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 (60N/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