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Statistical postprocessing of ensemble forecasts for severe weather at Deutscher Wetterdienst
Nonlinear Processes in Geophysics ( IF 1.7 ) Pub Date : 2020-10-06 , DOI: 10.5194/npg-27-473-2020
Reinhold Hess

This paper gives an overview of Deutscher Wetterdienst's (DWD's) postprocessing system called Ensemble-MOS together with its motivation and the design consequences for probabilistic forecasts of extreme events based on ensemble data. Forecasts of the ensemble systems COSMO-D2-EPS and ECMWF-ENS are statistically optimised and calibrated by Ensemble-MOS with a focus on severe weather in order to support the warning decision management at DWD. Ensemble mean and spread are used as predictors for linear and logistic multiple regressions to correct for conditional biases. The predictands are derived from synoptic observations and include temperature, precipitation amounts, wind gusts and many more and are statistically estimated in a comprehensive model output statistics (MOS) approach. Long time series and collections of stations are used as training data that capture a sufficient number of observed events, as required for robust statistical modelling. Logistic regressions are applied to probabilities that predefined meteorological events occur. Details of the implementation including the selection of predictors with testing for significance are presented. For probabilities of severe wind gusts global logistic parameterisations are developed that depend on local estimations of wind speed. In this way, robust probability forecasts for extreme events are obtained while local characteristics are preserved. The problems of Ensemble-MOS, such as model changes and consistency requirements, which occur with the operative MOS systems of the DWD are addressed.

中文翻译:

Deutscher Wetterdienst恶劣天气的集合预报的统计后处理

本文概述了称为Ensemble-MOS的Deutscher Wetterdienst(DWD)后处理系统,以及基于整体数据的极端事件概率预报的动机和设计结果。Ensemble-MOS对综合系统COSMO-D2-EPS和ECMWF-ENS的预报进行了统计优化和校准,重点是恶劣天气,以支持DWD的预警决策管理。集合均值和散度用作线性和逻辑多元回归的预测变量,以校正条件偏差。预测值来自天气观测,包括温度,降水量,阵风等,并以综合模型输出统计(MOS)方法进行统计估算。长时间序列和站点集合用作训练数据,可以捕获足够数量的观测事件,这是鲁棒统计建模所必需的。Logistic回归适用于发生预定义气象事件的概率。介绍了实现的详细信息,包括选择具有显着性检验的预测变量。对于大风的可能性,开发了依赖于本地风速估计的全局逻辑参数化。通过这种方式,可以在保留局部特征的同时获得针对极端事件的鲁棒概率预测。解决了随DWD的MOS系统发生的Ensemble-MOS问题,例如模型更改和一致性要求。根据强大的统计建模的要求。Logistic回归适用于发生预定义气象事件的概率。介绍了实现的详细信息,包括选择具有显着性检验的预测变量。对于大风的可能性,开发了依赖于本地风速估计的全局逻辑参数化。通过这种方式,可以在保留局部特征的同时获得针对极端事件的鲁棒概率预测。解决了随DWD的MOS系统发生的Ensemble-MOS问题,例如模型更改和一致性要求。根据强大的统计建模的要求。Logistic回归适用于发生预定义气象事件的概率。介绍了实现的详细信息,包括选择具有显着性检验的预测变量。对于大风的可能性,开发了依赖于本地风速估计的全局逻辑参数化。通过这种方式,可以在保留局部特征的同时获得针对极端事件的鲁棒概率预测。解决了随DWD的MOS系统发生的Ensemble-MOS问题,例如模型更改和一致性要求。介绍了实现的详细信息,包括选择具有显着性检验的预测变量。对于大风的可能性,开发了依赖于本地风速估计的全局逻辑参数化。通过这种方式,可以在保留局部特征的同时获得针对极端事件的鲁棒概率预测。解决了随DWD的MOS系统发生的Ensemble-MOS问题,例如模型更改和一致性要求。介绍了实现的详细信息,包括选择具有显着性检验的预测变量。对于大风的可能性,开发了依赖于本地风速估计的全局逻辑参数化。通过这种方式,可以在保留局部特征的同时获得针对极端事件的鲁棒概率预测。解决了随DWD的MOS系统发生的Ensemble-MOS问题,例如模型更改和一致性要求。
更新日期:2020-10-06
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