heiko paeth [email protected] statistical postprocessing of simulated precipitation –...
Post on 19-Dec-2015
215 views
TRANSCRIPT
![Page 1: Heiko Paeth heiko.paeth@uni-wuerzburg.de Statistical postprocessing of simulated precipitation – perspectives for impact research IMSC 2010 Heiko Paeth](https://reader030.vdocument.in/reader030/viewer/2022032800/56649d395503460f94a13d72/html5/thumbnails/1.jpg)
Heiko Paeth [email protected]
Statistical postprocessing of simulated precipitation –
perspectives for impact research
IMSC 2010
Heiko Paeth
Institute of Geography, University of Würzburg, Germany
![Page 4: Heiko Paeth heiko.paeth@uni-wuerzburg.de Statistical postprocessing of simulated precipitation – perspectives for impact research IMSC 2010 Heiko Paeth](https://reader030.vdocument.in/reader030/viewer/2022032800/56649d395503460f94a13d72/html5/thumbnails/4.jpg)
Heiko Paeth [email protected]
Diagnosis of model deficienciesPDFs of daily precipitation
climate models:area-mean
precipitation(50km x 50km)
station data:local
information(0,1km x 0,1km)
model datastation data
![Page 5: Heiko Paeth heiko.paeth@uni-wuerzburg.de Statistical postprocessing of simulated precipitation – perspectives for impact research IMSC 2010 Heiko Paeth](https://reader030.vdocument.in/reader030/viewer/2022032800/56649d395503460f94a13d72/html5/thumbnails/5.jpg)
Heiko Paeth [email protected]
Implications for impact research
climate model:
permanentdrizzlingwithin
grid box
hydrological model:
permanent soil moisturization,no peak runoff,
no erosion
![Page 6: Heiko Paeth heiko.paeth@uni-wuerzburg.de Statistical postprocessing of simulated precipitation – perspectives for impact research IMSC 2010 Heiko Paeth](https://reader030.vdocument.in/reader030/viewer/2022032800/56649d395503460f94a13d72/html5/thumbnails/6.jpg)
Heiko Paeth [email protected]
Implications for impact research
climate model:
permanentdrizzlingwithin
grid box
hydrological model:
permanent soil moisturization,no peak runoff,
no erosion
MOS WEGE
![Page 7: Heiko Paeth heiko.paeth@uni-wuerzburg.de Statistical postprocessing of simulated precipitation – perspectives for impact research IMSC 2010 Heiko Paeth](https://reader030.vdocument.in/reader030/viewer/2022032800/56649d395503460f94a13d72/html5/thumbnails/7.jpg)
Heiko Paeth [email protected]
MOS: methodology
MOS
multiplelinear
regressionmodel
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 selectedaccording to sig. test
+
![Page 8: Heiko Paeth heiko.paeth@uni-wuerzburg.de Statistical postprocessing of simulated precipitation – perspectives for impact research IMSC 2010 Heiko Paeth](https://reader030.vdocument.in/reader030/viewer/2022032800/56649d395503460f94a13d72/html5/thumbnails/8.jpg)
Heiko Paeth [email protected]
MOS: characteristics
explainedvariance(August)
number ofpredictors
(August)
type ofpredictors
![Page 10: Heiko Paeth heiko.paeth@uni-wuerzburg.de Statistical postprocessing of simulated precipitation – perspectives for impact research IMSC 2010 Heiko Paeth](https://reader030.vdocument.in/reader030/viewer/2022032800/56649d395503460f94a13d72/html5/thumbnails/10.jpg)
Heiko Paeth [email protected]
MOS: resultsmonthly precipitation variability
REMO(adj) – CRU(total STD)
REMO - CRU(total STD)
![Page 11: Heiko Paeth heiko.paeth@uni-wuerzburg.de Statistical postprocessing of simulated precipitation – perspectives for impact research IMSC 2010 Heiko Paeth](https://reader030.vdocument.in/reader030/viewer/2022032800/56649d395503460f94a13d72/html5/thumbnails/11.jpg)
Heiko Paeth [email protected]
WEGE: methodology
virtual station rainfall(result)
simulatedgrid-box
precipitation(dynamical part)
local topography(physical part)
v
random distributionin space
(stochastical part)
probability matching
model obs.
![Page 12: Heiko Paeth heiko.paeth@uni-wuerzburg.de Statistical postprocessing of simulated precipitation – perspectives for impact research IMSC 2010 Heiko Paeth](https://reader030.vdocument.in/reader030/viewer/2022032800/56649d395503460f94a13d72/html5/thumbnails/12.jpg)
Heiko Paeth [email protected]
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)
![Page 13: Heiko Paeth heiko.paeth@uni-wuerzburg.de Statistical postprocessing of simulated precipitation – perspectives for impact research IMSC 2010 Heiko Paeth](https://reader030.vdocument.in/reader030/viewer/2022032800/56649d395503460f94a13d72/html5/thumbnails/13.jpg)
Heiko Paeth [email protected]
WEGE: results
mean daily precipitation intensity mean daily precipitation variability
![Page 14: Heiko Paeth heiko.paeth@uni-wuerzburg.de Statistical postprocessing of simulated precipitation – perspectives for impact research IMSC 2010 Heiko Paeth](https://reader030.vdocument.in/reader030/viewer/2022032800/56649d395503460f94a13d72/html5/thumbnails/14.jpg)
Heiko Paeth [email protected]
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