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

Bloomfield, H. C. ORCID: https://orcid.org/0000-0002-5616-1503, Brayshaw, D. J. ORCID: https://orcid.org/0000-0002-3927-4362, Gonzalez, P. L. M. ORCID: https://orcid.org/0000-0003-0154-0087 and Charlton-Perez, A. ORCID: https://orcid.org/0000-0001-8179-6220 (2021) Pattern-based conditioning enhances sub-seasonal prediction skill of European national energy variables. Meteorological Applications, 28 (4). e2018. ISSN 1469-8080 doi: 10.1002/met.2018

Abstract/Summary

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 to 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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/99445
Identification Number/DOI	10.1002/met.2018
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > NCAS Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Royal Meteorological Society
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