Introduction to raincpc

The Climate Prediction Center's (CPC) global rainfall data, 1979 to present, 0.5 degree resolution (approximately 50 km), is one of the few high quality, long-term, observation-based, daily rainfall products available for free. Although raw data is available at CPC's ftp site, obtaining and processing the data is not straightforward.

Some issues with the raw CPC data:

too many files (365/366 files per year * 36 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 several 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, for instance using wget, does not seem to work properly
there is no software readily available to easily process/visualize the data

The package raincpc addresses the above drawbacks by providing functionality to download, process and visualize the raw rainfall data from CPC. Following are some examples.

Using raincpc

After installing the package, load the package along with SDMTools, raster, and ggplot2.

library(raincpc)
library(SDMTools)
library(raster)
library(ggplot2)

raincpc comes with the function cpc_get_rawdata to download the data from CPC. Following example extracts rainfall data for June 15-19 of 2014.

cpc_get_rawdata(2014, 6, 15, 2014, 6, 19)

Once the data is downloaded, the function cpc_read_rawdata can be used to read the binary data and convert it to a raster for analysis and graphics.

rain1 <- cpc_read_rawdata(2014, 6, 15)
print(rain1)

#> class       : RasterLayer 
#> dimensions  : 360, 720, 259200  (nrow, ncol, ncell)
#> resolution  : 0.5, 0.5  (x, y)
#> extent      : 0, 360, -90, 90  (xmin, xmax, ymin, ymax)
#> coord. ref. : NA 
#> data source : in memory
#> names       : layer 
#> values      : 0, 160.5  (min, max)

Extract the rainfall for all the five days, and then compute the total rainfall for June 15-19. This rainfall total is used in the following graphics.

rain2 <- cpc_read_rawdata(2014, 6, 16)
rain3 <- cpc_read_rawdata(2014, 6, 17)
rain4 <- cpc_read_rawdata(2014, 6, 18)
rain5 <- cpc_read_rawdata(2014, 6, 19)

rain_tot <- rain1 + rain2 + rain3 + rain4 + rain5
print(rain_tot)

#> class       : RasterLayer 
#> dimensions  : 360, 720, 259200  (nrow, ncol, ncell)
#> resolution  : 0.5, 0.5  (x, y)
#> extent      : 0, 360, -90, 90  (xmin, xmax, ymin, ymax)
#> coord. ref. : NA 
#> data source : in memory
#> names       : layer 
#> values      : 0, 287  (min, max)

Graphics

Here is the plot of total rainfall for June 15-19 2014.

png("gfx_rain1.png", width = 800, height = 600)
plot(rain_tot, 
     breaks = c(0, 80, 160, 240, 320),
     col = c("grey", "red", "green", "blue"), 
     main = "Rainfall (mm) June 15 - June 19 2014")
garbage <- dev.off()

Although it is easy to display the data using the plot method for a RasterLayer, the quality of the graphic is not great. Here is a plot of the same data using ggplot2. First, create a function to convert the data in a ggplot-friendly format.

raster_ggplot <- function(rastx) {
  require(SDMTools)

  stopifnot(class(rastx) == "RasterLayer")

  gfx_data <- getXYcoords(rastx)
  # lats need to be flipped
  gfx_data <- expand.grid(lons = gfx_data$x, lats = rev(gfx_data$y), 
                            stringsAsFactors = FALSE, KEEP.OUT.ATTRS = FALSE)
  gfx_data$rain <- rastx@data@values

  return (gfx_data)
}

Use the above function to plot the RasterLayer object using ggplot2.

rain_gg <- raster_ggplot(rain_tot)

rain_gg$rain_chunks <- cut(rain_gg$rain, breaks = c(0, 80, 160, 240, 320), 
                         include.lowest = TRUE)

gfx_gg <- ggplot(data = rain_gg)
gfx_gg <- gfx_gg + geom_raster(aes(lons, lats, fill = rain_chunks))
gfx_gg <- gfx_gg + scale_fill_manual(values = c("grey", "red", "green", "blue"))
gfx_gg <- gfx_gg + theme(axis.text = element_blank(), axis.ticks = element_blank())
gfx_gg <- gfx_gg + labs(x = NULL, y = NULL, fill = "Rain (mm)")
gfx_gg <- gfx_gg + ggtitle("Rainfall June 15 - June 19 2014")

png("gfx_rain2.png", width = 800, height = 600)
plot(gfx_gg)
garbage <- dev.off()

Extracting Regional Rainfall

SDMTools has functionality to extract regional data from the RasterLayer object. In order to extract the data, say over Southeast Asia, specify a lat-lon bounding box as a data frame and extract the data for this box.

lon_vals <- seq(100.75, 130.75, 0.5)
lat_vals <- seq(-10.75, 20.75, 0.5)
reg_box <- expand.grid(lons = lon_vals, lats = lat_vals, 
                            stringsAsFactors = FALSE, KEEP.OUT.ATTRS = FALSE)
reg_box$rain <- extract.data(reg_box, rain_tot)
reg_box$rain_chunks <- cut(reg_box$rain, breaks = c(0, 80, 160, 240, 320), 
                         include.lowest = TRUE)

Use ggplot to plot the regional rainfall over Southeast Asia.

gfx_gg <- ggplot(data = reg_box)
gfx_gg <- gfx_gg + geom_raster(aes(lons, lats, fill = rain_chunks))
gfx_gg <- gfx_gg + scale_fill_manual(values = c("grey", "red", "green", "blue"))
gfx_gg <- gfx_gg + theme(axis.text = element_blank(), axis.ticks = element_blank())
gfx_gg <- gfx_gg + labs(x = NULL, y = NULL, fill = "Rain (mm)")
gfx_gg <- gfx_gg + ggtitle("Rainfall over Southeast Asia June 15 - June 19 2014")

png("gfx_rain3.png")
plot(gfx_gg)
garbage <- dev.off()