Sensitivity analysis of planetary boundary layer and microphysics parameterization schemes in WRF model for Guwahati urban domain
Study region: Guwahati, Assam, India. Study focus: Increasing frequency of flash floods highlights the critical necessity for a reliable flood forecasting system to minimize potential human and property losses. Numerical Weather Prediction models plays crucial role in informed decision-making and ri...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
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| Series: | Journal of Hydrology: Regional Studies |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581825003313 |
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| Summary: | Study region: Guwahati, Assam, India. Study focus: Increasing frequency of flash floods highlights the critical necessity for a reliable flood forecasting system to minimize potential human and property losses. Numerical Weather Prediction models plays crucial role in informed decision-making and risk mitigation, especially the challenge of flood forecasting, which requires precise and high-resolution precipitation forecasts. This study delves into a detailed parameter sensitivity analysis to improve the accuracy of precipitation forecasts within a mesoscale urban climate. By conducting simulations using Weather Research and Forecasting model, coupled with Local Climate Zone map, research concentrates on refining mesoscale simulations tailored for urban climates. New hydrologic insights for the region: Model performance is thoroughly assessed by comparing the simulated precipitation data from WRF model with observed MSWEP data. This study explores application of a multi-criteria-based decision-making method, TOPSIS analysis. Based on TOPSIS analysis, among 10 combinations of Microphysics and Planetary Boundary Layer schemes, WSM6-MYJ, WSM6-BL, and WSM5-MYJ configurations consistently emerged as the most effective across all significant rainfall events. These combinations demonstrated strong correlation coefficients of 0.96, 0.76, and 0.94, respectively. Correspondingly, their RMSE (in mm) values were found to be 1.72, 1.5, and 2. The findings of this research offer valuable guidance for selecting parameterization schemes to enhance the precision of urban atmospheric simulations, consequently improving predictive capabilities and assisting in resilience development against extreme weather events. |
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| ISSN: | 2214-5818 |