Evaluation of high-resolution surface analyses and forecasts with ensemble data assimilation in regions of complex terrain

Accurate, high-resolution atmospheric surface analyses and short-range forecasts are important from many reasons. Our recent studies suggest that the errors in atmospheric surface analysis and forecast are flow-dependent. Because of its flow-dependent background error term, ensemble Kalman filter (EnKF) is capable of better assimilating near-surface observations over complex terrain. In this study, we performed data assimilation and numerical prediction experiments over the Intermountain West region with the observations obtained from the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) field program using an advanced research version of Weather Research and Forecasting (WRF) model and an ensemble Kalman filtering system developed by NCAR Data Assimilation Research Testbed (DART/WRF). Results show EnKF data assimilation improves the prediction of near surface atmospheric conditions, specifically in short-range forecasts. However, analyses and forecast results are sensitive to the model resolution and various configurations of data assimilation system. Specifications of terrain heights and land use parameters, as well as the soil state and coupling between land and atmosphere in the WRF model all have significant impacts on the forecasts of near surface atmospheric conditions. Research results and their implications for operational practices are discussed.