Predictability of Atmospheric Conditions over Complex Terrain with Ensemble Kalman Filter Data Assimilation: Evaluation with observations from MATERHORN field program

Weather forecasting in complex terrain remains a challenge due to a number of difficulties, including sparse observations, terrain misrepresentation in numerical models, and model errors related to the complexity of surface conditions. Owing to these limitations, few previous studies in data assimilation have emphasized complex terrain. The most recent field experiments of the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program provide comprehensive observations over mountainous regions, allowing the opportunity to study the predictability of atmospheric conditions over complex terrain. Specifically, MATERHORN is designed to identify and study the limitations of current mesoscale models in predicting weather in mountainous terrain and to develop scientific tools to help realize improvements in predictability. During fall 2012 and spring 2013, comprehensive observations were collected of soil states, surface energy budgets, near-surface atmospheric conditions, and profiling measurements from multiple platforms (e.g., lidar, radiosondes, aircraft etc.) over Dugway Proving Ground (DPG), Utah.

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.