Challenges in simulating prevailing fog types over urban region of Delhi

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.