Abstract
Despite an increased understanding of the physical processes involved, forecasting radiative cold pools and their associated meteorological phenomena (e.g., fog and freezing rain) remains a challenging problem in mesoscale models. The present study is focused on California’s tule fog where the Weather Research and Forecasting (WRF) Model’s frequent inability to forecast these events is addressed and substantially improved. Specifically, this was accomplished with four major changes from a commonly employed, default configuration. First, horizontal model diffusion and numerical filtering along terrain slopes was deactivated (or mitigated) since it is unphysical and can completely prevent the development of fog. However, this often resulted in unrealistically persistent foggy boundary layers that failed to lift. Next, changes specific to the Yonsei University (YSU) planetary boundary layer (PBL) scheme were adopted that include using the ice–liquid-water potential temperature 
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