Statistical postprocessing of ensemble forecasts for various weather quantities

• Applicant: Dr. Annette Möller
• Subject Area: Atmospheric Science ; Statistics and Econometrics
• Term: from 2018 to 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 395388010

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.

• DFG Programme: Scientific Networks