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Lookup NU author(s): Dr Hannah BloomfieldORCiD
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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. https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/met.2018
Author(s): Bloomfield HC, Brayshaw DJ, Gonzalez PLM, Charlton-Perez A
Publication type: Article
Publication status: Published
Journal: Meteorological Applications
Year: 2021
Volume: 28
Issue: 4
Print publication date: 30/07/2020
Acceptance date: 14/07/2021
ISSN (print): 1350-4827
ISSN (electronic): 1469-8080
Publisher: Royal Meteorological Society
URL: https://doi.org/10.1002/met.2018
DOI: 10.1002/met.2018
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