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Abstract Accurately predicting fog is challenging due to interplay of myriad processes in its formation and high spatiotemporal variability. This study compares the performance of the Weather Research and Forecasting model with control (CNTL‐WRF) and assimilated fine‐grid (HRLDAS‐WRF) soil fields in the Ingo‐Gangetic Plain (IGP) over a 2‐years winter period (2019–2020 and 2020–2021). Results show HRLDAS‐WRF enhances accuracy in representing surface fog's heterogeneity and lifecycle across the IGP, demonstrating a spatial skill improvement of approximately 18% with a Fraction Skill Score of 0.44, compared to CNTL‐WRF's (0.36). Employing fog classification algorithm identifies 25 dense fog episodes (Vis < 500 m) over Delhi's urban boundary layer, including 14 radiation (RAD), 5 cloud‐base lowering (CBL), 3 advection + radiation (ADV + RAD), and 3 evaporation (EVA) episodes. CNTL‐WRF predicts 20 episodes but misses five due to a dry bias in the initial moisture conditions. However, HRLDAS‐WRF demonstrates limited vertical fog growth in various occurrences, highlighting the crucial role of fine‐gridded soil states for enhanced land‐surface feedback. Detailed analysis shows a 40% reduction in mean onset error for RAD fog occurrences in HRLDAS‐WRF when compared to CNTL‐WRF. In CBL fog episodes, both models exhibit significant radiative cooling and inversion before fog onset, leading to inaccurate predictions as RAD fog. Similarly, forecasting the abrupt development of ADV + RAD fog episodes is challenging as models struggle to replicate moisture intrusion over radiatively cooled surfaces in windy conditions. Predicting EVA fog, forms within an hour after sunrise, remains difficult due to the current model parameterization that rapidly dissipates fog soon after sunrise.
Parde et al. (Tue,) studied this question.