Tim Böhme

Long-term evaluation of COSMO forecasting using combined observational data of the GOP period

Data of two years of observations (2007-2008) from the General Observation Period (GOP) are used to evaluate forecasts of the operational COSMO model applications (COSMO-DE and COSMO-EU) of the German Weather Service (DWD). As part of the German Priority Programme on Quantitative Precipitation Forecasting (PQP), the GOP gathered a comprehensive data set from existing instrumentation not used in routine verification and corresponding model output. In this paper we focus on the water cycle variables: integrated water vapor (IWV), cloud base height (CBH) and precipitation. In addition brightness temperatures (BT) from satellite observations are included. The biases in IWV and BT 6.2 μm data are small for COSMO-DE and COSMO-EU. CBH data show a larger bias with a maximum in the summer season. The largest biases have been found in the precipitation and BT 10.8 μm data. The latter can probably be explained by deficiencies in modelled clouds in the upper troposphere. A classification into different weather condition types gives some additional insight into model deficits. For northerly/north-westerly (maritime) flows model forecasts are too dry (cold) and for southerly (continental) flows too humid (warm).

Evaluation of microphysical assumptions of the COSMO model using radar and rain gauge observations

The exact forecast of precipitation is a challenge. New microphysics formulations were introduced recently into the COSMO model in order to improve the precipitation forecast. An important modification was the change from the autoconversion and accretion scheme following the Kessler (1969) formulation to the Seifert and Beheng (2001) scheme. The other main modification was implemented in the snow parameterisation by replacing the constant intercept parameter to a temperature dependent intercept parameter. These micro-physics modifications are evaluated in detail in three case studies (one stratiform and two convective cases) by comparing the modelled and observed reflectivity and precipitation data. Comparisons to weather radar reflectivity data show that especially light to moderate precipitation forecast (< 20 dB) is improved. For the evaluation of the modelled precipitation, weather radar and rain gauge data are combined in order to get spatially high resolution data of high accuracy. For the quality analysis, the new error measure SAL (analysis of structure, amplitude and location) is used. The results show that the new microphysics formulations improve the precipitation amplitude forecast of up to 50% for the convective cases while the forecast for the stratiform case is not improved.