The local ensemble transform Kalman filter (LETKF, Hunt et al. 2007) is a realistic implementation of ensemble Kalman square root filters for advanced data assimilation and ensemble prediction. The LETKF has been successfully applied to various numerical models including global and regional numerical weather prediction (NWP) models, global and regional ocean models, and even a Martian atmospheric model. Recently the LETKF has been applied to the Weather Research and Forecasting (WRF) model, a widely used nonhydrostatic regional NWP model. The LETKF system includes the recently developed adaptive inflation method. The WRF-LETKF system performed properly with real observations, so that a 9-day assimilation cycle experiment reproduced Typhoon Sinlaku (2008) very well although the typhoon was not generated at all without assimilation.
The ensemble sensitivity method of Liu and Kalnay (2008) is applied to the WRF-LETKF system, and impacts of real observations on short-range forecasts are assessed with additional enhancements of considering localization and introducing a targeted area that are essential for the high-dimensional real application. This ensemble-based method achieves the same goals as the adjoint-based method (Langland and Baker 2004) but without using an adjoint model. The results in the case of Typhoon Sinlaku show that upper-air soundings have the largest impact on improving 12-h forecasts and that the targeted impact evaluation performs as expected with shorter computational time. Denying negative-impact observations actually improves the forecasts, validating the estimated observation impact.