Ensemble Sensitivity-Based Subsetting for Convection: Progress Toward Operational Use

Ensemble sensitivity is a statistical tool applied within an ensemble that reveals the atmospheric flow features (e.g. position of a jet streak, or magnitude of a low-level moisture plume) at early forecast times that are related to a chosen forecast response later in the forecast window.  The response function is chosen to diagnose high-impact forecast features such as maximum updraft helicty over a specified area, or number of grid points of simulated reflectivity exceeding 40 dBZ in a chosen region.  Since ensemble sensitivity highlights the features early in a forecast important to the prediction of high-impact features later in the forecast, a subset of members with the smallest errors in sensitive regions can be chosen that might improve probabilistic forecasts of the response relative to the full ensemble. Similar to ensemble data assimilation, this process incorporates observational information to beneficially update forecast distributions.  The subsetting procedure can be done quickly once an ensemble has been run, and sensitivity-based subsets can typically be generated well before the next extended forecast can be run within a cycling storm-scale data assimilation and forecasting system. In turn, the subsetting procedure, if shown to improve forecasts, could be a unique and useful operational forecasting tool.

 

Ensemble sensitivity-based subsetting has been tested within the Texas Tech University operational ensemble system in both an idealized framework and in more operational settings in real time during several years of the National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT).  Response functions that diagnose severe convective hazards, such as updraft helicity, hail size, and simulated reflectivity have been tested to gain an understanding of both the general capability of the technique and the perception of forecasters regarding its value in a real-time forecasting environment.  Here we discuss this effort and its associated results, the technique’s current status, and future plans toward ultimate operational implementation.