Pattern‐based conditioning enhances sub‐seasonal prediction skill of European national energy variables

Hannah C. Bloomfield, David J. Brayshaw, Paula L. M. Gonzalez, Andrew Charlton‐Perez

Research output: Contribution to journalArticle (Academic Journal)peer-review

12 Citations (Scopus)
67 Downloads (Pure)


Sub-seasonal forecasts are becoming more widely used in the energy sector to inform high-impact, weather-dependent decisions. Using pattern-based methods (such as weather regimes) is also becoming commonplace, although until now an assessment of how pattern-based methods perform compared with gridded model output has not been completed. We compare four methods to predict weekly-mean anomalies of electricity demand and demand-net-wind across 28 European countries. At short lead times (days 0–10) grid-point forecasts have higher skill than pattern-based methods across multiple metrics. However, at extended lead times (day 12+) pattern-based methods can show greater skill than grid-point forecasts. All methods have relatively low skill at weekly-mean national impact forecasts beyond day 12, particularly for probabilistic skill metrics. We therefore develop a method of pattern-based conditioning, which is able to provide windows of opportunity for prediction at extended lead times: when at least 50% of the ensemble members of a forecast agree on a specific pattern, skill increases significantly. The conditioning is valuable for users interested in particular thresholds for decision-making, as it combines the dynamical robustness in the large-scale flow conditions from the pattern-based methods with local information present in the grid-point forecasts.
Original languageEnglish
Article numbere2018
JournalMeteorological Applications
Issue number4
Early online date30 Jul 2021
Publication statusPublished - Jul 2021

Bibliographical note

Funding Information:
This work was conducted as part of the sub‐seasonal to seasonal forecasting for energy (S2S4E) project, which was funded by the European Union's Horizon 2020 Research and Innovation Program (grant agreement number 776787). We thank Dominik Büeler, Christian Grams, Jan Wohland, Simon Lee and all the S2S4E project partners for useful discussions in the development of these data sets. We also thank the reviewers for their very useful feedback.

Publisher Copyright:
© 2021 The Authors. Meteorological Applications published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society.


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