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

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Pattern-based conditioning enhances sub-seasonal prediction skill of European national energy variables
Meteorological Applications ( IF 2.3 ) Pub Date : 2021-07-30 , DOI: 10.1002/met.2018
Hannah C. Bloomfield ₁ , David J. Brayshaw _{1,

2} , Paula L. M. Gonzalez _{1,

3} , Andrew Charlton‐Perez ₁

Affiliation

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.

中文翻译：

基于模式的调节增强了欧洲国家能源变量的亚季节预测能力

次季节性预报正越来越广泛地用于能源部门，以告知影响大、依赖天气的决策。使用基于模式的方法（例如天气状况）也变得司空见惯，尽管到目前为止尚未完成对基于模式的方法与网格模型输出相比如何执行的评估。我们比较了四种方法来预测 28 个欧洲国家的电力需求和需求净风能的每周平均异常。在较短的交付周期内（0-10 天），跨多个指标的网格点预测比基于模式的方法具有更高的技能。然而，在延长的提前期（第 12 天以上），基于模式的方法可以显示出比网格点预测更高的技能。在第 12 天之后的每周平均国家影响预测中，所有方法的技能都相对较低，特别是对于概率技能指标。因此，我们开发了一种基于模式的调节方法，该方法能够在延长的提前期为预测提供机会窗口：当预测中至少 50% 的整体成员同意特定模式时，技能会显着提高。调节对于对决策的特定阈值感兴趣的用户很有价值，因为它将基于模式的方法的大规模流动条件的动态鲁棒性与网格点预测中存在的局部信息相结合。

更新日期：2021-07-30

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