Improving short-term, near-surface temperature forecasts by integrating weather pattern information into Model Output Statistics

Matthias Zech aGerman Aerospace Center, Institute of Networked Energy Systems

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Lueder von Bremen aGerman Aerospace Center, Institute of Networked Energy Systems

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Abstract

Dynamical numerical weather prediction has remarkably improved over the last decades. Yet, postprocessing techniques are needed to calibrate forecasts which are based on statistical and Machine Learning techniques. With recent advances in the derivation of year-round, large-scale atmospheric circulations, or weather regimes, the question arises of whether this information can be valuable within forecast postprocessing methods. This paper investigates this by proposing a bias correction scheme to integrate the atmospheric circulation state derived from empirical orthogonal functions, referred to as weather patterns, for deterministic short-term, near-surface temperature forecasts based on LASSO regression. We propose a computational study which first evaluates different weather pattern definitions (spatial domain) to improve temperature forecasts in Europe. As a bias could be associated with the weather pattern at the model initialization time or at the realization time of the forecast, both variants are tested in this study. We show that forecasted weather patterns with the identical spatial domain as the forecast show best skill reaching Mean Squared Error Skill improvements of up to 3% (day-ahead) or 1% respectively (week ahead). Only considering land surface improvements in Europe, improvements of 4-6% for day-ahead and 1 to 5% for week-ahead forecasts are observable. We believe that this study not only introduces a simple yet effective tool to reduce bias in temperature forecasts but also contributes to the active discussion of how valuable weather patterns are and how to use them within forecast calibration techniques.

© 2024 American Meteorological Society. This is an Author Accepted Manuscript distributed under the terms of the default AMS reuse license. For information regarding reuse and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Matthias Zech, matthias.zech@dlr.de

Abstract

Dynamical numerical weather prediction has remarkably improved over the last decades. Yet, postprocessing techniques are needed to calibrate forecasts which are based on statistical and Machine Learning techniques. With recent advances in the derivation of year-round, large-scale atmospheric circulations, or weather regimes, the question arises of whether this information can be valuable within forecast postprocessing methods. This paper investigates this by proposing a bias correction scheme to integrate the atmospheric circulation state derived from empirical orthogonal functions, referred to as weather patterns, for deterministic short-term, near-surface temperature forecasts based on LASSO regression. We propose a computational study which first evaluates different weather pattern definitions (spatial domain) to improve temperature forecasts in Europe. As a bias could be associated with the weather pattern at the model initialization time or at the realization time of the forecast, both variants are tested in this study. We show that forecasted weather patterns with the identical spatial domain as the forecast show best skill reaching Mean Squared Error Skill improvements of up to 3% (day-ahead) or 1% respectively (week ahead). Only considering land surface improvements in Europe, improvements of 4-6% for day-ahead and 1 to 5% for week-ahead forecasts are observable. We believe that this study not only introduces a simple yet effective tool to reduce bias in temperature forecasts but also contributes to the active discussion of how valuable weather patterns are and how to use them within forecast calibration techniques.

© 2024 American Meteorological Society. This is an Author Accepted Manuscript distributed under the terms of the default AMS reuse license. For information regarding reuse and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Matthias Zech, matthias.zech@dlr.de
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