Statistical postprocessing of ensemble forecasts for various weather quantities

各种天气量集合预报的统计后处理

• 批准号: 395388010
• 负责人: Dr. Annette Möller
• 金额: --
• 依托单位: Forschungsgruppe Data Science
• 依托单位国家: 德国
• 项目类别: Scientific Networks
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/395388010?language=en
• 关键词: Statistical; postprocessing; ensemble; forecasts; various

It has become commmon practise to apply statistical postprocessing models on ensembles of numerical weather prediction (NWP) model outputs in to improve the forecast realiability and calibration of the deterministic predictions. A variety of state-of-the-art postprocessing models are available and their application enjoys increasing success. However, many of the standard models are designed to obtain a probabilistic forecast for a single weather variable, at a single spatial locations or time points. In this univariate approaches, possible multivariate dependencies are not explicitly accounted for.We plan to extend and modify existing ensemble postprocessing models in two different directions. On the one hand we will develop versions of existing state-of-the-art models that are suitable for non-Gaussian distributed weather variables. On the other hand we will extend the Gaussian and non-Gaussian models to a multivariate setting incoroprating dependencies among different weather quantities, spatial locations and time points.Explicitly modelling these types of dependence structures in the process of weather prediction is of increasing importance for various economic and social sectors, which require physically coherent predictions of future weather events such as wind speed, precipitation or of hydrological events as river discharge. The development of multivariate statistical postprocessing models is a highly active area of research. Thererfore, we plan to implement the developed models within the statistical software environment R and harmonize the new implementations with existing ones to make existing and new models available to a large group of potential users and researchers.

将统计后处理模型应用于数值天气预测（NWP）模型输出以提高确定性预测的预测真实性和校准，这已成为Commmon实践。提供了各种最先进的后处理模型，其应用程序的成功越来越大。但是，许多标准模型旨在在单个空间位置或时间点获得单个天气变量的概率预测。在这种单变量方法中，未明确考虑可能的多元依赖性。我们计划在两个不同的方向上扩展和修改现有的集合后处理模型。一方面，我们将开发适合非高斯分布式天气变量的现有最新模型的版本。另一方面，我们将将高斯和非高斯模型扩展到不同天气数量，空间位置和时间点之间的多元设置依赖性。在天气预测过程中对这些类型的依赖性结构进行建模是对各种经济和社交部门的重要性，这是对各种经济和社交部门的重要性，这些部门需要沿未来的天气迅速进行危险的事件，例如，危险事件的危险事件或速度迅速进行。多元统计后处理模型的发展是一个高度活跃的研究领域。因此，我们计划在统计软件环境R中实施开发的模型，并将新实施与现有实施相一致，以使大量潜在用户和研究人员可用现有模型和新模型。