Study on forecasting a cold-air-damming event with the WRF-EnKF system

This study investigates the impact of different observations on forecasting a cold-air-damming event with WRF (Weather Research and Forecasting) – EnKF (Ensemble Kalman Filter) system. The DART-EnKF (Data Assimilation Research Testbed) modeling system is developed at NCAR (Anderson et al, 2009, BAMS), which contains a suite of Ensemble Kalman Filter (EnKF) approaches and interfaces to several research and operational models. The DART-EnKF system is employed for data assimilation and forecasting experiments for a retrospect cold-air-damming event occurred over the Northeastern States using WRF and the diverse observations contained in NOAA/MADIS real time data stream and other datasets including the newly available TAMDAR (Tropospheric Airborne Meteorological Data Reporting) data from AirDat LLC. The goals of this study include, a) assessing the DART-EnKF capability for real-case data assimilation and forecasting using the Advanced Research WRF model and the abundant observations available for the case period; b) testing the sensitivities of different EnKF implementation in DART and varying background error covariance localization and inflation schemes; and c) evaluating the impact and the effectiveness of EnKF approaches for assimilation of different weather observations, such as the data of different observation platforms, the surface versus upper-air observations, the mass variables (T, Qv) versus winds (U,V). The results show that DART assimilates different observations reasonably well and the radiosonde and ACARS data are most effective. Using wind profiler and satellite winds are less effective, especially at the upper layers.