H24C-03:
The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) Dataset: Quasi-Global Precipitation Estimates for Drought Monitoring and Trend Analysis

Tuesday, 16 December 2014: 4:30 PM
Pete Peterson1, Chris C Funk1, Martin F Landsfeld1, Gregory J Husak1, Diego H Pedreros2, James P Verdin3, James Rowland4, Shraddhanand Shukla1, Amy McNally1, Joel Michaelsen5 and Andrew Verdin6, (1)University of California Santa Barbara, Santa Barbara, CA, United States, (2)USGS, Baltimore, MD, United States, (3)USGS/EROS, Boulder, CO, United States, (4)U.S. Geological Survey, Sioux Falls, SD, United States, (5)UC Santa Barbara, Santa Barbara, CA, United States, (6)University of Colorado at Boulder, Boulder, CO, United States
Abstract:
A high quality, long-term, high-resolution precipitation dataset is a key requirement for supporting drought monitoring and long term trend analysis. In this presentation we introduce a new dataset: the Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), developed by scientists at the University of California, Santa Barbara and the U.S. Geological Survey Earth Resources Observation and Science Center. This new quasi-global precipitation product is available at daily to seasonal time scales, with a spatial resolution of 0.05°, and a 1981 to near real-time period of record. The three main types of information used in the CHIRPS are: (1) global 0.05° precipitation climatologies, (2) time-varying grids of infrared cold cloud duration (CCD) precipitation estimates, and (3) in situ precipitation observations.

The CHG has developed an extensive database of in situ daily, pentadal and monthly precipitation totals with over a billion daily observations worldwide. Most of these observations come from four sets: the monthly Global Historical Climate Network version 2, the daily Global Historical Climate Network, the Global Summary of the Day (GSOD), and the daily Global Telecommunication System (GTS) provided by NOAA's Climate Prediction Center (CPC). A screening procedure was developed to remove suspected “false zeros” from the daily GTS and GSOD data, since these data can artificially suppress rainfall totals.

We compare CHIRPS and ARC2, CFS-Reanalysis, CHIRP, CMORPH, CPC-Unified, ECMWF, PERSIANNE, RFE2, TAMSAT, TRMM-RT7, TRMM-V7 to GPCC. The CHIRPS is shown to have high correlation, low systematic errors (bias) and low mean absolute errors. The CHIRPS performance is similar to research quality products like the GPCC and GPCP, but with higher resolution and lower latency. Cross validation results for over 100 countries and comparisons with alternate algorithms will be presented.

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