El paquete RPisco contiene herramientas para manipular datos de precipitación y temperatura de PISCO (SENAMHI).
RPisco package contains tools for manipulate precipitation and temperature data of PISCO (SENAMHI, Peru).
library(RPisco)
1. Installation - Instalación
Instale el paquete RPisco desde Github:
Install RPisco package from Github:
> library(devtools)
> install_github("GeomarPerales/RPisco")
2. RPisco Package - Paquete RPisco
El paquete RPisco contiene las siguientes herramientas: extracción de valores de datos PISCO diarios y mensuales, procesar los datos de PISCO de las series diarias/mensuales a series anuales, y obtener el promedio mensual/anual de la serie PISCO mensual/anual.
RPisco package contains the following tools: values extraction of daily/monthly PISCO data, processing PISCO data from daily/monthly series to annual series, and obtain monthly/yearly average from PISCO monthly/yearly serie.
3. Precipitation - Precipitación
Daily data - Datos diarios
<- "D:\\PISCOd_pp_2.1.nc"
PISCO.file <- data.frame(PISCO.file, -76.11, -13.11)
x <- piscod(x) data
## Loading required namespace: ncdf4
head(data)
## date values
## 1 1981-01-01 0.000000000
## 2 1981-01-02 0.000000000
## 3 1981-01-03 0.000000000
## 4 1981-01-04 0.000000000
## 5 1981-01-05 0.051410247
## 6 1981-01-06 0.000000000
Monthly data - Datos Mensuales
<- "D:\\PISCOm_pp_2.1.nc"
PISCO.file <- data.frame(PISCO.file, -76.11, -13.11)
x <- piscom(x)
data head(data)
## date values
## 1 1981-01-01 2.9515
## 2 1981-02-01 1.2462
## 3 1981-03-01 0.8867
## 4 1981-04-01 0.8088
## 5 1981-05-01 0.2178
## 6 1981-06-01 0.0674
4. Temperature - Temperatura
Daily data - Datos diarios
<- "D:\\PISCOd_tmn_v1.1.nc"
PISCO.file <- data.frame(PISCO.file, -76.11, -13.11)
x <- piscod(x)
data head(data)
## date values
## 1 1981-01-01 16.7
## 2 1981-01-02 16.6
## 3 1981-01-03 17.1
## 4 1981-01-04 17.4
## 5 1981-01-05 18.0
## 6 1981-01-06 17.7
Monthly data - Datos Mensuales
<- "D:\\PISCOm_tmn_v1.1.nc"
PISCO.file <- data.frame(PISCO.file, -76.11, -13.11)
x <- piscom(x)
data head(data)
## date values
## 1 1981-01-01 18
## 2 1981-02-01 20
## 3 1981-03-01 20
## 4 1981-04-01 18
## 5 1981-05-01 14
## 6 1981-06-01 12
Daily data - Datos diarios
<- "D:\\PISCOd_tmx_v1.1.nc"
PISCO.file <- data.frame(PISCO.file, -76.11, -13.11)
x <- piscod(x)
data head(data)
## date values
## 1 1981-01-01 26
## 2 1981-01-02 29
## 3 1981-01-03 29
## 4 1981-01-04 29
## 5 1981-01-05 29
## 6 1981-01-06 28
Monthly data - Datos Mensuales
<- "D:\\PISCOm_tmx_v1.1.nc"
PISCO.file <- data.frame(PISCO.file, -76.11, -13.11)
x <- piscom(x)
data head(data)
## date values
## 1 1981-01-01 28
## 2 1981-02-01 30
## 3 1981-03-01 31
## 4 1981-04-01 29
## 5 1981-05-01 26
## 6 1981-06-01 22
5. Study Case: Huarpa Basin - Caso de estudio: Cuenca Huarpa
head(Huarpa.stations)
## Estacion Lat Lon Alt
## 1 San Miguel -13.01667 -73.98333 3323
## 2 Hacienda Cochas -13.03333 -73.88333 3323
## 3 La Quinua -13.05528 -74.14139 3240
## 4 Wayllapampa -13.07639 -74.21667 2470
## 5 Huamanga -13.15000 -74.23694 2761
## 6 Allpachaca -13.38333 -74.26667 3600
<- "D:\\PISCOm_pp_2.1.nc"
PISCO.file <- piscomgroup(data.frame(PISCO.file, Huarpa.stations))
monthly.data head(monthly.data)
## date San Miguel Hacienda Cochas La Quinua Wayllapampa Huamanga
## 1 1981-01-01 58.54567 74.77754 93.01641 117.48862 103.28103
## 2 1981-02-01 67.39442 78.87476 142.98962 241.88490 203.90121
## 3 1981-03-01 53.66131 70.89542 55.87140 67.15765 53.11654
## 4 1981-04-01 16.62957 14.74391 17.29029 20.51554 19.11527
## 5 1981-05-01 3.61790 2.32993 3.83066 3.84793 1.74525
## 6 1981-06-01 1.80981 1.73947 3.27024 3.17444 4.42622
## Allpachaca Huanta Huanta gore San Pedro Acobamba Lircay Hacienda Tocaz
## 1 67.24044 97.80492 97.80492 131.34431 207.50142 179.05977 239.64926
## 2 183.31203 198.75272 198.75272 293.97815 291.34100 271.49072 158.84775
## 3 40.27865 58.93501 58.93501 83.37255 177.26332 127.10260 286.79398
## 4 15.99107 18.41072 18.41072 34.34954 61.56691 51.79061 47.10588
## 5 1.26738 4.14175 4.14175 2.74853 12.98256 6.97344 18.35392
## 6 1.58454 2.15502 2.15502 3.42321 4.56641 8.77444 9.07320
## Paucarbamba Lauricocha Huancavelica Paras Chuschi Libertadores 706
## 1 239.64926 110.33208 159.00160 125.30540 105.03394 158.56030
## 2 158.84775 104.70095 295.38922 282.13904 222.13841 267.16232
## 3 286.79398 100.64695 196.82372 57.97153 58.07713 115.83591
## 4 47.10588 12.80632 67.83571 147.80380 63.94131 90.31709
## 5 18.35392 7.40672 3.90998 1.08554 1.11921 0.78201
## 6 9.07320 2.09332 11.15154 1.07318 0.52701 0.21010
## Libertadores 156139 Choclococha Acnococha Tunel cero
## 1 135.37398 204.01686 127.07281 127.07281
## 2 279.44202 333.16922 220.66913 220.66913
## 3 98.34023 158.01579 81.13100 81.13100
## 4 92.24104 67.72575 62.18458 62.18458
## 5 1.12915 0.39376 0.56113 0.56113
## 6 0.18477 0.50078 0.32104 0.32104
monthlyavg(data.frame(monthly.data$date, monthly.data[,2]))
## Jan Feb Mar Apr May Jun Jul Aug
## 1 54.94048 49.30801 53.42671 23.35738 8.446521 2.745969 2.196354 3.838493
## Sep Oct Nov Dec
## 1 11.91895 22.48625 31.13459 77.18325
yearlyavg(data.frame(monthly.data$date, monthly.data[,2]))
## [1] 340.983
head(pisco2annual(data.frame(monthly.data$date, monthly.data[,2])))
## date values
## 1 1981 369.0274
## 2 1982 315.3467
## 3 1983 271.1277
## 4 1984 234.0625
## 5 1985 342.9361
## 6 1986 370.9350
6. References - Referencias
Aybar. (2017). Uso del producto grillado PISCO de precipitación en estudios, investigaciones sistemas operacionales de monitoreo y pronóstico hidrometeorológico. Lima: SENAMHI.