heiko paeth heiko.paeth@uni-wuerzburg.de statistical postprocessing of simulated precipitation –...
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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
Heiko Paeth heiko.paeth@uni-wuerzburg.de
Diagnosis of model deficienciesannual precipitation totals
Heiko Paeth heiko.paeth@uni-wuerzburg.de
Diagnosis of model deficienciesmonthly precipitation variability
Heiko Paeth heiko.paeth@uni-wuerzburg.de
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
Heiko Paeth heiko.paeth@uni-wuerzburg.de
Implications for impact research
climate model:
permanentdrizzlingwithin
grid box
hydrological model:
permanent soil moisturization,no peak runoff,
no erosion
Heiko Paeth heiko.paeth@uni-wuerzburg.de
Implications for impact research
climate model:
permanentdrizzlingwithin
grid box
hydrological model:
permanent soil moisturization,no peak runoff,
no erosion
MOS WEGE
Heiko Paeth heiko.paeth@uni-wuerzburg.de
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
+
Heiko Paeth heiko.paeth@uni-wuerzburg.de
MOS: characteristics
explainedvariance(August)
number ofpredictors
(August)
type ofpredictors
Heiko Paeth heiko.paeth@uni-wuerzburg.de
MOS: resultsannual precipitation totals
Heiko Paeth heiko.paeth@uni-wuerzburg.de
MOS: resultsmonthly precipitation variability
REMO(adj) – CRU(total STD)
REMO - CRU(total STD)
Heiko Paeth heiko.paeth@uni-wuerzburg.de
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.
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)
Heiko Paeth heiko.paeth@uni-wuerzburg.de
WEGE: results
mean daily precipitation intensity mean daily precipitation variability
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
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