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

定量降水预报（QPF）的天气时间尺度的提前时间为3-5天，本质上是概率。唯一可行的方法来预测降水量的潜在概率密度函数的估计是集合模拟。我们将建立一个基于育种技术的全球模式GME集合预报系统。在集合预报系统可用的情况下，第一猜测的协方差矩阵可以从个体实现的分布估计。这些想法被称为包围卡尔曼滤波，将与目前正在开发的DWD的3D-Var分析一起实施。完整的系统将首先通过使用标准分数从literatur一个单一的观察和平均集水区。标准分数将通过地质统计学的一种新方法加以扩展，以说明空间代表性误差。建立了这个系统，我们将包括新的想法，在验证和包围卡尔曼滤波所产生的贝叶斯观点的统计问题。

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.