Parameterization of Surface Layer Processes in Extreme Weather Conditions over Mountainous Region

A major uncertainty in parameterization of surface–atmosphere turbulent processes in the weather and climate models is due to various empirical functional forms which are utilized for incorporating the near-surface atmospheric stability in convective conditions. Based on the study of Srivastava et al., Namdev et al. have carried out an extensive analysis and argued that functions suggested by Kader and Yaglom outperform when utilized in the Weather Research and Forecasting Model, version 4.2.2 (WRFv4.2.2), for simulating the fair weather condition over Indian land surface. In this study, an attempt has been made to evaluate the suitability of the updated surface layer module in the WRFv4.2.2 Model over the Indian Himalayan region. For this purpose, two extreme weather conditions, the Uttarakhand floods of (i) June 2013 and (ii) July 2023, and one fair weather condition of November 2024 are analyzed. The analysis is based on very high-resolution (1 km) WRF Model simulations, reanalysis data, observational rainfall data, and high-frequency turbulent data from the micrometeorological tower installed in Ranichauri (30.309°N, 78.408°E), Uttarakhand. The results indicate that the performance of different similarity functions varied across locations, atmospheric conditions, and variables. The similarity functions suggested by Kader and Yaglom outperformed others for sensible heat and momentum fluxes under fair weather conditions over Ranichauri, Uttarakhand. Fairall et al. functions have shown good spatial agreement for sensible heat flux (SHF), latent heat flux (LHF), U10, and T2 in terms of statistical measures, despite some overestimation tendencies. The study recommends steps further to improve the surface–atmosphere turbulent processes over complex environments.