Generation, validation and calibration of probabilistic quantitative precipitation forecasts using breeding and Ensemble Kalman methods with the global model GME

点击翻译按钮获取中文摘要

Quantitative precipitation forecasts (QPF) with a lead time of synoptic time scales of 3-5 days are intrinsically probabilistic. The only feasible method for predicting estimates of the underlying probability density function of precipitation amount are ensemble simulations. We will set up an ensemble forecast systems for the global model GME based an the breeding technique. With an ensemble forecasting system available, the covariance matrix of the first guess can be estimated from the spread of the individual realizations. These ideas are known as Ensemble Kalman filtering which will be implemented together with the 3D-Var analyis of DWD currently under developement. The complete system will be validated first by using standard scores from literatur an single observations and averages for catchments. Standard scores will be extended through a new method from geostatistics to account for the spatial representativity error. Having set up this system we will include new ideas in validation and Ensemble Kalman filtering arising from a Bayesian view of the statistical problems.