Sensitivity Analysis of Parameterization Schemes in the WRF Model for Predicting Urban Precipitation Anomalies Over Chennai

Extreme rainfall events often lead to significantly heavier rainfall over urban areas compared to their surrounding regions. Predicting these positive urban precipitation anomalies during heavy rainfall remains a critical challenge in numerical weather modeling. This study explores the sensitivity of the Weather Research and Forecasting (WRF) model to approximately 70 combinations of parameterization schemes, including microphysics, cumulus convection, planetary boundary layer options, and urban canopy model schemes, focusing on urban precipitation anomalies.

The analysis is based on two significant heavy rainfall events over Chennai, India: October 22, 2006, and November 8, 2015. Model simulations are validated against Integrated Multi-satellite Retrievals for GPM (IMERG) precipitation data to evaluate their ability to capture urban anomalies. High-resolution simulations demonstrate that specific combinations of parameterization schemes, particularly those incorporating multi-level urban canopy models, enhance the model’s capacity to predict significant positive anomalies during intense rainfall events.

The findings underscore the critical role of urban canopy models in shaping precipitation intensity and spatial distribution and the interplay between cumulus convection and boundary layer processes in driving urban precipitation dynamics. These insights provide practical guidance for optimizing WRF parameterization settings, advancing the accuracy of urban-scale weather prediction, and deepening the understanding of urban hydrometeorological processes.