Obtain and Analyze Rainfall Data from the Climate Prediction Center

The Climate Prediction Center's (CPC) rainfall data for the world (1979 to present, 50 km resolution) and the USA (1948 to present, 25 km resolution), 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, processing and visualizing the data is not straightforward. There are more than 12,000 files for the world and about 24,000 files for the USA. Moreover, file formats and file extensions have not been consistent. This package provides functionality to download, process and visualize over 35 years of global rainfall data and over 65 years of USA rainfall data from CPC.


  • Binary output files are written only if desired in cpc_read_rawdata. Removed the explicit fixed number of bytes (4) used in the binary output.
  • Added functionality to also extract CPC USA data (1948 to present, 25 km resolution).

  • Fixed a bug with calculating the sequence of dates in cpc_get_rawdata.

  • Fixed broken links in the package vignette.

  • Added functionality to display rainfall data, output is a RasterLayer consistent with SDMTools. This change enables easier display and also extraction of regional data.

  • Data extraction was limited to 2013 in v0.1, changed it to two days short of the present day

  • Added a R markdown vignette to demonstrate the use of the package

  • Changed license to MIT

  • Ways to make the data download faster needs to be explored.

  • Future revision will include graphics functions to display the data. For now, visualization is done outside of the package. Examples from the project homepage help the user to visualize the processed CPC data using ggplot.

Reference manual

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0.4 by Gopi Goteti, 3 years ago

Browse source code at https://github.com/cran/raincpc

Authors: Gopi Goteti <my.ration.shop@gmail.com>

Documentation:   PDF Manual  

Task views:

MIT + file LICENSE license

Imports SDMTools

Suggests knitr, raster, ggplot2

See at CRAN