1. Heiko Paeth heiko.paeth@uni-wuerzburg.de Statistical postprocessing of simulated precipitation – perspectives for impact research IMSC 2010 Heiko Paeth Institute of Geography, University of Würzburg, Germany

2. Heiko Paeth heiko.paeth@uni-wuerzburg.de Diagnosis of model deficiencies annual precipitation totals

3. Heiko Paeth heiko.paeth@uni-wuerzburg.de Diagnosis of model deficiencies monthly precipitation variability

4. Heiko Paeth heiko.paeth@uni-wuerzburg.de Diagnosis of model deficiencies PDFs of daily precipitation climate models: area-mean precipitation (50km x 50km) station data: local information (0,1km x 0,1km) model data station data

5. Heiko Paeth heiko.paeth@uni-wuerzburg.de Implications for impact research climate model: permanent drizzling within grid box hydrological model: permanent soil moisturization, no peak runoff, no erosion 

6. Heiko Paeth heiko.paeth@uni-wuerzburg.de Implications for impact research climate model: permanent drizzling within grid box hydrological model: permanent soil moisturization, no peak runoff, no erosion MOSWEGE

7. Heiko Paeth heiko.paeth@uni-wuerzburg.de MOS: methodology MOS multiple linear regression model cross validation - 100 iterations with bootstrapping simulated predictors - REMO data 1979-2002 - rainfall, SAT, SLP, surface wind components local predictors: max. 0.5° around each CRU grid cell EOF predictors: EOFs 1-20 for each variable observed predictand - CRU monthly rainfall 1979-2002 ≤ 15 out of 145 predictors are selected according to sig. test +

8. Heiko Paeth heiko.paeth@uni-wuerzburg.de MOS: characteristics explained variance (August) number of predictors (August) type of predictors

9. Heiko Paeth heiko.paeth@uni-wuerzburg.de MOS: results annual precipitation totals

10. Heiko Paeth heiko.paeth@uni-wuerzburg.de MOS: results monthly precipitation variability REMO(adj) – CRU (total STD) REMO - CRU (total STD)

11. Heiko Paeth heiko.paeth@uni-wuerzburg.de WEGE: methodology virtual station rainfall (result) simulated grid-box precipitation (dynamical part) local topography (physical part) random distribution in space (stochastical part) probability matching modelobs.

12. Heiko Paeth heiko.paeth@uni-wuerzburg.de WEGE: results  REMO rainfall: - wrong seasonal cycle - underestimated extremes - hardly any dry spells  Weather Generator: - statistical distribution as observed - individual events not in phase with observations model data station data model data postprocessed original REMO rainfall rainfall from weather generator station time series (Kandi)

13. Heiko Paeth heiko.paeth@uni-wuerzburg.de WEGE: results mean daily precipitation intensity mean daily precipitation variability

14. Heiko Paeth heiko.paeth@uni-wuerzburg.de Summary  MOS and weather generator worked fine for West Africa and Benin, respectively  impact research in the field of hydrology, agro-economy and heatlh was carried out successfully   MOS approach requires in-phase relationship between model data and observations  weather generator requires high station density with long time series of daily precipitation