Abstract In numerical weather prediction models, point thunderstorm forecasts tend to have little predictive value beyond a few hours. Thunderstorms are difficult to predict due largely to their typically small size and correspondingly limited intrinsic predictability. We present an algorithm that predicts the probability of thunderstorm occurrence by blending multiple ensemble predictions. It combines several post-processing steps: spatial neighbourhood smoothing, dressing of probability density functions, adjusting sensitivity to model output, ensemble weighting, and calibration of the output probabilities. These operators are tuned using a machine learning technique that optimizes forecast value measured by event detection and false alarm rates. An evaluation during summer 2018 over western Europe demonstrates that the method can be deployed using about a month of historical data. Post-processed thunderstorm probabilities are substantially better than raw ensemble output. Forecast ranges from 9 hours to 4 days are studied using four ensembles: a three-member lagged ensemble, a 12-member non-lagged limited area ensemble, and two global ensembles including the recently implemented ECMWF thunderstorm diagnostic. The ensembles are combined in order to produce forecasts at all ranges. In most tested configurations, the combination of two ensembles outperforms single-ensemble output. The performance of the combination is degraded if one of the ensembles used is much worse than the other. These results provide measures of thunderstorm predictability in terms of effective resolution, diurnal variability and maximum forecast horizon.

中文翻译：

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通过混合多个集合进行概率性雷暴预报

摘要 在数值天气预报模型中，点雷暴预报往往在几个小时后几乎没有预测价值。雷暴很难预测，主要是因为它们通常很小，而且固有的可预测性也相应有限。我们提出了一种通过混合多个集合预测来预测雷暴发生概率的算法。它结合了几个后处理步骤：空间邻域平滑、概率密度函数的修整、调整对模型输出的敏感性、集成加权和输出概率的校准。这些算子使用机器学习技术进行调整，该技术优化了由事件检测和误报率测量的预测值。2018 年夏季对西欧的评估表明，该方法可以使用大约一个月的历史数据进行部署。后处理的雷暴概率大大优于原始集合输出。使用四个集合来研究从 9 小时到 4 天的预测范围：一个三成员滞后集合、一个 12 成员非滞后有限区域集合和两个全球集合，包括最近实施的 ECMWF 雷暴诊断。这些集合被组合起来以产生所有范围的预测。在大多数经过测试的配置中，两个集成的组合优于单集成输出。如果使用的一个集成比另一个更差，则组合的性能会下降。这些结果提供了有效分辨率方面的雷暴可预测性措施，