Ensemble Sensitivity Analysis for Targeted Observations of Supercell Thunderstorms

Abstract

Ensemble sensitivity analysis (ESA) has been demonstrated for observation targeting of synoptic-scale and mesoscale phenomena, but could have similar applications for storm-scale observations with mobile platforms. This paper demonstrates storm-scale ESA using an idealized supercell simulated with a 101-member CM1 ensemble. Correlation coefficients are used as a measure of sensitivity and are derived from single-variable and multivariable linear regressions of pressure, temperature, humidity, and wind with forecast response variables intended as proxies for the strength of supercells. This approach is suitable for targeting observing platforms that simultaneously measure multiple base-state variables. Although the individual correlations are found to be noisy and difficult to interpret, averaging across small areas of the domain and over the duration of the simulation is found to simplify the analysis. However, it is difficult to identify physically meaningful results from the sensitivity calculations, and evaluation of the results suggests that the overall skill would be low in targeting observations at the storm scale solely based on these sensitivity calculations. The difficulty in applying ESA at the scale of an individual supercell is likely due to applying the linear model to an environment with highly nonlinear dynamics, rapidly changing forecast metrics, and autocorrelation.

A convective‐scale 1,000‐member ensemble simulation and potential applications

“…Precipitation is chosen, as it is a primary forecast quantity of convective‐scale forecasting systems. ESA has been used successfully in various synoptic‐scale (Hakim and Torn, ; Torn and Hakim, ; Torn, ; Hanley et al ., ; Barrett et al ., ) and convective‐scale studies (Bednarczyk and Ancell, ; Wile et al ., ; Hill et al ., ; Limpert and Houston, ). However, most ESA studies relied on fairly small ensemble sizes and focused on the qualitative interpretation of sensitivities.…”

Multivariate Ensemble Sensitivity Analysis for Super Typhoon Haiyan (2013)

“…Using the two-scale model of Lorenz (2005), multivariate ensemble sensitivity analysis had superior skill in predicting the response when compared to univariate ensemble sensitivity analysis with diagonal approximation of the covariance, particularly when fast scales and model errors were presented, and observations were sparse (Hacker and Lei 2015). Limpert and Houston (2018) applied multivariate regression on each grid point in an idealized supercell simulation. But their multivariate sensitivity analysis showed difficulties to identify physically meaningful variables, possibly because the contributions of the covariances from variables at adjacent grid points are neglected.…”

New Perspectives on Ensemble Sensitivity Analysis with Applications to a Climatology of Severe Convection

“…This type of analysis may help particularly when fine-scale processes like convection are considered that possess substantial nonlinearity (Limpert andHouston 2018, Hill et al 2020) and is suggested as an avenue for future ESA enhancement. Such higher-order regressions might identify early atmospheric dependencies that reveal "Goldilocks" behavior for severe weather like tornadoes (Markowski and Richardson 2014) where neither high nor low temperature values in some portion of the storm's evolution, for example, result in tornadoes, but temperatures inbetween are associated with them.…”

“…2013, Chang et al 2013, Ancell 2016, Berman and Torn 2019, convective events (Hanley at al. 2013, Bednarczyk and Ancell 2015, Torn and Romine 2015, Hill et al 2016, Berman et al 2017, Limpert and Houston 2018, Kerr et al 2019, Coleman and Ancell 2020, Hill et al 2020, tropical cyclones (Torn and Hakim 2009, Torn 2010, Nystrom et al 2018, Ren et al 2019, Hu and Wu 2020, and flows in complex terrain with applications to wind power (Zack et al 2010abc, Wile et al 2015, Smith and Ancell 2017. Ensemble sensitivity studies such as these primarily fall into three categories: 1) examining sensitivity fields to understand the relevant dynamics or predictability associated with a high-impact weather event (e.g.…”