1. 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

2. 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

3. 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

4. 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

5. Station density

6. 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

7. 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

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

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

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

11. 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

12. Wet season map

13. CHIRPS WST Bias Ratio (data/GPCC)

14. CHIRPS WST MAE

15. CHIRPS WST Correlation

16. Droughts in historical context CHIRPS MAM anomaly
1984
2000
2011

17. 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.

18. 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

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

21. 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.

22. 12,000
8,000
4,000

23. Crop Zones Elevation Population

24. 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

25. 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.

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

27. CHIRPS WST Correlation
CHIRPS ARC2
RFE TAMSAT

28. CHIRPS WST Bias Ratio
CHIRPS ARC2
RFE TAMSAT

29. CHIRPS WST MAE
CHIRPS ARC2
RFE TAMSAT

30. Cross validation stats for April

31. Cross validation stats for April