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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.