5 Predictability of Atmospheric Conditions over Complex Terrain with Ensemble Kalman Filter Assimilation of Observations during MATERHORN

Tuesday, 6 August 2013
Holladay-Halsey (DoubleTree by Hilton Portland)
Hailing Zhang, Univ. of Utah, Salt Lake City, UT; and Z. Pu

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 improve 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., balloon, lidar, tower, 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.

Preliminary results from assimilating surface observations show that EnKF data assimilation has a positive impact on forecasts of atmospheric conditions over complex terrain. However, errors in the diurnal transition and synoptic transition periods are still notable. More experiments will emphasize the assimilation of additional sounding profiles and soil observations as well as the sensitivity of data assimilation to the localization scale and ensemble size. The impacts of various data types and various EnKF strategies on the predictability of atmospheric conditions over complex terrain will be evaluated.

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