Seasonal Prediction of Severe Convective Weather Parameters using the Climate Forecast System Version 2

The prospect for skillful long-term predictions of atmospheric conditions known to directly contribute to the onset and maintenance of severe convective storms remains unclear. This has motivated our ongoing assessment of NOAA Climate Forecast System Version 2 (CFSv2) forecasts over multiple time periods during the climatological peaks in severe convective weather activity. Such assessment utilizes real-time CFSv2 model runs (rather than reforecasts or reanalyses), and is conducted using objectively analyzed upper-air sounding data over the contiguous United States. Our particular focus is on forecasts of convective environments, which we are statistically downscaling in the form of environmental parameters, and also dynamically downscaling via convection-permitting mesoscale models.

Because environmental convective available potential energy (CAPE) and deep layer vertical wind shear can be used to distinguish an atmosphere conducive to intense rotating convection from one supportive of primarily non-severe ‘ordinary' convection, we have limited our assessment to these two parameters. Each is addressed individually to avoid the potential of one robust parameter masking the weakness of another.

Early results indicate potential value in CFSv2 forecasts of CAPE over periods as long as multiple weeks. A specific example of noteworthy skill occurred during a multiple day severe weather outbreak over the Great Plains in late May 2013. Both the 1-week and 4-week CAPE forecasts valid on 19 May 2013 had comparatively low root mean squared error (RMSE) values and high Spearman rank correlation, demonstrating skill both in terms of magnitude and geographic location. A comprehensive analysis of skill scores for forecasts with lead times of one week to more than a month during 2013 and 2014 will be presented and discussed. Conceivable operationally based products focused on probabilistic outcomes designed to benefit the forecasting community will be drawn from the results of the research.