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

使用育种和集成卡尔曼方法以及全球模型 GME 生成、验证和校准概率定量降水预报

• 批准号: 5426663
• 负责人: Professor Dr. Andreas N. Hense
• 金额: --
• 依托单位: Max-Planck-Institut für Meteorologie (MPI-M)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2004
• 资助国家: 德国
• 起止时间: 2003-12-31 至 2010-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/5426663?language=en
• 关键词: Generation; validation; calibration; probabilistic; quantitative

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.

提前时间为3-5天的天气时间尺度的定量降水预报本质上是概率性的。预报降水潜在概率密度函数的唯一可行方法是集合模拟。我们将基于育种技术建立全球模式GME的综合预报系统。有了集合预测系统，就可以根据个体实现的分布来估计第一次猜测的协方差矩阵。这些想法被称为集成卡尔曼滤波，它将与目前正在开发的DWD的3D-Var分析一起实现。整个系统将首先通过使用文献中的标准分数、单个观测和集水区的平均值来验证。标准分数将通过地质统计学的新方法进行扩展，以解释空间代表性误差。在建立了这个系统之后，我们将在统计问题的贝叶斯观点中引入验证和集成卡尔曼滤波的新思想。