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

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Presentation on theme: "The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) Dataset: Quasi-Global Precipitation Estimates for Drought Monitoring and Trend."— Presentation transcript:

1 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

2 Thanks to, USGS, USAID, NOAA and NASA SERVIR for funding
George Huffman for TRMM 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

3 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

4 Station density

5 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

6 Colombia IDEAM SON 338 validation stations SON time series stats
Source correlation MAE CHIRP CHIRPS CFS CPC-Unif ECMWF GPCC

7 Colombia IDEAM SON total [mm]
900 800 700 600 500 400

8 Colombia IDEAM SON total [mm]
1200 1000 800 600 400 Diego Pedreros Poster GC33C-0534: The Use of CHIRPS to Analyze Historical Rainfall in Colombia, Wed. 1:40 - 6pm

9 Wet season map

10 CHIRPS WST Bias Ratio (data/GPCC)

11 CHIRPS WST Correlation

12 chg.geog.ucsb.edu/data/chirps
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. Starting with CHPclim leads to low bias estimates. CHIRPS adds to FEWS NET’s confluence of evidence. CHIRPS v2.0 will be released late January 2015 chg.geog.ucsb.edu/data/chirps

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14 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

15 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 Over ½ billion records across 135k stations since 1981 Quality Control: False zeroes, location check, elevations, GSOD duplicates, neighbor coherence, reality checks Decrease in available station data over time

16 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

17 Colombia IDEAM AMJ total [mm]
900 800 700 600 500 400

18 Colombia IDEAM AMJ total [mm]
1200 1000 800 600 400

19 Droughts in historical context CHIRPS MAM anomaly
1984 2000 2011

20 CHIRPS WST MAE

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

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23 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.

24 12,000 8,000 4,000

25 Crop Zones Elevation Population

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27 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

28 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.

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

30 CHIRPS WST Correlation
CHIRPS ARC2 RFE TAMSAT

31 CHIRPS WST Bias Ratio CHIRPS ARC2 RFE TAMSAT

32 CHIRPS WST MAE CHIRPS ARC2 RFE TAMSAT

33 Cross validation stats for April

34 Cross validation stats for April

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