1. U.S. Department of the Interior U.S. Geological Survey Evaluating the drought monitoring capabilities of rainfall estimates for Africa Chris Funk Pete Peterson Amy McNally

2. 1 What makes good rainfall estimates go bad? 1.Sparse station data 2.Inconsistent networks 3.Complex mean fields 4.Biased satellite estimates 5.Inhomogeneous satellite inpu ts

3. 1 Climate Hazard Group Climatology Method Spatial Means Temporal Anomalies Dense gauge observations Elevation, latitude, longitude Satellite mean fields Sparse gauge observations Satellite anomaly fields

4. 1 FEWS NET TRMM-IR Precipitation (FTIP)  Quasi-global (±60°N/S), 0.05°, pentads  Built around 0.05° rainfall mean fields  FEWS NET Climatology (FCLIM)  Two components  TRMM V6/RT as percentage (TR%)  Cold cloud duration IR estimates as percentages (IR%)  Global CCD models trained against TRMM 3B42 pentads  CCD threshold, slope, intercept for each month  FTIP = (0.5 TR% + 0.5 IR%) * FCLIM  Unbiased IR precipitation (UIRP) = IR% * FCLIM

5. 1 Hispaniola Rainfall dekads: TRMM and FTIP Satellite % X FCLIM --------------- Improved Rainfall Estimates TRMM FTIP

6. 1 Building better climatologies  Use regional moving window regressions to model long term mean monthly rainfall as a function of  Elevation, Slope, latitude, longitude, …  TRMM, Land Surface Temperatures, Infrared brightness temperatures, ….  Use standard interpolation approaches (kriging or IDW to blend in station anomalies)

7. 1 Monthly means of TRMM v6, LST and IR

8. 1 Local correlations w/ satellite mean fields much better than elevation or slope

9. 1 FCLIM Variance Explained

10. 1 FCLIM Annual Means

11. 1 Climatology Validations RegionN-stnsStn MeanModel MeanMBEMAEPct MBEPct MAER2 FCLIMCombined2216979920200.69 Colombia1941681598305180.84 Afghanistan223534193250.53 SE Asia6122144-2237-18300.28 Ethiopia7697943103 0.91 Sahel1048682394110.90 Mexico1814777823-2300.65 CRUCombined221697106-932-9320.51 Colombia194168174-646-4280.59 Afghanistan223545-1020-27570.18 SE Asia6122152-3056-25460.25 Ethiopia7697101-423-4230.68 Sahel1048690-420-4240.74 Mexico181477751242310.60 WorldclimCombined221697106-926-9270.61 Colombia194168178-1131-6190.82 Afghanistan223541-618-17520.18 SE Asia6122155-3350-27410.42 Ethiopia7697 0200210.72 Sahel1048688-219-3220.75 Mexico18147779-217-2230.78

12. 1 Drought monitoring validation study for Africa  Modeled after previous analyses by Grimes and Dinku  0.25° block kriged monthly station data (2001-2010)  Interpolated as %, multiplied by FCLIM  Study looks at drought hits, misses, false alarms and correct negatives  Drought event defined as a two-month period with less than 190 mm of rainfall  Focused on main rainy season rainfall  For each grid cell,  Identify three month period with max average rainfall  Build time-series of two-month rainfall combinations

13. 1 Building time-series of main season rainfall  Identify main rainy season (i.e. MAM)  For each year, construct three 2-month combinations: e.g. March+April, April+May, March+May  Do this for each year to obtain ~27 combinations: 2001MA, 2001AM, 2001MM, 2002MA, 2002AM, 2002MM, 2003MA, 2003AM, 2003MM, 2004MA, 2004AM, 2004MM, 2005MA, 2005AM, 2005MM, 2006MA, 2006AM, 2006MM, 2007MA, 2007AM, 2007MM, 2008MA, 2008AM, 2008MM, 2009MA, 2009AM, 2009MM, 2010MA, 2010AM, 2010MM  Focus on rainfall during germination/grain filling

14. 1 Main growing season correlation maps

15. 1 Main growing season mean bias maps

16. 1 Main growing season mean absolute error maps

17. 1 Hits and misses Satellite Estimate Kriged Station Data 190 mm Hit Miss False Alarm Correct Negative

18. 1 Main growing season hits

19. 1 Main growing season correct negatives

20. 1 Main growing season misses

21. 1 Main growing season False Alarms

22. 1 Conclusions  Incorporating climatologies improves skill  FCLIM unbiasing easily reproducable with TARCAT, ECMWF, …  Fitting CCD models using TRMM 3B42 seems worthwhile  RFE2, FTIP, and TARCAT showed best-correlations  FTIP and then the TRMM showed the smallest mean bias  TARCAT, RFE2 and FTIP had lower MAE  TARCAT, RFE2, FTIP, ECMWF do a good job discriminating hits and correct negatives  Misses  Some in the Sahel (RFE2, TRMM, RFE2)  Sudan (ECMWF)  False Alarms:  Part of east Africa in each  Sahel in ECMWF  Focus on rainfall during germination/grain filling