1 / 22

Evaluating the drought monitoring capabilities of rainfall estimates for Africa

Evaluating the drought monitoring capabilities of rainfall estimates for Africa. Chris Funk Pete Peterson Amy McNally. What makes good rainfall estimates go bad?. Sparse station data Inconsistent networks Complex mean fields Biased satellite estimates Inhomogeneous satellite inpu ts.

colman
Télécharger la présentation

Evaluating the drought monitoring capabilities of rainfall estimates for Africa

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Evaluating the drought monitoring capabilities of rainfall estimates for Africa Chris Funk Pete Peterson Amy McNally

  2. What makes good rainfall estimates go bad? • Sparse station data • Inconsistent networks • Complex mean fields • Biased satellite estimates • Inhomogeneous satellite inputs

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

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

  6. 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. Monthly means of TRMM v6, LST and IR

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

  9. FCLIM Variance Explained

  10. FCLIM Annual Means

  11. Climatology Validations

  12. 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. 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. Main growing season correlation maps

  15. Main growing season mean bias maps

  16. Main growing season mean absolute error maps

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

  18. Main growing season hits

  19. Main growing season correct negatives

  20. Main growing season misses

  21. Main growing season False Alarms

  22. 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

More Related