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The ensemble transform Kalman filter (ET KF) is currently used at the National Centers for Environmental Prediction (NCEP) to identify deployments of aircraft-borne dropwindsondes that are likely to significantly improve 1-3 day forecasts of winter storms over the continental United States. It is unique among existing targeted observing strategies in that it attempts to predict the reduction in forecast-error variance associated with each deployment of targeted observations. To achieve this, the ET KF predicts the variance of ‘signals’ for each feasible deployment, where a signal represents the difference between two forecasts, initialized with and without the targeted observations. For linear forecast-error evolution, the signal variance is equal to the reduction in forecasterror variance, provided that observation-and background-error covariances are accurately specified and identical to those produced by the operational …