Mitigating climate risks by improving weather forecasts using copulabased approaches for post-processing (PP) of forecast ensembles

使用基于联结函数的预测集合后处理 (PP) 方法改进天气预报，从而减轻气候风险

• 批准号: 520017589
• 负责人: Professorin Dr. Claudia Czado
• 金额: --
• 依托单位: Lehrstuhl für Mathematische Statistik (M4)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/520017589?language=en
• 关键词: Mitigating; climate; risks; improving; weather

Accurate weather predictions play an important role in understanding and mitigating risks induced by climate change, as well as for predicting power outputs of renewable energies. Weather prediction today is conducted via numerical weather prediction (NWP) models. The output of a model run is a single deterministic forecast of future weather events or variables. To be able to assess forecast uncertainty it has become common practise to use an ensemble of NWP forecasts obtained by running the NWP model multiple times with different initial conditions and/or model formulations. However, these so-called ensemble forecasts typically lack calibration and require statistical postprocessing. Several statistical postprocessing methods have been developed to account for the needs of different weather variables. It has become specifically important to extend the postprocessing models so that they explicitly incorporate dependencies e.g. in space, time or between weather variables. This project aims at developing new types of postprocessing models based on vine copulas. Vine-copulas allow for very flexible and data driven modelling of any type of multivariate dependence. The aim is to adapt the vine copulas for use in the context of statistical ensemble postprocessing. Specifically, it is planned to develop vine copula based postprocessing models for different weather variables, such as temperature, wind speed, precipitation, cloud cover and solar irradiance. The vine copula based quantile regression will also be utilized for postprocessing renewable energy weather variables like wind speed and solar irradiance and performing conversion to prediction of the respective power output in a joint approach. In a next step these models are supposed to be extended to the multivariate context, incorporating dependencies in time, in space, and between weather variables and also attempting to model all these dependencies jointly. The developed models are supposed to be implemented within the statistics software package R, and to be compared to state-of-the-art postprocessing models in a study investigating predictive performance a calibration properties of the postprocessed forecasts.

准确的天气预报对于了解和减轻气候变化引起的风险以及预测可再生能源的发电量发挥着重要作用。今天的天气预报是通过数值天气预报（NWP）模型进行的。模型运行的输出是对未来天气事件或变量的单一确定性预测。为了能够评估预报的不确定性，使用通过使用不同的初始条件和/或模型公式多次运行 NWP 模型而获得的 NWP 预报集合已成为常见做法。然而，这些所谓的集合预测通常缺乏校准，并且需要统计后处理。已经开发了几种统计后处理方法来满足不同天气变量的需求。扩展后处理模型变得特别重要，以便它们明确地包含依赖关系，例如在空间、时间或天气变量之间。该项目旨在开发基于 vine copula 的新型后处理模型。 Vine-copula 允许对任何类型的多元依赖关系进行非常灵活且数据驱动的建模。目的是调整 vine copula 以用于统计集成后处理。具体来说，计划针对不同的天气变量（例如温度、风速、降水、云量和太阳辐照度）开发基于 vine copula 的后处理模型。基于 vine copula 的分位数回归还将用于后处理可再生能源天气变量，如风速和太阳辐照度，并以联合方法执行转换以预测相应的功率输出。下一步，这些模型应该扩展到多变量环境，纳入时间、空间和天气变量之间的依赖关系，并尝试联合建模所有这些依赖关系。开发的模型应该在统计软件包 R 中实现，并在研究预测性能和后处理预测的校准特性的研究中与最先进的后处理模型进行比较。