The tenth IMSC, Beijing, China, 2007 - International Meetings on ...

conditioned on some indices of the atmospheric variables. Due to these weather states the
spatial precipitation dependence can be partially or completely captured. In the present study
various NHMMs are used to relate daily precipitation at 30 rain gauge stations covering
broadly the territory of Bulgaria to synoptic atmospheric data. At each site a 40-year record
(1960-2000) of daily precipitation amounts is modeled. <strong>The</strong> atmospheric data consists of daily
sea-level pressure, geopotential height at 500 hPa, air temperature at 850 hPa and relative
humidity at 700 hPa (subset of NCEP-NCAR reanalysis dataset) on a 2.5° x 2.5° grid covering
the Europe-Atlantic sector 30°W–60°E, 20°N–70°N for the same period. <strong>The</strong> first 30 years
data are used for model fitting purposes while the remaining 10 years are used for model
evaluation. Detailed model validation is carried out on various aspects. <strong>The</strong> identified weather
states are found to be physically interpretable in terms of regional climatology. A comparison is
also made between the NHMM and classical at site two-state first-order non-stationary Markov
stochastic daily precipitation model, conditional on a summary of 5 pseudotemps (based on all
atmospheric variables in NCEP-NCAR reanalysis dataset) surrounding the territory of
Bulgaria.
References
Neytchev, P., Zucchini,W., Hristov, H. and Neykov, N.M. (2006) Development of a multisite
daily precipitation model for Bulgaria using hidden Markov models. In: Proc. of the XXIIIrd
conference of Danubian countries on the hydrological forecasting and hydrological bases of
water management. Belgrade, Serbia, 28- 31 August.
Bridging the gap between simulated climate and local weather
Heiko Paeth
Institute of Geography, University of Wuerzburg
Heiko.Paeth@uni-wuerzburg.de
Data from global and regional climate models usually refer to grid cells and are, hence,
basically different from station data. This particularly holds for variables with enhanced
spatio-temporal variability like precipitation. On the other hand, many follow-up modelling
study require atmospheric data with the statistical characteristics of station data in order to
simulate for instance realistic surface runoff or erosion rates.
Here, a dynamical-statistical tool is presented to derive virtual station data from regional
climate model output in tropical West Africa. This weather generator incorporates daily gridded
rainfall from the model, an orographic term and a stochastic term, accounting for the chaotic
spatial distribution of local rain events within the model grid box. Total sums and the probability
density function of daily precipitation are adjusted to available station data.It is also assured
that the generated data are still consistent with other model parameters like cloudiness and
atmospheric circulation. <strong>The</strong> resulting virtual station data are in excellent agreement with
various observed characteristics, which do not enter explicitly the weather generator. algorithm.
This holds for the mean daily rainfall intensity and variability, the relative number of rainless
days and the scaling of precipitation over different time scales.
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