Ensemble Kalman Filter Optimization for Convection-Permitting Forecasts in Complex Terrain

Weather forecasting in complex terrain remains challenging due to mountainous topography, which generates high spatial variability, local atmospheric circulations, and rapidly evolving convective systems. This paper focuses on the optimization of the Ensemble Kalman Filter (EnKF) to enhance convection-permitting numerical weather prediction in complex terrain. A high-resolution ensemble forecasting framework is used to analyze the effects of key EnKF parameters, including ensemble size, covariance localization, and inflation. Controlled experiments are used to compare baseline and optimized configurations with respect to meteorological parameters, including temperature, wind, humidity, and precipitation. The analysis shows that ensemble size affects the stability of forecast-error covariance estimation, whereas covariance localization is required to control unrealistic spatial correlations caused by finite ensemble sizes. Ensemble inflation helps ensure sufficient ensemble dispersion to avoid overconfidence in the background forecast. The findings show that effective EnKF optimization must be based on a trade-off among uncertainty representation, observation impact, and computational efficiency, rather than maximizing a single parameter. The integrated system offers a more consistent approach to producing atmospheric initial conditions for convection-permitting forecasts. The paper identifies the potential of systematic EnKF optimization to enhance short-range forecasting in mountainous areas and provide more useful data for precipitation and weather-hazard applications.