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AGU Fall Meeting: Tuesday, 2014.12.16
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, chg.geog.ucsb.edu/data/chirps tinyurl.com/chg-products/CHIRPS-latest
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Overview of CHIRPS process
1) Create historic precipitation climatology CHPclim 2) Convert IR data to precipitation estimate IRP IRP = b0 + b1*(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 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 ( ). Use CCD to calculate near real time precipitation (IRP) from CPC-IR (½ hourly). Apply to B1 IR data (3-hourly) from to extend IRP time series. TRMM-V7 rain rate [mm/day] % of time IR temperature < 235o 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 ½ 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) 2nd day after new pentad Final CHIRPS (all available stations) > 15th 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 Source correlation MAE CHIRP CHIRPS CFS CPC-Unif ECMWF GPCC Monthly SON stats Source correlation MAE CHIRP CHIRPS CFS CPC-Unif ECMWF GPCC
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Colombia IDEAM AMJ total [mm]
900 800 700 600 500 400
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Colombia IDEAM AMJ total [mm]
1200 1000 800 600 400
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Colombia IDEAM SON total [mm]
900 800 700 600 500 400
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Colombia IDEAM SON total [mm]
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
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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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Mean Absolute Error [mm/month] (less is better)
chg.geog.ucsb.edu/data/chirps/index.html tinyurl.com/chg-products/CHIRPS-latest
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CHIRPS WST Correlation
CHIRPS ARC2 RFE TAMSAT
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CHIRPS WST Bias Ratio CHIRPS ARC2 RFE TAMSAT
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CHIRPS WST MAE CHIRPS ARC2 RFE TAMSAT
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Cross validation stats for April
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Cross validation stats for April
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