In epijim/raincpc: Obtain and Analyze Rainfall data from the Climate Prediction Center (CPC)

R package raincpc: Obtain and Analyze Rainfall data from the Climate Prediction Center (CPC)

The Climate Prediction Center's daily rainfall data for the entire world, CPC, 1979 - present & 50-km resolution, is one of the few high quality and long-term observation-based rainfall products available for free. Data is available at CPC's ftp site. However, it is a lot of data and there is no software to analyze and visualize the data.

Some issues with size/format of the CPC data: too many files (365/366 files per year * 34 years, separate folder for each year) each file has 360 rows and 720 columns file naming conventions have changed over time - one format prior to 2006 and couple of different formats afterwards file formats have changed over time - gzipped files prior to 2008 and plain binary files afterwards downloading multiple files simultaneously from the CPC ftp site, using wget, does not seem to work properly there is no software/code readily available to easily process/visualize the data

R package raincpc: Data for anytime period during 1979-present can be downloaded and processed Just two functions required: one to download the data (cpc_get_rawdata) and another to process the downloaded data (cpc_read_rawdata) * Making spatial maps using the processed data is easy, via ggplot

Here are some examples.

Invoke required libraries.

library(raincpc)

library(ggplot2)
library(reshape2)

Rainfall during Typhoon Yolanda/Haiyan, November 2013.

# obtain data
cpc_get_rawdata(2013, 11, 7, 2013, 11, 7) 
# process data
haiyan <- cpc_read_rawdata(2013, 11, 7) 

Re-orient data for plotting.

# function to flip a matrix upside down (change CPC orientation from S-N to N-S)
Fn_Flip_Matrix_Rows <- function(mat) {
  return (mat[nrow(mat):1,])
}
# function to rotate a matrix 90 degress clockwise for plotting only
# used to counteract the "image" function default behavior
Fn_Rotate_Matrix <- function(mat) {
  return (t(mat)[,nrow(mat):1])
}

haiyan <- Fn_Rotate_Matrix(Fn_Flip_Matrix_Rows(haiyan))

Global rainfall map.

gfx_map <- ggplot(data = melt(haiyan)) 
gfx_map <- gfx_map + geom_raster(aes(Var1, Var2, fill = value)) 
gfx_map <- gfx_map + scale_fill_continuous(low = "blue", high = "red")
gfx_map <- gfx_map + theme(axis.text = element_blank(), 
                           axis.ticks = element_blank())
gfx_map <- gfx_map + labs(x = NULL, y = NULL, fill = "Rain (mm/day)")
gfx_map <- gfx_map + ggtitle("Rainfall during Typhoon Haiyan/Yolanda, Nov 7 2013")

png("gfx_haiyan.png", width = 800, height = 650)
plot(gfx_map)
garbage <- dev.off()

epijim/raincpc documentation built on May 16, 2019, 8:18 a.m.