Postprocessing multi-model ensemble temperature forecasts using Distributional Regression Networks

Authors: Enric Casellas Masana, Josep Ramon Miró Cubells, and Jordi Moré Pratdesaba

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Introduction

This work presents a comparative analysis of postprocessing techniques for multi-model weather ensemble forecasts (Poor Man's Ensemble).

We explore the application of Ensemble Model Output Statistics (EMOS) using the IMPROVER framework and also two Distributional Regression Network (DRN) approaches to postprocess hourly temperature forecasts.

Acknowledgements

• Meteorological Service of Catalonia for providing data.
• IMPROVER, developed by the MetOffice.