The Ensemble Transform Kalman Filter (ET KF) is currently used operationally at the National Centers for Environmental Prediction (NCEP) to identify deployments of aircraft-borne dropwindsondes that optimize the chance of significantly improving 1-3 day forecasts of winter storms over the continental United States. The ensemble-based technique 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 forecast error variance, provided that observation and background error covariances are accurately specified and identical to those produced by the operational data assimilation scheme. However, model trajectories and background error covariances assumed by the ET KF are both imperfect and different from the imperfect error covariances used in NCEP's 3D-Var data assimilation scheme, and hence their signals are likely to differ.

In spite of these differences, we are able to establish a linear relationship of positive gradient between the ET KF signal variance and the sample variance of NCEP signal realizations at both the targeted analysis and forecast verification times, for 30 forecasts from the 2000 Winter Storm Reconnaissance Program. This relationship enables the NCEP signal variance to be predicted by the ET KF, via a statistical rescaling that corrects the ET KF's current over-prediction of signal variance magnitude. A monotonically increasing relationship is also found to exist between the NCEP signal variance and the reduction in NCEP forecast error variance. The ET KF signal variance predictions can be used to make quantitative estimates of the forecast error variance reducing effect of targeted observations. Potential benefits include (i) making rapid decisions on when and where to deploy targeted observations, (ii) warning operational data quality control schemes against the rejection of observational data if the signal variance is large, and (iii) estimating the likelihood of economic benefit due to any future deployment of observations.