final_program_abstracts[1]

11 IMSC Session Program
Statistical postprocessing of simulated precipitation –
perspectives for impact research
Wednesday - Parallel Session 5b
Heiko Paeth
Institute of Geography, University of Würzburg, Germany
Rainfall represents an important factor in agriculture and food security, particularly,
in the low latitudes. Climatological and hydrological studies which attempt to
diagnose the hydrological cycle, require high-quality precipitation data. In West
Africa, like in many parts of the world, the density of observational data is low and
climate models are needed in order to perform homogeneous and complete data sets.
However, climate models tend to produce systematic errors, especially, in terms of
rainfall and cloud processes, which are usually approximated by physical
parameterizations.
A 25-year climatology of monthly precipitation in West Africa is presented, derived
from a regional climate model simulation, and evaluated with respect to observational
data. It is found that the model systematically underestimates the rainfall amount and
variability and does not capture some details of the seasonal cycle in sub-Saharan
West Africa. Thus, in its present form the precipitation climatology is not appropriate
to draw a realistic picture of the hydrological cycle in West Africa nor to serve as
input data for impact research. Therefore, a statistical model is developed in order to
adjust the simulated rainfall data to the characteristics of observed precipitation.
Assuming that the regional climate model is much more reliable in terms of
atmospheric circulation and thermodynamics, model output statistics (MOS) is used
to correct simulated rainfall by means of other simulated parameters of the nearsurface
climate like temperature, sea level pressure and wind components. Monthly
data is adjusted by a cross-validated multiple regression model. The resulting adjusted
rainfall climatology reveals a substantial improvement in terms of the model
deficiencies mentioned above.
In addition, many applications like for instance hydrological models require
atmospheric data with the statistical characteristics of station data. A dynamicalstatistical
tool to construct virtual station data based on regional climate model output
for tropical West Africa is developed. 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 a model grid box. In
addition, the simulated probability density function of daily precipitation is adjusted
to available station data in Benin. The resulting virtual station data are in excellent
agreement with various observed characteristics which are not explicitly addressed by
the algorithm. This holds for the mean daily rainfall intensity and variability, the
relative number of rainless days and the scaling of precipitation in time.
Abstracts 166