Open AccessEnvironmental ScienceMedicine

C. Funk, P. Peterson, M. Landsfeld, Diego H. Pedreros, J. Verdin, S. Shukla, G. Husak, J. Rowland, L. Harrison, A. Hoell, J. Michaelsen

2015.12.8Scientific Data

DOI: 10.1038/sdata.2015.66

tlooto Summary

The Variable Infiltration Capacity model, a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights, is presented and it is shown that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.

Abstract

The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to ‘smart’ interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia. Design Type(s) observation design • time series design • data integration objective Measurement Type(s) atmospheric precipitation Technology Type(s) meterological observation Factor Type(s) Sample Characteristic(s) Africa • Central America • Caribbean Region • East Africa • atmospheric water vapour Design Type(s) observation design • time series design • data integration objective Measurement Type(s) atmospheric precipitation Technology Type(s) meterological observation Factor Type(s) Sample Characteristic(s) Africa • Central America • Caribbean Region • East Africa • atmospheric water vapour Machine-accessible metadata file describing the reported data (ISA-Tab format)

Citation format

FUNK, C., et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific Data, 2015, 2.