1 / 14

Statistical postprocessing of simulated precipitation – perspectives for impact research

IMSC 2010. Statistical postprocessing of simulated precipitation – perspectives for impact research. Heiko Paeth. Institute of Geography, University of Würzburg, Germany. Diagnosis of model deficiencies. annual precipitation totals. Diagnosis of model deficiencies.

medea
Download Presentation

Statistical postprocessing of simulated precipitation – perspectives for impact research

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. IMSC 2010 Statistical postprocessing of simulated precipitation – perspectives for impact research Heiko Paeth Institute of Geography, University of Würzburg, Germany

  2. Diagnosis of model deficiencies annual precipitation totals

  3. Diagnosis of model deficiencies monthly precipitation variability

  4. 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. Implications for impact research  climate model: permanent drizzling within grid box hydrological model: permanent soil moisturization, no peak runoff, no erosion

  6. Implications for impact research  climate model: permanent drizzling within grid box hydrological model: permanent soil moisturization, no peak runoff, no erosion MOS WEGE

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

  8. MOS: characteristics number of predictors (August) type of predictors explained variance (August)

  9. MOS: results annual precipitation totals

  10. MOS: results monthly precipitation variability REMO(adj) – CRU (total STD) REMO - CRU (total STD)

  11. WEGE: methodology simulated grid-box precipitation (dynamical part) local topography (physical part) random distribution in space (stochastical part) probability matching virtual station rainfall (result) model obs.

  12. 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 original REMO rainfall rainfall from weather generator station time series (Kandi) model data station data model data postprocessed

  13. WEGE: results mean daily precipitation intensity mean daily precipitation variability

  14. 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 

More Related