Heiko Paeth [email protected] Statistical postprocessing of simulated precipitation –...

Heiko Paeth [email protected]

Statistical postprocessing of simulated precipitation –

perspectives for impact research

IMSC 2010

Heiko Paeth

Institute of Geography, University of Würzburg, Germany


Diagnosis of model deficienciesannual precipitation totals


Diagnosis of model deficienciesmonthly precipitation variability


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


Implications for impact research

climate model:

permanentdrizzlingwithin

grid box

hydrological model:

permanent soil moisturization,no peak runoff,

no erosion


Implications for impact research

climate model:

permanentdrizzlingwithin

grid box

hydrological model:

permanent soil moisturization,no peak runoff,

no erosion

MOS WEGE


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

+


MOS: characteristics

explainedvariance(August)

number ofpredictors

(August)

type ofpredictors


MOS: resultsannual precipitation totals


MOS: resultsmonthly precipitation variability

REMO(adj) – CRU(total STD)

REMO - CRU(total STD)


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.


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)


WEGE: results

mean daily precipitation intensity mean daily precipitation variability


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

Heiko Paeth [email protected] Statistical postprocessing of simulated precipitation –...

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