R标准化降水指数.nc文件 [英] R Standardized Precipitation Index .nc file

问题描述

我正在尝试根据CHIRPS的月平均降水量数据计算SPI，因为它太大了，因此将其缩减到我感兴趣的区域，这里是:

I'm trying to calculate the SPI from CHIRPS monthly mean precipitation data, because it's too large I cut it down to my area of interest and here it is: https://www.dropbox.com/s/jpwcg8j5bdc5gq6/chirps_mensual_v1.nc?dl=0 I did this to open it:

require(utils)
require(colorRamps)
require(RNetCDF)
require(rasterVis)
require(rgdal)
library(ncdf4)
library(raster)


datos2 <- nc_open("Datos/chirps_mensual_v1.nc")
ppt_array <- ncvar_get(datos2, "precip")

#I'm only taking complete years so I took out two months from 2018

ppt_mes <- ppt_array[ , ,1:444]

我知道有一个SPI库，但是我不知道如何格式化数据才能使用它.因此，我尝试通过拟合伽马分布来在没有功能的情况下执行此操作，但我不知道如何针对此数据库执行操作.

I know there is a SPI library but I don't know how should I format the data in order to use it. So I tried to do it without the function by fitting the gamma distribution but I dont' know how to do it for this data base.

有人知道如何通过函数或通过拟合分布来计算SPI吗?

Does anyone know how to calculate SPI either with the function or by fitting the distribution?

推荐答案

最后，我使用SPI库创建了该结果，如果要计算该值，它将是每个网格点中每个月的值在特定区域，我也做到了，但是如果您也愿意，我可以分享它:

At the end I made it by using the SPI library, the result will be a value for each month in each grid point, if you want to calculate the value over a specific area I made that too but I can share it if you want it too:

此外，这是我使用CRU数据制作的，但是您可以对其进行调整:

Also, this one I made it using CRU data but you can adjust it:

#spei cru 1x1
rm(list=ls(all=TRUE)); dev.off()

require(utils)
require(RNetCDF)
require(rasterVis)
require(rgdal)
library(ncdf4)
require(SPEI)

########################################################################################################


prec <- open.nc("pre_mensual.nc")

lon <- length(var.get.nc(prec, "lon"))
lat <- length(var.get.nc(prec, "lat"))
lon1 <- var.get.nc(prec, "lon")
lat1 <- var.get.nc(prec, "lat")
ppt  <- var.get.nc(prec, "pre") 
ppt  <- ppt[ , ,109:564] #31 18 456 (1980-2017)
anio = 456/12

###########################################################################################################
#Reshape data 

precip <- sapply(1:dim(ppt)[3], function(x)t(ppt[,,x]))

############################################################################################################
#This is for SPI-6, you can use either of them

spi_6 <- array(list(),(lon*lat))

for (i in 1:(lon*lat)) {
  spi_6[[i]] <- spi(precip[i,], scale=6, na.rm=TRUE)
}
#############################################################################################################
#Go back to an array form

sapply(spi_6, '[[',2 )->matriz_ppt 
ppt_6 <- array(aperm(matriz_ppt, c(2,1),c(37,63,456)));spi_c <- array(t(ppt_6), dim=c(37,63,456))
#############################################################################################################
    #Save to netcdf

for(i in 1:456) { 
  nam <- paste("SPI", i, sep = "")
  assign(nam,raster((spi_c[ , ,i]), xmn=min(lon1), xmx=max(lon1), ymn=min(lat1), ymx=max(lat1), crs=CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs+ towgs84=0,0,0")) )
}

gpcc_spi <- stack(mget(paste0("SPI", 1:456)))

outfile <- "spi6_cru_1980_2017.nc"
crs(gpcc_spi) <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0" 
writeRaster(gpcc_spi, outfile, overwrite=TRUE, format="CDF", varname="SPEI", varunit="units",longname="SPEI CRU", xname="lon", yname="lat")

这不是计算它的最时尚的方法，但它确实有效. :)

It's not the most stylish way to calculate it but it does work. :)

如果要计算某个区域的SPI/SPEI，这就是我所做的:

If you want to calculate the SPI/SPEI over an area this is what I did:

library(SPEI)
library(ncdf4)
library(raster)
#

pre_nc <- nc_open("pre_1971_2017_Vts4.nc")
pre <- ncvar_get(pre_nc, "pre")
pre <- pre[, , 109:564] #This is for the time I'm interested in
lats <- ncvar_get(pre_nc, "lat")
lons <- ncvar_get(pre_nc, "lon")
times <- 0:467

# Read mask

#This is a mask you need to create that adjusts to your region of interest
#It consist of a matrix of 0's and 1's, the 1's are placed in the area
#you are interested in

mask1 <- nc_open("cuenca_IV_CDO_05_final.nc")
m1 <- ncvar_get(mask1, "Band1")
m1[m1 == 0] <- NA
#
# Apply mask to data
#
pre1 <- array(NA, dim=dim(pre))

#
for(lon in 1:length(lons)){
  for(lat in 1:length(lats)){
    pre1[lon,lat,] <- pre[lon,lat,]*m1[lon,lat]
  } 
}

#
# Mean over the area of interest
#
mean_pre1 <- apply(pre1,c(3),mean, na.rm=TRUE)

# Calculate SPI/SPEI

spi1 <- matrix(data= NA, nrow = 456, ncol = 48)
for (i in 1:48) {
  spi1[,i] <- spi(data=ts(mean_pre1,freq=12),scale= i)$fitted
}

#This calculates SPI/SPEI-1 to SPI/SPEI-48, you can change it
# Save
#
write.table(spi1,'spi_1980_2017.csv',sep=';',row.names=FALSE)

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