| Description |
Accurate weather forecasting in complex terrain is of great importance, yet it is a challenging problem due to a number of difficulties, including sparse observations, terrain misrepresentation in numerical models, and model errors related to terrain complexity. Owing to these limitations, few previous studies in data assimilation have emphasized regions of complex terrain. This dissertation presents the first comprehensive evaluation of data assimilation methods and forecast error characteristics for near-surface atmospheric variables in complex terrain. The mesoscale community Weather Research and Forecasting (WRF) model and an advanced ensemble Kalman filter (EnKF) data assimilation system are employed. First, the capability of the advanced EnKF method in assimilating near-surface observations (2-m temperature and 10-m wind) is examined in an observing system simulation experiments framework and compared with the traditional three-dimensional variational data assimilation (3DVAR) method. Results indicated that the EnKF is able to effectively assimilate surface observations and improve the short-range weather forecasts, while the 3DVAR method has fundamental problems in assimilating surface observations. Next, the performance of the WRF model in predicting near-surface atmospheric temperature and wind conditions under various terrain and weather regimes is examined. The WRF model is able to simulate these weather phenomena reasonably well. Forecasts of near-surface variables in flat terrain generally agree well with observations. In complex terrain, forecasts not only suffer from the model's inability to reproduce accurate atmospheric conditions in the lower atmosphere but also struggle with representative issues due to mismatches between the model and the actual terrain. A statistical analysis during a 1-month period over the Dugway Proving Ground (DPG), Utah illustrates that forecast errors in near-surface variables depend strongly on the diurnal variation in surface conditions, especially when synoptic forcing is weak. Finally, the impact of observations from the recent field experiments of the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) is examined with EnKF. Results illustrated that the quality of the EnKF/WRF analysis is generally high and the short-range forecast errors are comparable to those of the National Centers for Environmental Prediction (NCEP) North American Mesoscale Model (NAM) forecasts for both 10-m wind speed and direction. |
