The Nature and Variability of Ensemble Sensitivity Fields that Diagnose Severe Convection

Ensemble sensitivity analysis (ESA) is a statistical technique that uses information from an ensemble of forecasts to reveal relationships between chosen forecast metrics and the larger atmospheric state at various forecast times. A number of studies have employed ESA from the perspectives of dynamical interpretation, observation targeting, and ensemble subsetting toward improved probabilistic prediction of high-impact events, mostly at synoptic scales. We tested ESA using convective forecast metrics at the 2016 HWT Spring Forecast Experiment to understand the utility of convective ensemble sensitivity fields in improving forecasts of severe convection and its individual hazards within a real-time ensemble data assimilation/forecasting system. The main purpose of this evaluation was to understand the temporal coherence and general characteristics of convective sensitivity fields, particularly to understand whether they could be used with observations to improve ensemble predictability within an operational framework.

The magnitude and coverage of simulated reflectivity, updraft helicity, and surface wind speed were used as severe convective response functions over a 6-week period, and the sensitivity of these variables to winds, temperatures, geopotential heights, and dew points at different atmospheric levels and at different forecast times are evaluated. The sensitivities are calculated within the Texas Tech real-time ensemble data assimilation/forecasting system, which possesses 42 members that run twice daily to 48-hr forecast time. Here we summarize the findings regarding the nature of the sensitivity fields over the 6-week period and their potential for operational use.