12B.4 Optimizing a Sensitivity-based Ensemble Subsetting Technique for Convective-scale Forecasts

Thursday, 16 January 2020: 11:15 AM
258A (Boston Convention and Exhibition Center)
Austin A Coleman, Texas Tech Univ., Lubbock, TX; and B. C. Ancell

Ensemble sensitivity analysis (ESA) is a useful and computationally inexpensive tool for analyzing how features in the flow at early forecast times affect different relevant forecast features later in the forecast window. Given the surplus of observations relative to data assimilation cycles, ensemble sensitivity may be used to increase predictability and forecast accuracy through an objective ensemble subsetting technique. This technique identifies ensemble members with the lowest errors in regions of high sensitivity to produce a smaller, more representative ensemble subset.

While ensemble subsets have been shown to reduce forecast errors with statistical significance for synoptic-scale systems, its application to convective-scale forecast features is still being explored. Current work investigates the utility of an objective, convective-scale ensemble subsetting technique. Objective verification of the sensitivity-based ensemble subsetting technique is conducted for probabilistic forecasts of 2-5 km updraft helicity (UH) and simulated reflectivity. The subsetting procedure is optimized for fractions skill scores and reliability metrics through a rigorous permutation experiment performed on these forecasts. A plethora of degrees of freedom are varied to identify the lead times, subset sizes, forecast thresholds, and atmospheric predictors that provide most forecast benefit. Certain UH and reflectivity subset configurations consistently improve both verification metrics relative to their respective full ensemble forecasts. More subset configurations consistently improve simulated reflectivity forecasts than UH forecasts, likely owing to the status of reflectivity as a directly-verifiable quantity. To examine the degree to which limitations of UH verification are detrimental to the optimization of this technique and likely other ensemble post-processing techniques, experiments are performed in an idealized framework in which one ensemble member is randomly-selected as truth. These results and their broader implications for convective-scale predictability are discussed.

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