On the Evaluation of Probabilistic Thunderstorm Forecasts and the Automated Generation of Thunderstorm Threat Areas during Environment Canada Pan Am Science Showcase

Abstract

Abstract An object-based forecasting, nowcasting, and alerting system prototype was demonstrated during the summer 2015 Environment Canada Pan Am Science Showcase (ECPASS) in Toronto. Part of this demonstration involved the generation of experimental thunderstorm threat areas by both automated NWP postprocessing algorithms and by a pair of human forecasters. This paper first develops a rigorous verification methodology for the intercomparison of continuous as well as categorical probabilistic thunderstorm forecasts. The methodology is then applied to the intercomparison of thunderstorm forecasts made during ECPASS. Statistical postprocessing of forecasts by smoothing with optimal bandwidth followed by recalibration is found to improve the skill scores of all thunderstorm forecasts studied at all lead times between 6 and 48 h. In addition, the calibrated ensemble mean forecasts are found to be better than the calibrated deterministic thunderstorm forecasts for all lead times considered, though postprocessing of the convective rain-rate forecast gives results that are statistically comparable with the ensemble mean forecast. Thunderstorm threat areas that were automatically generated by thresholding the output of NWP-based postprocessed algorithms have better scores than those generated by human forecasters for most lead times beyond 9 h, indicating that they could be integrated as an automated tool for providing high-quality “first-guess” thunderstorm threat areas in an object-based forecasting, nowcasting, and alerting system. A unique contribution of this paper is a novel verification methodology for the fair comparison between continuous and categorical probabilistic forecasts, a methodology that could be used for other experiments involving human- and automatically generated object-based forecasts derived from probabilistic forecasts.