Predictability of Atmospheric Conditions over Complex Terrain with Ensemble Kalman Filter Data Assimilation: Evaluation with observations from MATERHORN field program
With this study, we examine the impact of data assimilation and high-resolution numerical simulations on the predictability of atmospheric conditions over complex terrain using the mesoscale community Weather Research and Forecasting (WRF) model, an advanced ensemble Kalman Filter (EnKF) data assimilation method, and the observations obtained from MATERHORN.
Results from assimilating surface and sounding observations show that EnKF data assimilation has a positive impact on analyses and forecasts of atmospheric conditions over complex terrain. With the data assimilation, the model reproduces reasonable forecasts to various synoptic and local flows. The flow features over different land types are also distinguished. However, errors in the diurnal variations are still notable. Detailed diagnoses and sensitivity studies are being conducted to understand the factors that influence the predictability and also to seek the ways for improvements.