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UCSB CHIRPS v2p0 daily africa 0p25

UCSB CHIRPS v2p0 daily africa 0p25.


outlinean outline showing all sub-datasets and variables contained in this dataset
CHIRPS Home PageCHIRPS page at UCSB Climate Hazards Group
USGS Data Series 832Reference paper PDF

Datasets and variables

UCSB CHIRPS v2p0 daily africa 0p25 prcp[ X Y | T]

Independent Variables (Grids)

Time (time) grid: /T (julian_day) ordered (1 Jan 2000) to (31 Dec 2017) by 1.0 N= 6575 pts :grid
Longitude (longitude) grid: /X (degree_east) periodic (19.875W) to (54.875E) by 0.25 N= 300 pts :grid
Latitude (latitude) grid: /Y (degree_north) ordered (39.875N) to (39.875S) by 0.25 N= 320 pts :grid

Other Info

The Climate Hazards Group InfraRed Precipitation with Stations development process was carried out through U.S. Geological Survey (USGS) cooperative agreement #G09AC0000 01 "Monitoring and Forecasting Climate, Water and Land Use for Food Production in the Developing World" with funding from: U.S. Agency for International Development Office of Food for Peace , award #AID-FFP-P-10-00002 for "Famine Early Warning Systems Network Support," the National Aeronautics and Space Administration Applied Sciences Program, Decisions award #NN10AN26I for "A Land Data Assimilation System for Famine Early Warning," SERVIR award #NNH12AU22I for "A Long Time-Series Indicator of Agricultural Drought for the Greater Horn of Africa," The National Oc eanic and Atmospheric Administration award NA11OAR4310151 for "A Global Standardized Precipitation Index supporting the US Drought Portal and the Famine Early Warning System Network," and t he USGS Land Change Science Program.
Pete Peterson
Climate Hazards Group. University of California at Santa Barbara
There is an improved temporal downscaling procedure for estimating the final daily CHIRPS. The previous daily data will be supported through the end of 2015. Since CHIRPS pentads an d monthly remain unchanged there will be no change in the CHIRPS version number. This is not a new version of CHIRPS, it is an improvement on the temporal downscaling to daily estimates. T he Problem: When temporally downscaling CHIRPS to daily maps, there are many locations with significant residuals. (Residual here is the difference between monthly CHIRPS and the sum of d aily CHIRPS for that month) In fact for over 80 percent of the locations with residuals, the residual was 100 percent of the monthly CHIRPS. These are places where the daily CCD fails, ofte n due to warm precipitation. The Solution: Add an extra step to distribute the monthly precipitation to across days in the month only for pixels where the monthly residual is > 1 mm. The first challenge was to decide the appropriate number of days of precipitation for a given month. We used the locations with zero residual to derive a relationship between total monthly CHI RPS and number of rain days. Now, using the total residual and number of rain days, we use the highest N values of daily CHIRP for the month to proportionally distribute the monthly total across those days.
Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros, D.H., Verdin, J.P., Rowland, J.D., Romero, B.E., Husak, G.J., Michaelsen, J.C., and Verdin, A.P., 2014, A quasi-global pr ecipitation time series for drought monitoring: U.S. Geological Survey Data Series 832, 4 p.,
CHIRPS Version 2.0
Version 2.0
created by Climate Hazards Group


Funk, C. C, Peterson, P. J., Landsfeld, M. F., Pedreros, D. H., Verdin, J. P., Rowland, J. D., Romero, B. E., Husak, G. J. Michaelsen, J. C., and Verdin, A. P., 2014, A quasi-global precipitation time series for drought monitoring: U. S. Geological Survey Data Series 832, 4 p.,

Last updated: Wed, 17 Jan 2018 23:20:41 GMT
Expires: Thu, 01 Feb 2018 00:00:00 GMT

Data Views

UCSB CHIRPS v2p0 daily africa 0p25[ ]