RPisco

RPisco: tools for manipulate PISCO (SENAMHI, Peru) data

RPisco: Herramientas para manipular datos PISCO (SENAMHI, Perú)

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

PISCO.file <- "D:\\PISCOd_pp_2.1.nc"
x <- data.frame(PISCO.file, -76.11, -13.11)
data <- piscod(x)
## 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

PISCO.file <- "D:\\PISCOm_pp_2.1.nc"
x <- data.frame(PISCO.file, -76.11, -13.11)
data <- piscom(x)
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

PISCO.file <- "D:\\PISCOd_tmn_v1.1.nc"
x <- data.frame(PISCO.file, -76.11, -13.11)
data <- piscod(x)
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

PISCO.file <- "D:\\PISCOm_tmn_v1.1.nc"
x <- data.frame(PISCO.file, -76.11, -13.11)
data <- piscom(x)
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

PISCO.file <- "D:\\PISCOd_tmx_v1.1.nc"
x <- data.frame(PISCO.file, -76.11, -13.11)
data <- piscod(x)
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

PISCO.file <- "D:\\PISCOm_tmx_v1.1.nc"
x <- data.frame(PISCO.file, -76.11, -13.11)
data <- piscom(x)
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
PISCO.file <- "D:\\PISCOm_pp_2.1.nc"
monthly.data <- piscomgroup(data.frame(PISCO.file, Huarpa.stations))
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.