Severe-weather prediction using convective-scale ensembles

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Convective storms represent a key element of the climate system, with impacts on scales ranging from microscopic aerosol particles up to the global circulation. On the regional scale they provide valuable rainfall that sustains life and agriculture but also inflict severe weather including hailstorms and flash-flooding. A process of such critical importance demands accurate prediction from operational weather centres. However, because of their highly turbulent, chaotic, and multi-scale nature, these storms are tremendously difficult to accurately predict. Even small uncertainties in numerical weather prediction models cause storms to form in the wrong place, at the wrong time, or to not develop at all. Although some level of forecast uncertainty is unavoidable, convection forecasts can still improve by diminishing the magnitude of this uncertainty and by adopting probabilistic methods that quantify its impact on the model outcome.