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The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) Dataset: Quasi-Global Precipitation Estimates for Drought Monitoring and Trend Analysis Peterson PJ, Funk CC, Landsfeld MF, Husak GJ, Pedreros DH, Verdin JP, Rowland JD, Michaelsen JC, Shukla S, McNally A, Verdin AP AGU Fall Meeting: Tuesday, 2014.12.16 chg.geog.ucsb.edu/data/chirps tinyurl.com/chg-products/CHIRPS-latest

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1) Create historic precipitation climatology CHPclim 2) Convert IR data to precipitation estimate IRP IRP = b 0 + b 1 *(Cold Cloud Duration Percent) 3) Apply time variability of IRP to CHPclim to make CHIRP CHIRP = CHPclim * (IRP %normal) 4) Blend in stations with CHIRP to make CHIRPS Overview of CHIRPS process chg.geog.ucsb.edu/data/chirps tinyurl.com/chg-products/CHIRPS-latest

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IR to IRP Cold Cloud Duration Regress Cold Cloud Duration (CCD) to TRMM-V7 pentad precipitation [mm/day] at each pixel for each month (2000-2012). Use CCD to calculate near real time precipitation (IRP) from CPC-IR ( hourly). Apply to B1 IR data (3-hourly) from 1981-2000 to extend IRP time series. TRMM-V7 rain rate [mm/day] % of time IR temperature < 235 o K

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CHG Station Climatology Database (CSCD) Global sources: GHCN, GTS, GSOD Regional/National sources: Sahel, Nicholson, Peru, SUNFUN, Tanzania, Mozambique, Zambia, Ethiopia, Malawi, Mozambique, Belize, Guatemala, Central America, Mexico, SMN, Colombia, Panama, Afghanistan, Himalaya, Brazil Screen GTS and GSOD for false zeroes Over billion records across 135k stations since 1981 Quality Control: GSOD duplicates, neighbor coherence, reality checks Decrease in available station data over time

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Station density

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CHIRPS characteristics Spatial Extent: Quasi-Global: all longitudes, 50N-50S Spatial resolution: 0.05 x 0.05 Temporal extent: 1981 present Temporal resolution: daily, pentads, dekads, monthly, 3-monthly Two products, different latency: Preliminary CHIRPS (GTS only) 2 nd day after new pentad Final CHIRPS (all available stations) > 15 th of the following month chg.geog.ucsb.edu/data/chirps tinyurl.com/chg-products/CHIRPS-latest

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Colombia IDEAM AMJ/SON Monthly AMJ stats 1981-2013 Source correlationMAE CHIRP0.38 71.9 CHIRPS0.96 40.7 CFS0.82281.0 CPC-Unif0.40166.0 ECMWF0.72255.0 GPCC0.98 12.9 Monthly SON stats 1981-2013 Source correlationMAE CHIRP0.39 65.7 CHIRPS0.97 38.3 CFS0.76221.0 CPC-Unif0.45154.0 ECMWF0.76203.0 GPCC0.96 20.6

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Colombia IDEAM AMJ total [mm] 900 800 700 600 500 400 1985 1990 1995 2000 2005 2010

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Colombia IDEAM AMJ total [mm] 1985 1990 1995 2000 2005 2010 1200 1000 800 600 400

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Colombia IDEAM SON total [mm] 900 800 700 600 500 400 1985 1990 1995 2000 2005 2010

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Colombia IDEAM SON total [mm] 1985 1990 1995 2000 2005 2010 1200 1000 800 600 400 GC33C-0534: The Use of CHIRPS to Analyze Historical Rainfall in Colombia, Wed. 1:40 - 6pm

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Wet season map

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CHIRPS WST Bias Ratio (data/GPCC)

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CHIRPS WST MAE

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CHIRPS WST Correlation

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Droughts in historical context CHIRPS MAM anomaly 1984 2000 2011

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Conclusions CHIRPS 30+ year record provides historical context for modern droughts. CHIRPS is comparable to GPCC with higher spatial resolution and lower latency. CHIRPS supports consistent drought monitoring. CHPclim provides low bias estimates. Next release of CHIRPS January 2015.

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Thanks to, USGS, USAID, NOAA and NASA SERVIR for funding George Huffman for TRMM-V7 data Wassila Thiaw and Nicholas Novella for CPC IR data Ken Knapp for B1 IR data GHCN, GTS and GSOD Tufa Dinku at IRI for feedback Jim Rowland at EROS for feedback Regional data providers INSIVUMEH, ETESA, Jorgeluis Vazquez, CATIE, Eric Alfaro, IDEAM, Tamuka Magadrize, Sharon Nicholson, Dave Allured, Haline Heidinger, Junior

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Snippets This code on your webserver:Gives you this image on your website:

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Construct Wet Season Total comparisons For each dataset, ARC2, CFS, CHIRP, CHIRPS, CPCU, ECMWF, GPCC, RFE2, TAMSAT and TRMM-RT7 Construct cubes of Wet Season Totals and compare to GPCC.

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12,000 8,000 4,000 0

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Crop Zones Elevation Population

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The GeoCLIM Climatological Analysis The Climatological Analysis tool in the GeoCLIM allows the user to calculate statistics, trends and frequencies for a season for a given set of years. chg.geog.ucsb.edu/data/chirps/index.html tinyurl.com/chg-products/CHIRPS-latest

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The Water Requirement Satisfaction Index (WRSI) model The WRSI is an indicator of crop performance based on the availability of water to the crop during a growing season. The main data inputs in this model are precipitation and evapotranspiration.

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chg.geog.ucsb.edu/data/chirps/index.html tinyurl.com/chg-products/CHIRPS-latest Mean Absolute Error [mm/month] (less is better)

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CHIRPS WST Correlation RFE2 TAMSAT CHIRPS ARC2

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CHIRPS WST Bias Ratio RFE2 TAMSAT CHIRPS ARC2

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CHIRPS WST MAE RFE2 TAMSAT CHIRPS ARC2

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Cross validation stats for April

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