A framework for probabilistic weather forecast post-processing across models and lead times using machine learning

Kirkwood, C., Economou, T., Odbert, H. and Pugeault, N. (2021) A framework for probabilistic weather forecast post-processing across models and lead times using machine learning. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379, 20200099. (doi: 10.1098/rsta.2020.0099) (PMID:33583271)

Abstract

Forecasting the weather is an increasingly data-intensive exercise. Numerical weather prediction (NWP) models are becoming more complex, with higher resolutions, and there are increasing numbers of different models in operation. While the forecasting skill of NWP models continues to improve, the number and complexity of these models poses a new challenge for the operational meteorologist: how should the information from all available models, each with their own unique biases and limitations, be combined in order to provide stakeholders with well-calibrated probabilistic forecasts to use in decision making? In this paper, we use a road surface temperature example to demonstrate a three-stage framework that uses machine learning to bridge the gap between sets of separate forecasts from NWP models and the ‘ideal’ forecast for decision support: probabilities of future weather outcomes. First, we use quantile regression forests to learn the error profile of each numerical model, and use these to apply empirically derived probability distributions to forecasts. Second, we combine these probabilistic forecasts using quantile averaging. Third, we interpolate between the aggregate quantiles in order to generate a full predictive distribution, which we demonstrate has properties suitable for decision support. Our results suggest that this approach provides an effective and operationally viable framework for the cohesive post-processing of weather forecasts across multiple models and lead times to produce a well-calibrated probabilistic output.

• Item Type: Articles
• Additional Information: This work has been conducted as part of CK’s PhD research, funded by the UK’s Engineering and Physical Sciences Research Council (EPSRC project ref: 2071900) and the Met Office.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Pugeault, Dr Nicolas
• Authors: Kirkwood, C., Economou, T., Odbert, H., and Pugeault, N.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
• Publisher: Royal Society
• ISSN: 1364-503X
• ISSN (Online): 1471-2962
• Published Online: 15 February 2021
• Copyright Holders: Copyright © 2021 The Authors
• First Published: First published in Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379:20200099
• Publisher Policy: Reproduced under a Creative Commons License