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. An emerging method for achieving the above goals is convective-scale ensembles, which are only now becoming computationally feasible. These consist of numerous forecasts that capture the spread of potential outcomes arising from model-based uncertainties. The ensemble framework also allows observations, notably from precipitation radars, to be naturally assimilated into the model to diminish errors and improve storm predictability. In addition to their practical benefits, these ensembles allow for a deeper understanding of the mechanisms and sensitivities of convective storms. By providing numerous realizations of a given event, they allow for the exposure of subtle process interactions that would otherwise be imperceptible. In this five-year project, a convective-scale ensemble system will be developed and applied to improve the physical understanding and prediction of convective storms over Canada. This will be undertaken in collaboration with Environment Canada, which will maximize the resources available for research progress and facilitate the transfer of our findings into operational practice.