期刊名称：Tellus A: Dynamic Meteorology and Oceanography
电子版ISSN：1600-0870
出版年度：2020
卷号：72
期号：1
页码：1-20
DOI：10.1080/16000870.2019.1696142
摘要：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.
