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

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

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

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% IR%) * FCLIM  Unbiased IR precipitation (UIRP) = IR% * FCLIM

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

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)

1 Monthly means of TRMM v6, LST and IR

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

1 FCLIM Variance Explained

1 FCLIM Annual Means

1 Climatology Validations RegionN-stnsStn MeanModel MeanMBEMAEPct MBEPct MAER2 FCLIMCombined Colombia Afghanistan SE Asia Ethiopia Sahel Mexico CRUCombined Colombia Afghanistan SE Asia Ethiopia Sahel Mexico WorldclimCombined Colombia Afghanistan SE Asia Ethiopia Sahel Mexico

1 Drought monitoring validation study for Africa  Modeled after previous analyses by Grimes and Dinku  0.25° block kriged monthly station data ( )  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

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

1 Main growing season correlation maps

1 Main growing season mean bias maps

1 Main growing season mean absolute error maps

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

1 Main growing season hits

1 Main growing season correct negatives

1 Main growing season misses

1 Main growing season False Alarms

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