[A-AS03] Extreme Events and Mesoscale Weather: Observations and Modeling

[AAS03-19] Numerical Weather Prediction Model Underestimated the Extent of Extreme Rainfall Event during the
2024 Wayanad (Kerala, India) Landslide

*PIYUSH SRIVASTAVA¹, Shiwam Singh¹, Saurabh Singh¹, Anandu Prabhakaran¹, Srikrishnan Siva Subramanian¹, Yunus Ali Pulpadan² (1.Indian Institute of Technology Roorkee, Roorkee, India, 2.Indian Institute of Science Education and Research Mohali, India)

Keywords:Extreme weather, Early Warning System, Landslide, Rainfall, Weather Forecasting

This study evaluates the performance of the Weather Research and Forecasting (WRF) model in simulating the extreme rainfall which triggered the 2024 Wayanad (Kerala) landslide on 30 July 2024, in the Western Ghats of India. Two experiments using different initial condition datasets are conducted, and model performance is assessed against observational data from 84 Automatic Weather Stations (AWS) across Kerala from July 29 to 31, 2024. The model is found to capture the spatiotemporal patterns of key meteorological variables, including temperature, rainfall and geopotential height. The model found a pre-event low-pressure system and an anticyclonic circulation over Wayanad during the event, indicating conditions conducive to heavy rainfall. However, statistical analysis using AWS derived rainfall revealed a tendency to underpredict extreme rainfall by the WRF model. The variability in model performance emphasizes the need for improved understanding and representation of micro-scale processes over complex terrains like the Western Ghats to enhance India’s Landslide Early Warning System.