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The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes

Chris FunkUC Santa Barbara Climate Hazards Group, Santa Barbara, California 93106, USAPete PetersonUC Santa Barbara Climate Hazards Group, Santa Barbara, California 93106, USAM. F. LandsfeldUC Santa Barbara Climate Hazards Group, Santa Barbara, California 93106, USADiego PedrerosUS Geological Survey, Center for Earth Resources Observation and Science, 47914 252nd St., Sioux Falls, South Dakota 57198, USAJ. P. VerdinUS Geological Survey, Center for Earth Resources Observation and Science, 47914 252nd St., Sioux Falls, South Dakota 57198, USAShraddhanand ShuklaUC Santa Barbara Climate Hazards Group, Santa Barbara, California 93106, USAG. J. HusakUC Santa Barbara Climate Hazards Group, Santa Barbara, California 93106, USAJames RowlandUS Geological Survey, Center for Earth Resources Observation and Science, 47914 252nd St., Sioux Falls, South Dakota 57198, USAL. HarrisonUC Santa Barbara Climate Hazards Group, Santa Barbara, California 93106, USAAndrew HoellNational Oceanic and Atmospheric Administration Earth Systems Research Laboratory, Boulder, Colarodo 80305, USAJoel MichaelsenUC Santa Barbara Climate Hazards Group, Santa Barbara, California 93106, USA

2015en

ABI

Annotatsiya

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.

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Identifikatorlar

DOI: 10.1038/sdata.2015.66

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