynacor[,"ynapr>15"]
    nam1<-paste(outinddir,paste(ofilename,"_R10mm.csv",sep=""),sep="/")
    write.table(target,file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
    
    namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
    if(sum(is.na(target[,"R10"]))>=(yeare-years+1-10)){
        betahat<-NA
        betastd<-NA
        pvalue<-NA
    }
    else{
        fit1<-lsfit(target[,1],target[,"R10"])
        out1<-ls.print(fit1,print.it=F)
        pvalue<-round(as.numeric(out1$summary[1,6]),3)
        betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
        betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
    }
    cat(file=namt,paste(latitude,longitude,"r10mm",years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
    
    nam2<-paste(outjpgdir,paste(ofilename,"_R10mm.jpg",sep=""),sep="/")
    jpeg(nam2,width=1024,height=768)
    plotx(target[,1],target[,2],main=paste("R10mm",ofilename,sep="   "),xlab="Year",ylab="R10mm")
    dev.off()
}
#----------- daysprcp10 ends -----------------------------------------

#----------- extremedays -----------------------------------------  
extremedays<-function(opt=0){
    if(opt==0){
        euu=uu
        eul=ul
        elu=lu
        ell=ll
    }
    else{
        euu=uuu
        eul=uul
        elu=ulu
        ell=ull
    }
    ys<-yeare-years+1
    #  beginyear<-dd[1,1]
    #  endyear<-dd[dim(dd)[1],1]
    tclext<-c(years:yeare)
    su<-rep(0,ys)
    id<-su
    tr<-su
    fd<-su
    tclext<-cbind(tclext,su,id,tr,fd)
    dimnames(tclext)[[2]][1]<-"year"
    i=1
    for (year in years:yeare) {
        mid1<-dd[dd$year==year,"tmax"];  mid1<-mid1[is.na(mid1)==F]
        mid2<-dd[dd$year==year,"tmin"];  mid2<-mid2[is.na(mid2)==F]
        tclext[i,"su"]<-length(mid1[mid1>euu])
        tclext[i,"id"]<-length(mid1[mid1<eul])
        tclext[i,"tr"]<-length(mid2[mid2>elu])
        tclext[i,"fd"]<-length(mid2[mid2<ell])
        i<-i+1} #for end    
    tclext<-as.data.frame(tclext)
    tclext[,"su"]<-tclext[,"su"]+ynacor[,"ynatma>15"]
    tclext[,"id"]<-tclext[,"id"]+ynacor[,"ynatma>15"]
    tclext[,"tr"]<-tclext[,"tr"]+ynacor[,"ynatmi>15"]
    tclext[,"fd"]<-tclext[,"fd"]+ynacor[,"ynatmi>15"]
    #    assign("extdays",tclext,envir=.GlobalEnv)
    if(opt==0){
        nam1<-paste(outinddir,paste(ofilename,"_SU25.csv",sep=""),sep="/")
        nam2<-paste(outinddir,paste(ofilename,"_ID0.csv",sep=""),sep="/")
        nam3<-paste(outinddir,paste(ofilename,"_TR20.csv",sep=""),sep="/")
        nam4<-paste(outinddir,paste(ofilename,"_FD0.csv",sep=""),sep="/")
    }
    else{
        nam1<-paste(outinddir,paste(ofilename,"_SU",as.character(euu),".csv",sep=""),sep="/")
        nam2<-paste(outinddir,paste(ofilename,"_ID",as.character(eul),".csv",sep=""),sep="/")
        nam3<-paste(outinddir,paste(ofilename,"_TR",as.character(elu),".csv",sep=""),sep="/")
        nam4<-paste(outinddir,paste(ofilename,"_FD",as.character(ell),".csv",sep=""),sep="/")
    }
    
    write.table(tclext[,c("year","su")],file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
    write.table(tclext[,c("year","id")],file=nam2,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
    write.table(tclext[,c("year","tr")],file=nam3,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
    write.table(tclext[,c("year","fd")],file=nam4,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
    
    # output trend base on annual indicies data
    namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
    for( i in c("su","id","tr","fd")){
        if(sum(is.na(tclext[,i]))>=(yeare-years+1-10)){
            betahat<-NA
            betastd<-NA
            pvalue<-NA
        }
        else{
            fit1<-lsfit(tclext[,"year"],tclext[,i])
            out1<-ls.print(fit1,print.it=F)
            pvalue<-round(as.numeric(out1$summary[1,6]),3)
            betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
            betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
        }
        if(opt==0){
            if(i=="su") ii<-"su25"
            if(i=="id") ii<-"id0"
            if(i=="fd") ii<-"fd0"
            if(i=="tr") ii<-"tr20"
        }
        else{
            if(i=="su") ii<-paste("su",as.character(euu),sep="")
            if(i=="id") ii<-paste("id",as.character(eul),sep="")
            if(i=="tr") ii<-paste("tr",as.character(elu),sep="")
            if(i=="fd") ii<-paste("fd",as.character(ell),sep="")
        }
        cat(file=namt,paste(latitude,longitude,ii,years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
    }
    
    namp<-c("","","","")
    if(opt==0){
        namp[1]<-paste(outjpgdir,paste(ofilename,"_SU25.jpg",sep=""),sep="/")
        namp[2]<-paste(outjpgdir,paste(ofilename,"_ID0.jpg",sep=""),sep="/")
        namp[3]<-paste(outjpgdir,paste(ofilename,"_TR20.jpg",sep=""),sep="/")
        namp[4]<-paste(outjpgdir,paste(ofilename,"_FD0.jpg",sep=""),sep="/")
    }
    else{
        namp[1]<-paste(outjpgdir,paste(ofilename,"_SU",as.character(euu),".jpg",sep=""),sep="/")
        namp[2]<-paste(outjpgdir,paste(ofilename,"_ID",as.character(eul),".jpg",sep=""),sep="/")
        namp[3]<-paste(outjpgdir,paste(ofilename,"_TR",as.character(elu),".jpg",sep=""),sep="/")
        namp[4]<-paste(outjpgdir,paste(ofilename,"_FD",as.character(ell),".jpg",sep=""),sep="/")
    }
    if(opt==0) ylab<-c("SU25","ID0","TR20","FD0")
    else ylab<-c(paste("SU",as.character(euu),sep=""), 
                 paste("ID",as.character(eul),sep=""), 
                 paste("TR",as.character(elu),sep=""), 
                 paste("FD",as.character(ell),sep=""))
    
    xlab<-rep("year",4)
    for(i in 1:4){
        title1[i]<-paste(ylab[i],ofilename,sep="   ")
        jpeg(file=namp[i],width=1024,height=768)
        plotx(tclext[,1],tclext[,i+1],main=title1[i],ylab=ylab[i],xlab="Year")
        dev.off()
    }
}
#----------- extremedays ends -----------------------------------------  

#----------- exceedance -----------------------------------------  
exceedance<-function(){
    if (flag==T) return()
    a<-1:365
    ys<-endyear-startyear+1;yss<-ys-1
    mondays<-c(31,28,31,30,31,30,31,31,30,31,30,31)
    mone<-rep(0,12);mons<-mone
    for(i in 1:12) mone[i]<-sum(mondays[1:i])
    mons[1]<-1
    for(i in 2:12) mons[i]<-mone[i-1]+1
    
    monex<-matrix(NA,ys*12,6)
    dimnames(monex)<-list(NULL,c("year","month","tx10p","tx90p","tn10p","tn90p"))
    monex[,"month"]<-rep(1:12,ys)
    monex[,"year"]<-startyear:endyear
    monex[,"year"]<-mysort(monex[,"year"],decreasing=F)
    monex<-as.data.frame(monex)
    
    
    b<-matrix(0,365,4)
    a<-cbind(a,b)
    aa<-array(a,c(365,5,ys))
    dimnames(aa)<-list(NULL,c("day","pcmax10","pcmax90","pcmin10","pcmin90"),NULL)
    ms<-winsize*ys
    i=winsize-round(winsize/2,digits=0)
    i1=round(winsize/2,digits=0)
    
    #  daynorm2<-daynorm1[-(1:i1),] # daynorm2 is total base period normalized data
    daynorm2<-dd[dd$year>=startyear,]
    daynorm2<-daynorm2[daynorm2$year<=endyear,]
    daynorm2<-daynorm2[daynorm2$month!=2|daynorm2$day!=29,]
    daynorm2<-daynorm2[,-4]
    #  i2<-dim(daynorm2)[1]
    #  i3<-i2-i1+1
    #  daynorm2<-daynorm2[-(i3:i2),]
    
    yearex<-c(startyear:endyear);   txg10p<-rep(0,length(yearex))
    txg90p<-rep(0,length(yearex));  tng10p<-rep(0,length(yearex))
    tng90p<-rep(0,length(yearex))
    d<-as.data.frame(cbind(yearex,txg10p,txg90p,tng10p,tng90p))
    colnames(d)[1]<-"year"
    
    monex<-matrix(0,ys*12,6)
    dimnames(monex)<-list(NULL,c("year","month","tx10p","tx90p","tn10p","tn90p"))
    monex[,"month"]<-rep(1:12,ys)
    monex[,"year"]<-startyear:endyear
    monex[,"year"]<-mysort(monex[,"year"],decreasing=F)
    monex<-as.data.frame(monex)
    
    ratecount<-matrix(0,365,4)
    dimnames(ratecount)<-list(NULL,c("pcmax10","pcmax90","pcmin10","pcmin90"))
    
    for (year in startyear:endyear){ # year loop start
        
        midvalue<-daynorm2[daynorm2$year==year,]
        zz=year-startpoint #index in base period, say, zzth year
        
        indd<-exwin[exwin[,3]!=ys,3]
        
        for (k in 1:(ys-1)){ # for k (boot strap) start
            
            for (i in 1:365){ # day loop start
                ppc<-exwins[,,i]
                ppc<-ppc[ppc[,3]!=zz,]
                ppc<-ppc[,-3]
                
                ppc<-cbind(ppc,indd)
                
                ppcc<-rbind(ppc[ppc[,"indd"]==k,],ppc)
                itmp<-percentile(ms,ppcc[,1],c(0.1,0.9))
                aa[i,"pcmax10",zz]<-itmp[1]-1e-5
                aa[i,"pcmax90",zz]<-itmp[2]+1e-5
                itmp<-percentile(ms,ppcc[,2],c(0.1,0.9))
                aa[i,"pcmin10",zz]<-itmp[1]-1e-5
                aa[i,"pcmin90",zz]<-itmp[2]+1e-5
            }
            ratecount[,"pcmax10"]<-midvalue[,"tmax"]-aa[,"pcmax10",zz]
            ratecount[,"pcmax90"]<-midvalue[,"tmax"]-aa[,"pcmax90",zz]
            ratecount[,"pcmin10"]<-midvalue[,"tmin"]-aa[,"pcmin10",zz]
            ratecount[,"pcmin90"]<-midvalue[,"tmin"]-aa[,"pcmin90",zz]
            for(mon in 1:12){
                tmptx10p<-ratecount[mons[mon]:mone[mon],"pcmax10"]
                tmptx90p<-ratecount[mons[mon]:mone[mon],"pcmax90"]
                tmptn10p<-ratecount[mons[mon]:mone[mon],"pcmin10"]
                tmptn90p<-ratecount[mons[mon]:mone[mon],"pcmin90"]
                monex[(zz-1)*12+mon,"tx10p"]<- monex[(zz-1)*12+mon,"tx10p"]+length(tmptx10p[tmptx10p<0&is.na(tmptx10p)==F])
                monex[(zz-1)*12+mon,"tx90p"]<- monex[(zz-1)*12+mon,"tx90p"]+length(tmptx90p[tmptx90p>0&is.na(tmptx90p)==F])
                monex[(zz-1)*12+mon,"tn10p"]<- monex[(zz-1)*12+mon,"tn10p"]+length(tmptn10p[tmptn10p<0&is.na(tmptn10p)==F])
                monex[(zz-1)*12+mon,"tn90p"]<- monex[(zz-1)*12+mon,"tn90p"]+length(tmptn90p[tmptn90p>0&is.na(tmptn90p)==F])
                if(leapyear(year)&mon==2){
                    if(dd[dd$year==year&dd$month==2&dd$day==29,"tmax"]>aa[59,"pcmax90",zz]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,"tmax"])==F&is.na(aa[58,"pcmax90",zz])==F)
                        monex[(zz-1)*12+mon,"tx90p"]<-monex[(zz-1)*12+mon,"tx90p"]+1
                    if(dd[dd$year==year&dd$month==2&dd$day==29,"tmax"]<aa[59,"pcmax10",zz]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,"tmax"])==F&is.na(aa[58,"pcmax10",zz])==F)
                        monex[(zz-1)*12+mon,"tx10p"]<-monex[(zz-1)*12+mon,"tx10p"]+1
                    if(dd[dd$year==year&dd$month==2&dd$day==29,"tmin"]>aa[59,"pcmin90",zz]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,"tmin"])==F&is.na(aa[58,"pcmin90",zz])==F)
                        monex[(zz-1)*12+mon,"tn90p"]<-monex[(zz-1)*12+mon,"tn90p"]+1
                    if(dd[dd$year==year&dd$month==2&dd$day==29,"tmin"]<aa[59,"pcmin10",zz]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,"tmin"])==F&is.na(aa[58,"pcmin10",zz])==F)
                        monex[(zz-1)*12+mon,"tn10p"]<-monex[(zz-1)*12+mon,"tn10p"]+1
                } #if end
            } #for mon end
        } #for k (boot strap) end
    }# for year (from startyear to endyear) end
    
    monex[,"tx10p"]<-monex[,"tx10p"]/29.
    monex[,"tx90p"]<-monex[,"tx90p"]/29.
    monex[,"tn10p"]<-monex[,"tn10p"]/29.
    monex[,"tn90p"]<-monex[,"tn90p"]/29.
    #  monex<-rbind(bdm,monex,adm)
    
    #  assign("dm",dm,envir=.GlobalEnv)
    
    dm<-merge(monex,d,by="year")
    dm<-rbind(bdm,dm,adm)
    
    len<-yeare-years+1
    for(i in c("tx10p","tx90p","tn10p","tn90p")){
        if (i=="tx10p") {ii<-"_TX10P.csv";   kk<-3;nastat=7}#natma
        if (i=="tx90p") {ii<-"_TX90P.csv";   kk<-4;nastat=7}#natma
        if (i=="tn10p") {ii<-"_TN10P.csv";   kk<-5;nastat=8}#natmi
        if (i=="tn90p") {ii<-"_TN90P.csv";   kk<-6;nastat=8}#natmi
        
        nam1<-paste(outinddir,paste(ofilename,ii,sep=""),sep="/")
        ofile<-matrix(0,len,14)
        dimnames(ofile)<-list(NULL,c("year","jan","feb","mar","apr","may","jun","jul","aug","sep","oct","nov","dec","annual"))
        ofile<-as.data.frame(ofile)
        for(j in years:yeare){
            if(leapyear(j)) fulldays<-c(31,29,31,30,31,30,31,31,30,31,30,31)
            else fulldays<-c(31,28,31,30,31,30,31,31,30,31,30,31)
            k<-j-years+1
            ofile[k,1]<-j
            ofile[k,2:13]<-dm[dm$year==j,kk]
            for(mon in 1:12){
                if(nastatistic[(k-1)*12+mon,nastat]>10) ofile[k,(mon+1)]<-NA
                else   ofile[k,(mon+1)]<-dm[(k-1)*12+mon,kk]*fulldays[mon]/(fulldays[mon]-nastatistic[(k-1)*12+mon,nastat])
            }
            ofile[k,14]<-sum(ofile[k,2:13],na.rm=T)
        }
        ofile[,14]<-ofile[,14]+ynacor[,nastat-4]
        for(j in years:yeare){
            k<-j-years+1
            if(leapyear(j)) fulldays<-c(31,29,31,30,31,30,31,31,30,31,30,31)
            else fulldays<-c(31,28,31,30,31,30,31,31,30,31,30,31)
            for(mon in 1:12) ofile[k,mon+1]<-ofile[k,mon+1]*100/fulldays[mon] # change output from counting days to %
        }
        ofile[,14]<-ofile[,14]*100/365 # change output from counting days to %
        write.table(round(ofile,2),file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
        
        namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
        if(sum(is.na(ofile[,14]))>=(yeare-years+1-10)){
            betahat<-NA
            betastd<-NA
            pvalue<-NA
        }
        else{
            fit1<-lsfit(ofile[,1],ofile[,14])
            out1<-ls.print(fit1,print.it=F)
            pvalue<-round(as.numeric(out1$summary[1,6]),3)
            betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
            betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
        }
        cat(file=namt,paste(latitude,longitude,i,years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
        
        nam2<-paste(outjpgdir,paste(ofilename,"_",toupper(i),".jpg",sep=""),sep="/")
        jpeg(file=nam2,width=1024,height=768)
        plotx(ofile[,1],ofile[,14],main=paste(toupper(i),ofilename,sep="   "),ylab=toupper(i),xlab="Year")
        dev.off()
    }
}
#----------- exceedance ends -----------------------------------------  

#----------- index641cdd -----------------------------------------  
index641cdd<-function(){
    ys<-yeare-years+1
    cdd<-rep(0,ys)
    year<-c(years:yeare)
    target<-as.data.frame(cbind(year,cdd))
    year=years
    for (i in 1:ys){
        mid<-dd[dd$year==year,"prcp"]
        #  mid<-mid[is.na(mid)==F]
        if(i==1) kk<-0
        mm<-0
        for(j in 1:length(mid)){
            if(mid[j]<1&is.na(mid[j])==F) kk<-kk+1
            else {
                if(mm<kk) mm<-kk
                kk<-0
            }
        }
        if(mm<kk){
            if(year==yeare) mm<-kk
            else
                if(dd[dd$year==year+1&dd$month==1&dd$day==1,"prcp"]>=1|is.na(dd[dd$year==year+1&dd$month==1&dd$day==1,"prcp"])==T) mm<-kk
                # in case whole year dry, the next year will have a CDD bigger than 365
                # then the CDD indice for current year should not be 0 but NA
                if(mm==0) mm<-NA
        }
        target[i,"cdd"]<-mm
        year=year+1
    }
    
    #for(i in 1:(ys-1))
    #  if(target[i,"cdd"]==0&target[i+1,"cdd"]>=365) target[i,"cdd"]<-NA
    
    target[,"cdd"]<-target[,"cdd"]+ynacor[,"ynapr>15"]
    nam1<-paste(outinddir,paste(ofilename,"_CDD.csv",sep=""),sep="/")
    write.table(target,file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
    
    namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
    if(sum(is.na(target[,"cdd"]))>=(yeare-years+1-10)){
        betahat<-NA
        betastd<-NA
        pvalue<-NA
    }
    else{
        fit1<-lsfit(target[,1],target[,"cdd"])
        out1<-ls.print(fit1,print.it=F)
        pvalue<-round(as.numeric(out1$summary[1,6]),3)
        betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
        betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
    }
    cat(file=namt,paste(latitude,longitude,"cdd",years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
    
    nam2<-paste(outjpgdir,paste(ofilename,"_CDD.jpg",sep=""),sep="/")
    jpeg(nam2,width=1024,height=768)
    plotx(target[,1],target[,2],main=paste("CDD",ofilename,sep="   "),xlab="Year",ylab="CDD")
    dev.off()
}
#----------- index641cdd ends -----------------------------------------  

#----------- index641cwd -----------------------------------------  
index641cwd<-function(){
    ys<-yeare-years+1
    cwd<-rep(0,ys)
    year<-years:yeare
    target<-as.data.frame(cbind(year,cwd))
    year=years
    for (i in 1:ys){
        mid<-dd[dd$year==year,"prcp"]
        #  mid<-mid[is.na(mid)==F]
        if(i==1) kk<-0
        mm<-0
        for(j in 1:length(mid)){
            if(mid[j]>=1&is.na(mid[j])==F) kk<-kk+1
            else {
                if(mm<kk) mm<-kk
                kk<-0
            }
        }
        if(mm<kk){
            if(year==yeare) mm<-kk
            else
                if(dd[dd$year==year+1&dd$month==1&dd$day==1,"prcp"]<1|is.na(dd[dd$year==year+1&dd$month==1&dd$day==1,"prcp"])==T) mm<-kk
        }
        
        target[i,"cwd"]<-mm
        year=year+1
    }
    target[,"cwd"]<-target[,"cwd"]+ynacor[,"ynapr>15"]
    nam1<-paste(outinddir,paste(ofilename,"_CWD.csv",sep=""),sep="/")
    write.table(target,file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
    
    namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
    if(sum(is.na(target[,"cwd"]))>=(yeare-years+1-10)){
        betahat<-NA
        betastd<-NA
        pvalue<-NA
    }
    else{
        fit1<-lsfit(target[,1],target[,"cwd"])
        out1<-ls.print(fit1,print.it=F)
        pvalue<-round(as.numeric(out1$summary[1,6]),3)
        betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
        betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
    }
    cat(file=namt,paste(latitude,longitude,"cwd",years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
    
    nam2<-paste(outjpgdir,paste(ofilename,"_CWD.jpg",sep=""),sep="/")
    jpeg(nam2,width=1024,height=768)
    plotx(target[,1],target[,2],main=paste("CWD",ofilename,sep="   "),xlab="Year",ylab="CWD")
    dev.off()
}
#----------- index641cwd ends -----------------------------------------  

#----------- rx1d -----------------------------------------  
rx1d<-function(){
    len<-yeare-years+1
    aa1<-matrix(NA,12*len,3)
    dimnames(aa1)<-list(NULL,c("year","month","rx1d"))
    aa1[,"year"]<-years:yeare
    aa1[,"year"]<-mysort(aa1[,"year"],decreasing=F)
    aa1[,"month"]<-1:12
    jjj3=1
    mid<-dd[,1:4]
    for (year in years:yeare){
        aaaa<-mid[mid$year==year,]
        aaaam1<-aaaa[aaaa$month==1,"prcp"];        aaaam2<-aaaa[aaaa$month==2,"prcp"]
        aaaam3<-aaaa[aaaa$month==3,"prcp"];        aaaam4<-aaaa[aaaa$month==4,"prcp"]
        aaaam5<-aaaa[aaaa$month==5,"prcp"];        aaaam6<-aaaa[aaaa$month==6,"prcp"]
        aaaam7<-aaaa[aaaa$month==7,"prcp"];        aaaam8<-aaaa[aaaa$month==8,"prcp"]
        aaaam9<-aaaa[aaaa$month==9,"prcp"];        aaaam10<-aaaa[aaaa$month==10,"prcp"]
        aaaam11<-aaaa[aaaa$month==11,"prcp"];      aaaam12<-aaaa[aaaa$month==12,"prcp"]
        aa1[jjj3,"rx1d"]<-max(aaaam1,na.rm=T);     aa1[jjj3+1,"rx1d"]<-max(aaaam2,na.rm=T)
        aa1[jjj3+2,"rx1d"]<-max(aaaam3,na.rm=T);   aa1[jjj3+3,"rx1d"]<-max(aaaam4,na.rm=T)
        aa1[jjj3+4,"rx1d"]<-max(aaaam5,na.rm=T);   aa1[jjj3+5,"rx1d"]<-max(aaaam6,na.rm=T)
        aa1[jjj3+6,"rx1d"]<-max(aaaam7,na.rm=T);   aa1[jjj3+7,"rx1d"]<-max(aaaam8,na.rm=T)
        aa1[jjj3+8,"rx1d"]<-max(aaaam9,na.rm=T);   aa1[jjj3+9,"rx1d"]<-max(aaaam10,na.rm=T)
        aa1[jjj3+10,"rx1d"]<-max(aaaam11,na.rm=T); aa1[jjj3+11,"rx1d"]<-max(aaaam12,na.rm=T)
        jjj3=jjj3+12}
    aa1[,"rx1d"]<-aa1[,"rx1d"]+nacor[,"mnapr>3"]
    ofile<-matrix(0,len,14)
    #    aa1<-as.data.frame(aa1)
    dimnames(ofile)<-list(NULL,c("year","jan","feb","mar","apr","may","jun","jul","aug","sep","oct","nov","dec","annual"))
    ofile<-as.data.frame(ofile)
    for(j in years:yeare){
        k<-j-years+1
        ofile[k,1]<-j
        ofile[k,2:13]<-aa1[aa1[,1]==j,3]
        ofile[k,14]<-max(ofile[k,2:13],na.rm=F)
    }
    nam1<-paste(outinddir,paste(ofilename,"_RX1day.csv",sep=""),sep="/")
    write.table(ofile,file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
    
    namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
    if(sum(is.na(ofile[,14]))>=(yeare-years+1-10)){
        betahat<-NA
        betastd<-NA
        pvalue<-NA
    }
    else{
        fit1<-lsfit(ofile[,1],ofile[,14])
        out1<-ls.print(fit1,print.it=F)
        pvalue<-round(as.numeric(out1$summary[1,6]),3)
        betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
        betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
    }
    cat(file=namt,paste(latitude,longitude,"rx1day",years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
    
    nam2<-paste(outjpgdir,paste(ofilename,"_RX1day.jpg",sep=""),sep="/")
    jpeg(nam2,width=1024,height=768)
    plotx(ofile[,1],ofile[,14],main=paste("RX1day",ofilename,sep="   "),xlab="Year",ylab="RX1day")
    dev.off()
}
#----------- rx1d ends -----------------------------------------  

#----------- rx5d -----------------------------------------  
rx5d<-function(){
    a2<-c(0,0,0,0)
    a1<-dd[,"prcp"]
    a1<-append(a2,a1)
    n<-length(a1)
    a<-rep(0,n)
    for (i in 5:n){
        a[i]<-sum(a1[(i-4):i],na.rm=T)}
    a<-a[-(1:4)]
    a<-cbind(dd[,1:2],a)
    
    len<-yeare-years+1
    aa1<-matrix(NA,12*len,3)
    dimnames(aa1)<-list(NULL,c("year","month","rx5d"))
    aa1[,"year"]<-years:yeare
    aa1[,"year"]<-mysort(aa1[,"year"],decreasing=F)
    aa1[,"month"]<-1:12
    jjj2=1
    for (year in years:yeare){
        aaaa<-a[a$year==year,]
        aaaam1<-aaaa[aaaa$month==1,"a"];           aaaam2<-aaaa[aaaa$month==2,"a"]
        aaaam3<-aaaa[aaaa$month==3,"a"];           aaaam4<-aaaa[aaaa$month==4,"a"]
        aaaam5<-aaaa[aaaa$month==5,"a"];           aaaam6<-aaaa[aaaa$month==6,"a"]
        aaaam7<-aaaa[aaaa$month==7,"a"];           aaaam8<-aaaa[aaaa$month==8,"a"]
        aaaam9<-aaaa[aaaa$month==9,"a"];           aaaam10<-aaaa[aaaa$month==10,"a"]
        aaaam11<-aaaa[aaaa$month==11,"a"];         aaaam12<-aaaa[aaaa$month==12,"a"]
        aa1[jjj2,"rx5d"]<-max(aaaam1,na.rm=T);     aa1[jjj2+1,"rx5d"]<-max(aaaam2,na.rm=T)
        aa1[jjj2+2,"rx5d"]<-max(aaaam3,na.rm=T);   aa1[jjj2+3,"rx5d"]<-max(aaaam4,na.rm=T)
        aa1[jjj2+4,"rx5d"]<-max(aaaam5,na.rm=T);   aa1[jjj2+5,"rx5d"]<-max(aaaam6,na.rm=T)
        aa1[jjj2+6,"rx5d"]<-max(aaaam7,na.rm=T);   aa1[jjj2+7,"rx5d"]<-max(aaaam8,na.rm=T)
        aa1[jjj2+8,"rx5d"]<-max(aaaam9,na.rm=T);   aa1[jjj2+9,"rx5d"]<-max(aaaam10,na.rm=T)
        aa1[jjj2+10,"rx5d"]<-max(aaaam11,na.rm=T); aa1[jjj2+11,"rx5d"]<-max(aaaam12,na.rm=T)
        jjj2=jjj2+12}
    
    aa1[,"rx5d"]<-aa1[,"rx5d"]+nacor[,"mnapr>3"]
    
    ofile<-matrix(0,len,14)
    #    aa1<-as.data.frame(aa1)
    dimnames(ofile)<-list(NULL,c("year","jan","feb","mar","apr","may","jun","jul","aug","sep","oct","nov","dec","annual"))
    ofile<-as.data.frame(ofile)
    for(j in years:yeare){
        k<-j-years+1
        ofile[k,1]<-j
        ofile[k,2:13]<-aa1[aa1[,1]==j,"rx5d"]
        ofile[k,14]<-max(ofile[k,2:13],na.rm=F)
    }
    #    print(dim(rx5d))
    nam1<-paste(outinddir,paste(ofilename,"_RX5day.csv",sep=""),sep="/")
    write.table(ofile,file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
    
    namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
    if(sum(is.na(ofile[,14]))>=(yeare-years+1-10)){
        betahat<-NA
        betastd<-NA
        pvalue<-NA
    }
    else{
        fit1<-lsfit(ofile[,1],ofile[,14])
        out1<-ls.print(fit1,print.it=F)
        pvalue<-round(as.numeric(out1$summary[1,6]),3)
        betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
        betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
    }
    cat(file=namt,paste(latitude,longitude,"rx5day",years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
    
    nam2<-paste(outjpgdir,paste(ofilename,"_RX5day.jpg",sep=""),sep="/")
    jpeg(nam2,width=1024,height=768)
    plotx(ofile[,1],ofile[,14],main=paste("RX5day",ofilename,sep="   "),xlab="Year",ylab="RX5day")
    dev.off()
}
#----------- rx5d ends -----------------------------------------  

#----------- extremedaytem -----------------------------------------  
extremedaytem<-function(){
    len<-yeare-years+1
    aa1<-matrix(NA,12*len,6)
    dimnames(aa1)<-list(NULL,c("year","month","txx","txn","tnx","tnn"))
    aa1[,"year"]<-years:yeare
    aa1[,"year"]<-mysort(aa1[,"year"],decreasing=F)
    aa1[,"month"]<-1:12
    jjj4=1
    for (year in years:yeare){
        aaaa<-dd[dd$year==year,]
        aaaama1<-aaaa[aaaa$month==1,"tmax"];   aaaama2<-aaaa[aaaa$month==2,"tmax"]
        aaaama3<-aaaa[aaaa$month==3,"tmax"];   aaaama4<-aaaa[aaaa$month==4,"tmax"]
        aaaama5<-aaaa[aaaa$month==5,"tmax"];   aaaama6<-aaaa[aaaa$month==6,"tmax"]
        aaaama7<-aaaa[aaaa$month==7,"tmax"];   aaaama8<-aaaa[aaaa$month==8,"tmax"]
        aaaama9<-aaaa[aaaa$month==9,"tmax"];   aaaama10<-aaaa[aaaa$month==10,"tmax"]
        aaaama11<-aaaa[aaaa$month==11,"tmax"]; aaaama12<-aaaa[aaaa$month==12,"tmax"]
        
        aaaami1<-aaaa[aaaa$month==1,"tmin"];   aaaami2<-aaaa[aaaa$month==2,"tmin"]
        aaaami3<-aaaa[aaaa$month==3,"tmin"];   aaaami4<-aaaa[aaaa$month==4,"tmin"]
        aaaami5<-aaaa[aaaa$month==5,"tmin"];   aaaami6<-aaaa[aaaa$month==6,"tmin"]
        aaaami7<-aaaa[aaaa$month==7,"tmin"];   aaaami8<-aaaa[aaaa$month==8,"tmin"]
        aaaami9<-aaaa[aaaa$month==9,"tmin"];   aaaami10<-aaaa[aaaa$month==10,"tmin"]
        aaaami11<-aaaa[aaaa$month==11,"tmin"]; aaaami12<-aaaa[aaaa$month==12,"tmin"]
        
        aa1[jjj4,"txx"]<-max(aaaama1,na.rm=T);     aa1[jjj4+1,"txx"]<-max(aaaama2,na.rm=T)
        aa1[jjj4+2,"txx"]<-max(aaaama3,na.rm=T);   aa1[jjj4+3,"txx"]<-max(aaaama4,na.rm=T)
        aa1[jjj4+4,"txx"]<-max(aaaama5,na.rm=T);   aa1[jjj4+5,"txx"]<-max(aaaama6,na.rm=T)
        aa1[jjj4+6,"txx"]<-max(aaaama7,na.rm=T);   aa1[jjj4+7,"txx"]<-max(aaaama8,na.rm=T)
        aa1[jjj4+8,"txx"]<-max(aaaama9,na.rm=T);   aa1[jjj4+9,"txx"]<-max(aaaama10,na.rm=T)
        aa1[jjj4+10,"txx"]<-max(aaaama11,na.rm=T); aa1[jjj4+11,"txx"]<-max(aaaama12,na.rm=T)
        
        aa1[jjj4,"txn"]<-min(aaaama1,na.rm=T);     aa1[jjj4+1,"txn"]<-min(aaaama2,na.rm=T)
        aa1[jjj4+2,"txn"]<-min(aaaama3,na.rm=T);   aa1[jjj4+3,"txn"]<-min(aaaama4,na.rm=T)
        aa1[jjj4+4,"txn"]<-min(aaaama5,na.rm=T);   aa1[jjj4+5,"txn"]<-min(aaaama6,na.rm=T)
        aa1[jjj4+6,"txn"]<-min(aaaama7,na.rm=T);   aa1[jjj4+7,"txn"]<-min(aaaama8,na.rm=T)
        aa1[jjj4+8,"txn"]<-min(aaaama9,na.rm=T);   aa1[jjj4+9,"txn"]<-min(aaaama10,na.rm=T)
        aa1[jjj4+10,"txn"]<-min(aaaama11,na.rm=T); aa1[jjj4+11,"txn"]<-min(aaaama12,na.rm=T)
        
        aa1[jjj4,"tnx"]<-max(aaaami1,na.rm=T);     aa1[jjj4+1,"tnx"]<-max(aaaami2,na.rm=T)
        aa1[jjj4+2,"tnx"]<-max(aaaami3,na.rm=T);   aa1[jjj4+3,"tnx"]<-max(aaaami4,na.rm=T)
        aa1[jjj4+4,"tnx"]<-max(aaaami5,na.rm=T);   aa1[jjj4+5,"tnx"]<-max(aaaami6,na.rm=T)
        aa1[jjj4+6,"tnx"]<-max(aaaami7,na.rm=T);   aa1[jjj4+7,"tnx"]<-max(aaaami8,na.rm=T)
        aa1[jjj4+8,"tnx"]<-max(aaaami9,na.rm=T);   aa1[jjj4+9,"tnx"]<-max(aaaami10,na.rm=T)
        aa1[jjj4+10,"tnx"]<-max(aaaami11,na.rm=T); aa1[jjj4+11,"tnx"]<-max(aaaami12,na.rm=T)
        
        aa1[jjj4,"tnn"]<-min(aaaami1);aa1[jjj4+1,"tnn"]<-min(aaaami2,na.rm=T)
        aa1[jjj4+2,"tnn"]<-min(aaaami3,na.rm=T);   aa1[jjj4+3,"tnn"]<-min(aaaami4,na.rm=T)
        aa1[jjj4+4,"tnn"]<-min(aaaami5,na.rm=T);   aa1[jjj4+5,"tnn"]<-min(aaaami6,na.rm=T)
        aa1[jjj4+6,"tnn"]<-min(aaaami7,na.rm=T);   aa1[jjj4+7,"tnn"]<-min(aaaami8,na.rm=T)
        aa1[jjj4+8,"tnn"]<-min(aaaami9,na.rm=T);   aa1[jjj4+9,"tnn"]<-min(aaaami10,na.rm=T)
        aa1[jjj4+10,"tnn"]<-min(aaaami11,na.rm=T); aa1[jjj4+11,"tnn"]<-min(aaaami12,na.rm=T)
        
        jjj4=jjj4+12}
    exdaytem<-as.data.frame(aa1)
    #    midnacor<-nacor[nacor$year>=startyear,]
    #    midnacor<-midnacor[midnacor$year<=endyear,]
    exdaytem[,"txx"]<-exdaytem[,"txx"]+nacor[,"mnatma>3"]
    exdaytem[,"txn"]<-exdaytem[,"txn"]+nacor[,"mnatma>3"]
    exdaytem[,"tnx"]<-exdaytem[,"tnx"]+nacor[,"mnatmi>3"]
    exdaytem[,"tnn"]<-exdaytem[,"tnn"]+nacor[,"mnatmi>3"]
    
    for(i in c("txx","txn","tnx","tnn")){
        if (i=="txx") {ii<-"_TXx.csv";ij<-"_TXx.jpg";kk<-3;ik<-1;ki<-3}# ik=1, take max as yearly record
        if (i=="txn") {ii<-"_TXn.csv";ij<-"_TXn.jpg";kk<-4;ik<-0;ki<-3}# ik=0, take min as yearly record
        if (i=="tnx") {ii<-"_TNx.csv";ij<-"_TNx.jpg";kk<-5;ik<-1;ki<-4}# ki=3, take TMAX annual missing values
        if (i=="tnn") {ii<-"_TNn.csv";ij<-"_TNn.jpg";kk<-6;ik<-0;ki<-4}# ki=4, take TMIN annual missing values
        nam<-paste(outinddir,paste(ofilename,ii,sep=""),sep="/")
        ofile<-matrix(0,len,14)
        #    ojpg<-rep(0,len)
        dimnames(ofile)<-list(NULL,c("year","jan","feb","mar","apr","may","jun","jul","aug","sep","oct","nov","dec","annual"))
        ofile<-as.data.frame(ofile)
        for(j in years:yeare){
            k<-j-years+1
            ofile[k,1]<-j
            ofile[k,2:13]<-exdaytem[exdaytem$year==j,kk]
            if(ik==1) ofile[k,14]<-max(ofile[k,2:13],na.rm=T)
            else ofile[k,14]<-min(ofile[k,2:13],na.rm=T)
        }
        ofile[,14]<-ofile[,14]+ynacor[,ki]
        write.table(ofile,file=nam,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
        
        namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
        if(sum(is.na(ofile[,14]))>=(yeare-years+1-10)){
            betahat<-NA
            betastd<-NA
            pvalue<-NA
        }
        else{
            fit1<-lsfit(ofile[,1],ofile[,14])
            out1<-ls.print(fit1,print.it=F)
            pvalue<-round(as.numeric(out1$summary[1,6]),3)
            betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
            betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
        }
        cat(file=namt,paste(latitude,longitude,i,years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
        
        nam2<-paste(outjpgdir,paste(ofilename,ij,sep=""),sep="/")
        jpeg(nam2,width=1024,height=768)
        plotx(ofile[,1],ofile[,14],main=paste(toupper(i),ofilename,sep="   "),xlab="Year",ylab=(paste(toupper(substr(i,1,2)),substr(i,3,3),sep="")))
        dev.off()
    }
}
#----------- extremedaytem ends -----------------------------------------  

#----------- index646 -----------------------------------------  
index646<-function(){
    ys=yeare-years+1
    b<-matrix(0,ncol=2,nrow=ys)
    dimnames(b)<-list(NULL,c("year","sdii"))
    b[,"year"]<-c(years:yeare)
    b<-as.data.frame(b)
    year=years
    for (i in 1:ys){
        mid<-dd[dd$year==year,"prcp"]
        mid<-mid[mid>=1]
        b[i,"sdii"]<-mean(mid,na.rm=T)
        year=year+1  }
    b[,"sdii"]<-b[,"sdii"]+ynacor[,"ynapr>15"]
    nam1<-paste(outinddir,paste(ofilename,"_SDII.csv",sep=""),sep="/")
    write.table(round(b,digit=1),file=nam1,append=F,quote=F,sep=", ",na="-99.9",row.names=F)
    
    namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
    if(sum(is.na(b[,"sdii"]))>=(yeare-years+1-10)){
        betahat<-NA
        betastd<-NA
        pvalue<-NA
    }
    else{
        fit1<-lsfit(b[,"year"],b[,"sdii"])
        out1<-ls.print(fit1,print.it=F)
        pvalue<-round(as.numeric(out1$summary[1,6]),3)
        betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)
        betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)
    }
    cat(file=namt,paste(latitude,longitude,"sdii",years,yeare,betahat,betastd,pvalue,sep=","),fill=180,append=T)
    
    nam2<-paste(outjpgdir,paste(ofilename,"_SDII.jpg",sep=""),sep="/")
    jpeg(nam2,width=1024,height=768)
    plotx(b[,1],b[,2],main=paste("SDII",ofilename,sep="   "),xlab="Year",ylab="SDII")
    dev.off()
}
#----------- index646 ends -----------------------------------------  

#----------- main1 -----------------------------------------  
main1<-function(){
    main<-tktoplevel()
    tkfocus(main)
    tkwm.title(main,"Calculating Climate Indices")
    tkgrid(tklabel(main,text="Check desired indices",font=fontHeading1))
    txt="It may take 5 minutes to compute all the indices, "
    #	if(prallna==1) tkgrid(tklabel(main, text="PRCP all missing, indices related to PRCP may not be calculated",font=fontHeading2))
    #	if(txallna==1|tnallna==1) tkgrid(tklabel(main,text="TMAX or TMIN all missing, indices related to TMAX and TMIN may not be calculated",font=fontHeading2))
    
    #cb0 <- tkcheckbutton(main);cb0Val <- cbvalue0
    
    cb1 <- tkcheckbutton(main);  cb1Val <- cbvalue1
    cb2 <- tkcheckbutton(main);  cb2Val <- cbvalue2
    cb3 <- tkcheckbutton(main);  cb3Val <- cbvalue3
    cb4 <- tkcheckbutton(main);  cb4Val <- cbvalue4
    cb5 <- tkcheckbutton(main);  cb5Val <- cbvalue5
    cb6 <- tkcheckbutton(main);  cb6Val <- cbvalue6
    cb7 <- tkcheckbutton(main);  cb7Val <- cbvalue7
    cb8 <- tkcheckbutton(main);  cb8Val <- cbvalue8
    cb9 <- tkcheckbutton(main);  cb9Val <- cbvalue9
    cb10 <- tkcheckbutton(main); cb10Val <- cbvalue10
    cb11 <- tkcheckbutton(main); cb11Val <- cbvalue11
    cb12 <- tkcheckbutton(main); cb12Val <- cbvalue12
    cb13 <- tkcheckbutton(main); cb13Val <- cbvalue13
    cb14 <- tkcheckbutton(main); cb14Val <- cbvalue14
    cb15 <- tkcheckbutton(main); cb15Val <- cbvalue15
    cb21 <- tkcheckbutton(main); cb21Val <- cbvalue21
    
    #tkconfigure(cb0,variable=cb0Val)#,value=cb1Val)
    tkconfigure(cb1,variable=cb1Val)#,value=cb1Val)
    tkconfigure(cb2,variable=cb2Val)#,value=cb2Val)
    tkconfigure(cb3,variable=cb3Val)#,value=cb3Val)#"dtr")
    tkconfigure(cb4,variable=cb4Val)#,value=cb4Val)#"daysprcp10")
    tkconfigure(cb5,variable=cb5Val)#,value=cb5Val)#"nordaytem1")
    tkconfigure(cb6,variable=cb6Val)#,value=cb6Val)#"extremedays")
    tkconfigure(cb7,variable=cb7Val)#,value=cb7Val)#"exceedance")
    tkconfigure(cb8,variable=cb8Val)#,value=cb8Val)#"ind144hwd")
    tkconfigure(cb9,variable=cb9Val)#,value=cb9Val)#"ind641cdd")
    tkconfigure(cb10,variable=cb10Val)#,value=cb10Val)#"rx5d")
    tkconfigure(cb11,variable=cb11Val)#,value=cb11Val)#"ind646")
    tkconfigure(cb12,variable=cb12Val)#,value=cb12Val)#"ind695")
    tkconfigure(cb13,variable=cb13Val)#,value=cb13Val)#"rx1d"
    tkconfigure(cb14,variable=cb14Val)#,value=cb14Val)#"extreme day tem"
    tkconfigure(cb15,variable=cb15Val)
    tkconfigure(cb21,variable=cb21Val)
    
    #tkgrid(tklabel(main,text="Select the indices you want to calculate:"))
    
    #tkgrid(tklabel(main,text="ALL 26 indices!!"),cb0)
    tkgrid(tklabel(main,text="SU25, FD0, TR20, ID0"),cb1)
    tkgrid(tklabel(main,text="User Defined SU, FD, TR, ID"),cb21)
    
    tkgrid(tklabel(main,text="GSL, growing season length"),cb2)#143
    
    tkgrid(tklabel(main,text="TXx, TXn, TNx, TNn"),cb3)
    #tkgrid(tklabel(main,text="TXn, TNx, TNn, following by same choice"),cb3)#extremedaytem
    
    tkgrid(tklabel(main,text="TX10p, TX90p, TN10p, TN90p"),cb4)
    #tkgrid(tklabel(main,text="TX90p, TN10p, TN90p, following by same choice"),cb4)#exceedance
    
    tkgrid(tklabel(main,text="WSDI"),cb5)#hwfi
    tkgrid(tklabel(main,text="CSDI"),cb6)#cwdi
    
    #tkgrid(tklabel(main,text="Normal day temperature with user defined window size"),cb2)
    tkgrid(tklabel(main,text="DTR"),cb7)#dtr
    
    tkgrid(tklabel(main,text="Rx1day"),cb8)#rx1d
    tkgrid(tklabel(main,text="Rx5day"),cb9)#rx5d
    
    tkgrid(tklabel(main,text="SDII"),cb10)#index646
    
    tkgrid(tklabel(main,text="R10mm"),cb11)#daysprcp10()
    tkgrid(tklabel(main,text="R20mm"),cb12)#daysprcp20()
    tkgrid(tklabel(main,text="Rnnmm"),cb13)#daysprcpn()
    
    tkgrid(tklabel(main,text="CDD, CWD"),cb14)
    #tkgrid(tklabel(main,text="CWD"),cb14)#641
    
    #tkgrid(tklabel(main,text="daynortem     "),cb5)
    #tkgrid(tklabel(main,text="ind144hwd      "),cb8)#144
    
    tkgrid(tklabel(main,text="R95p, R99p, PRCPTOT"),cb15)#695
    #tkgrid(tklabel(main,text="R99pTOT"),cb15)
    
    #tkgrid(tklabel(main,text="Annual Days with PRCP>=95 percentile"),cb17)#r95ptot
    #tkgrid(tklabel(main,text="Annual Days with PRCP>=99 percentile"),cb17)
    
    #----------- OnOK -----------------------------------------  
    OnOK <- function(){
        #filename<-tclvalue(tkgetSaveFile(filetypes="{{EXCEL Files} {.csv}} {{All files} *}"))
        #if (!nchar(filename))
        #tkmessageBox(message="No file was selected!")
        #else tkmessageBox(message=paste("The results will be saved under",filename))
        #nam<-substr(filename,start=1,stop=(nchar(filename)-4))
        #assign("nam",nam,envir=.GlobalEnv)
        
        #    cbv0 <- as.character(tclvalue(cb0Val))
        cbv1 <- as.character(tclvalue(cb1Val))
        cbv21 <- as.character(tclvalue(cb21Val))
        cbv2 <- as.character(tclvalue(cb2Val))
        cbv3 <- as.character(tclvalue(cb3Val))
        cbv4 <- as.character(tclvalue(cb4Val))
        cbv5 <- as.character(tclvalue(cb5Val))
        cbv6 <- as.character(tclvalue(cb6Val))
        cbv7 <- as.character(tclvalue(cb7Val))
        cbv8 <- as.character(tclvalue(cb8Val))
        cbv9 <- as.character(tclvalue(cb9Val))
        cbv10 <- as.character(tclvalue(cb10Val))
        cbv11 <- as.character(tclvalue(cb11Val))
        cbv12 <- as.character(tclvalue(cb12Val))
        cbv13 <- as.character(tclvalue(cb13Val))
        cbv14 <- as.character(tclvalue(cb14Val))
        cbv15 <- as.character(tclvalue(cb15Val))
        tkdestroy(main)
        #    if (cbv0==1) {cbv1<-1;cbv2<-1;cbv3<-1;cbv4<-1;cbv5<-1;
        namt<-paste(outtrddir,paste(ofilename,"_trend.csv",sep=""),sep="/")
        cat(file=namt,paste("Lon","Lat","Indices","SYear","EYear","Slope","STD_of_Slope","P_Value",sep=","),fill=180,append=F)
        if (cbv1==1) if(txallna==0&tnallna==0) extremedays()
        if (cbv21==1) if(txallna==0&tnallna==0) extremedays(opt=1)
        if (cbv2==1) if(txallna==0&tnallna==0) ind143gsl()
        if (cbv3==1) if(txallna==0&tnallna==0) extremedaytem()
        if (cbv4==1) if(txallna==0&tnallna==0) exceedance()
        if (cbv5==1) if(txallna==0) hwfi()
        if (cbv6==1) if(tnallna==0) cwdi()
        if (cbv7==1) if(txallna==0&tnallna==0) dtr()
        if (cbv8==1) if(prallna==0) rx1d()
        if (cbv9==1) if(prallna==0) rx5d()
        if (cbv10==1) if(prallna==0) index646()
        if (cbv11==1) if(prallna==0) daysprcp10()
        if (cbv12==1) if(prallna==0) daysprcp20()
        if (cbv13==1) if(prallna==0) daysprcpn()
        if (cbv14==1) if(prallna==0) {index641cdd();index641cwd()}
        if (cbv15==1) if(prallna==0) r95ptot()
        cbvalue1<-cb1Val;assign("cbvalue1",cbvalue1,envir=.GlobalEnv)
        cbvalue2<-cb2Val;assign("cbvalue2",cbvalue2,envir=.GlobalEnv)
        cbvalue3<-cb3Val;assign("cbvalue3",cbvalue3,envir=.GlobalEnv)
        cbvalue4<-cb4Val;assign("cbvalue4",cbvalue4,envir=.GlobalEnv)
        cbvalue5<-cb5Val;assign("cbvalue5",cbvalue5,envir=.GlobalEnv)
        cbvalue6<-cb6Val;assign("cbvalue6",cbvalue6,envir=.GlobalEnv)
        cbvalue7<-cb7Val;assign("cbvalue7",cbvalue7,envir=.GlobalEnv)
        cbvalue8<-cb8Val;assign("cbvalue8",cbvalue8,envir=.GlobalEnv)
        cbvalue9<-cb9Val;assign("cbvalue9",cbvalue9,envir=.GlobalEnv)
        cbvalue10<-cb10Val;assign("cbvalue10",cbvalue10,envir=.GlobalEnv)
        cbvalue11<-cb11Val;assign("cbvalue11",cbvalue11,envir=.GlobalEnv)
        cbvalue12<-cb12Val;assign("cbvalue12",cbvalue12,envir=.GlobalEnv)
        cbvalue13<-cb13Val;assign("cbvalue13",cbvalue13,envir=.GlobalEnv)
        cbvalue14<-cb14Val;assign("cbvalue14",cbvalue14,envir=.GlobalEnv)
        cbvalue15<-cb15Val;assign("cbvalue15",cbvalue15,envir=.GlobalEnv)
        cbvalue21<-cb21Val;assign("cbvalue21",cbvalue21,envir=.GlobalEnv)
        nstation<-tktoplevel()
        tkwm.title(nstation,"Calculation Done")
        tkfocus(nstation)
        okk<-function(){tkdestroy(nstation);tkfocus(start1)}
        textlabel0<-tklabel(nstation,text="     ")
        textlabel1<-tklabel(nstation,text="Indices calculation completed",font=fontHeading1)
        textlabel2<-tklabel(nstation,text=paste("Plots are in: ",outjpgdir,sep=" "),font=fontHeading1)
        okk.but<-tkbutton(nstation,text="   OK   ",command=okk,width=20)
        tkgrid(textlabel0)
        tkgrid(textlabel1)
        tkgrid(textlabel2)
        tkgrid.configure(textlabel1,sticky="w")
        tkgrid.configure(textlabel2,sticky="w")
        tkgrid.configure(textlabel0,sticky="e")
        #    cancell.but<-tkbutton(nstation,text="   NO   ",command=cancell,width=15)
        #    tkgrid(textlabel2,okk.but,cancell.but,textlabel0)
        tkgrid(okk.but,textlabel0)
        #    tkgrid.configure(cancell.but,sticky="w")
        tkgrid(textlabel0)
    }
    #----------- OnOK ends -----------------------------------------  
    
    #----------- done2 -----------------------------------------  
    done2<-function(){
        tkdestroy(main)
        tkfocus(start1)
        #  return()
    }
    #----------- done2 ends -----------------------------------------  
    
    ok.but <-tkbutton(main,text="   OK   ",command=OnOK,width=30)
    cancel.but<-tkbutton(main,text="CANCEL",command=done2,width=30)
    tkgrid(ok.but)
    tkgrid(cancel.but)
    tkgrid(tklabel(main,text="It may take more than 5 minutes to compute all the indices",font=fontHeading2))
    tkgrid(tklabel(main,text="Please be patient, you will be informed once computations are done",font=fontHeading2))
    tkgrid(tklabel(main,text="",font=fontHeading))#empty line
    
}
#----------- main1 ends ----------------------------------------- 
# End of Part II ( Functions of Calculating climate indecies )
##################################################################################


##################################################################################
# Part III
# Main program
# call getfile, read data file and store in dd.
##################################################################################
start1<-tktoplevel()
#----------- plotx -----------------------------------------  
plotx<-
    function (x,y,main="",xlab="",ylab="")
    {
        plot(x,y,xlab=xlab,ylab=ylab,type="b")
        fit<-lsfit(x,y)
        out<-ls.print(fit,print.it=F)
        r2<-round(100*as.numeric(out$summary[1,2]),1)
        pval<-round(as.numeric(out$summary[1,6]),3)
        beta<-round(as.numeric(out$coef.table[[1]][2,1]),3)
        betaerr<-round(as.numeric(out$coef.table[[1]][2,2]),3)
        abline(fit,lwd=3)
        xy<-cbind(x,y)
        xy<-na.omit(xy)
        lines(lowess(xy[,1],xy[,2]),lwd=3,lty=2)
        subtit=paste("R2=",r2," p-value=",pval," Slope estimate=",beta," Slope error=",betaerr)
        title(main=main)
        title(sub=subtit,cex=0.5)
    }
#----------- plotx ends -----------------------------------------  

#----------- ts -----------------------------------------  
ts<-function(ys=x[1,1],ye=x[nrow(x),1],x=dd) 
{
    #
    # EDA function
    #
    par(mfrow=c(3,1))
    xs<-x[(x[,1]>=ys)&(x[,1]<=ye),]
    ts.plot(xs[,4])
    title("Daily precipitation",xlab="day",ylab="precip")
    ts.plot(xs[,5])
    title("Daily maximum temperature",xlab="day",ylab="tmax")
    ts.plot(xs[,6])
    title("Daily minimum temperature",xlab="day",ylab="tmin")
    par(mfrow=c(1,1))
    
    cat(paste("Station series defined from ",x[1,1]," to ",x[nrow(x),1],"\n"))
    cat(paste("Summary statistics for window from ",ys," to ", ye,"\n"))
}
#----------- ts ends -----------------------------------------  

#----------- ?????????? -----------------------------------------  
function (y1=x[1,1],y2=dd[nrow(x),1],m=1,v=4,x=dd) 
{
    #
    # Little function to see how many NAs there are in month m
    # in period y1 to y2 for variable v:
    # 
    # Usage: nas(1960,1990,1,4)
    #
    
    xs<-x[(x[,1]>=y1)&(x[,1]<=y2)&(x[,2]==m),]
    cat(paste("Years from ",y1,"-",y2," Month=",m," Variable=",v,"\n"))
    cat(paste("Total number of days=",length(xs[,v])," Total missing=",sum(is.na(xs[,v])),"\n"))
}
#----------- ?????? ends -----------------------------------------  
startss<-function(){
    tkwm.title(start1,"RclimDex")
    tkgrid(tklabel(start1,text="     RClimDex by Junior version BETA  ",font=fontHeading))
    tkgrid(tklabel(start1,text="",font=fontHeading))#empty line
    tkgrid(tklabel(start1,text="",font=fontHeading))#empty line
    #tkgrid(tklabel(start1,text="",font=fontHeading))#empty line
    start.but<-tkbutton(start1,text="Load Data and Run QC",command=getfile,width=30)
    cal.but<-tkbutton(start1,text=" Indices Calculation ",command=nastat,width=60)
    cancel.but<-tkbutton(start1,text="Exit",command=done,width=30)
    tkgrid(start.but)
    tkgrid(cal.but)
    tkgrid(cancel.but)
    tkgrid(tklabel(start1,text="",font=fontHeading))   
}#end of startss function
startss()
##################################################################################


```



.
</div>