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

准确的天气预报在了解和减轻气候变化带来的风险以及预测可再生能源的发电量方面发挥着重要作用。今天的天气预报是通过数值天气预报(NWP)模式进行的。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报。为了能够评估预报的不确定性，通常的做法是使用通过多次运行具有不同初始条件和/或模式公式的数值预报模式而获得的数值预报集合。然而，这些所谓的集合预报通常缺乏校准，需要统计后处理。已经开发了几种统计后处理方法来考虑不同天气变量的需要。特别重要的是要扩展后处理模型，以便它们明确地包含空间、时间或天气变量之间的相关性。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变量相关性进行非常灵活和数据驱动的建模。其目的是调整VINE COPLA以用于统计集合后处理。具体地说，计划开发基于Vine Copula的不同天气变量的后处理模型，如温度、风速、降水、云量和太阳辐照度。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，并在联合方法中执行转换为预测各自的功率输出。在下一步，这些模型将被扩展到多变量环境，包括时间、空间和天气变量之间的相关性，并尝试对所有这些相关性进行联合建模。所开发的模型应该在统计软件包R中实现，并在一项研究中与最先进的后处理模型进行比较，该研究调查了后处理预测的预测性能和校准特性。

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.