Assessment of the WRF model configuration optimization in predicting the heavy rainfall over urban city Bhubaneswar, India
Abstract Bhubaneswar, Odisha, experiences an increasing trend of heavy rainfall events (HREs). This study aims to configure the WRF mesoscale model configuration at a hectometre scale and undertakes numerical experiments at a 0.5 km grid spacing. The experiments simulate HREs and assess the various...
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Springer
2025-05-01
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| Series: | Computational Urban Science |
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| Online Access: | https://doi.org/10.1007/s43762-025-00180-2 |
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| author | Alugula Boyaj N. R. Karrevula Madhusmita Swain P. Sinha Raghu Nadimpalli Sahidul Islam V. Vinoj Manoj Khare Dev Niyogi U. C. Mohanty |
| author_facet | Alugula Boyaj N. R. Karrevula Madhusmita Swain P. Sinha Raghu Nadimpalli Sahidul Islam V. Vinoj Manoj Khare Dev Niyogi U. C. Mohanty |
| author_sort | Alugula Boyaj |
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| description | Abstract Bhubaneswar, Odisha, experiences an increasing trend of heavy rainfall events (HREs). This study aims to configure the WRF mesoscale model configuration at a hectometre scale and undertakes numerical experiments at a 0.5 km grid spacing. The experiments simulate HREs and assess the various physical parameterization schemes to identify suitable combinations for the region. Sensitivity experiments with various physical parametrization options identified the top eight combinations based on rainfall statistics. Their performance was further evaluated by simulating an additional four HREs over Bhubaneswar. A novel rank analysis approach based on statistical techniques to determine the rank of each configuration. The Noah-MP; Ferrier; Multi-Scale Kain-Fritsch (MFS), Noah-MP;Ferrier; Kain-Fritsch (MFK), as well as Noah; Lin;No cumulus (NLN), and Noah; Ferrier; No cumulus (NFN) emerged as the top performers in simulating precipitation. The study also tested eight parameterization combinations for simulating air temperature, relative humidity, and wind speed. The top configurations change when a different variable is used as a reference. However, a broad choice of MFS, MFK, and Noah-MP; Ferrier; No cumulus (MFN) merged as the top configurations in simulating HRE characteristics. These model configurations were independently tested and yielded good performance in simulating the atmospheric pre-storm environment and storm characteristics. Broadly stated the choice of Noah-MP instead of the Noah land model, with Ferrier and Multi-Scale Kain-Fritsch schemes could yield good results- though there is no singular best potential. These findings help establish the computational framework for studying and improving the understanding of heavy rainfall, enhance weather hazard preparedness, and offer an optimized WRF model for forecasting HRE in cities. |
| format | Article |
| id | doaj-art-16846e10d9794e8392ea4dcd030641fe |
| institution | DOAJ |
| issn | 2730-6852 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Computational Urban Science |
| spelling | doaj-art-16846e10d9794e8392ea4dcd030641fe2025-08-20T03:09:34ZengSpringerComputational Urban Science2730-68522025-05-015112110.1007/s43762-025-00180-2Assessment of the WRF model configuration optimization in predicting the heavy rainfall over urban city Bhubaneswar, IndiaAlugula Boyaj0N. R. Karrevula1Madhusmita Swain2P. Sinha3Raghu Nadimpalli4Sahidul Islam5V. Vinoj6Manoj Khare7Dev Niyogi8U. C. Mohanty9School of Earth, Ocean, and Climate Sciences, Indian Institute of Technology BhubaneswarSchool of Earth, Ocean, and Climate Sciences, Indian Institute of Technology BhubaneswarSchool of Earth, Ocean, and Climate Sciences, Indian Institute of Technology BhubaneswarCentre for Development of Advanced ComputingMeteorological Department, Urban Meteorology and Climate Cell, India Centre for Development of Advanced ComputingSchool of Earth, Ocean, and Climate Sciences, Indian Institute of Technology BhubaneswarCentre for Development of Advanced ComputingDepartment of Earth and Planetary Sciences, Maseeh Department of Civil, Architectural, and Environmental Engineering, Jackson School of Geosciences, and, University of Texas at Austin Centre for Climate Smart Agriculture (CCSA), Siksha ‘O’ Anusandhan, Deemed to Be UniversityAbstract Bhubaneswar, Odisha, experiences an increasing trend of heavy rainfall events (HREs). This study aims to configure the WRF mesoscale model configuration at a hectometre scale and undertakes numerical experiments at a 0.5 km grid spacing. The experiments simulate HREs and assess the various physical parameterization schemes to identify suitable combinations for the region. Sensitivity experiments with various physical parametrization options identified the top eight combinations based on rainfall statistics. Their performance was further evaluated by simulating an additional four HREs over Bhubaneswar. A novel rank analysis approach based on statistical techniques to determine the rank of each configuration. The Noah-MP; Ferrier; Multi-Scale Kain-Fritsch (MFS), Noah-MP;Ferrier; Kain-Fritsch (MFK), as well as Noah; Lin;No cumulus (NLN), and Noah; Ferrier; No cumulus (NFN) emerged as the top performers in simulating precipitation. The study also tested eight parameterization combinations for simulating air temperature, relative humidity, and wind speed. The top configurations change when a different variable is used as a reference. However, a broad choice of MFS, MFK, and Noah-MP; Ferrier; No cumulus (MFN) merged as the top configurations in simulating HRE characteristics. These model configurations were independently tested and yielded good performance in simulating the atmospheric pre-storm environment and storm characteristics. Broadly stated the choice of Noah-MP instead of the Noah land model, with Ferrier and Multi-Scale Kain-Fritsch schemes could yield good results- though there is no singular best potential. These findings help establish the computational framework for studying and improving the understanding of heavy rainfall, enhance weather hazard preparedness, and offer an optimized WRF model for forecasting HRE in cities.https://doi.org/10.1007/s43762-025-00180-2Heavy rainfall eventsWRF modelUrban RainfallWeather Forecasting |
| spellingShingle | Alugula Boyaj N. R. Karrevula Madhusmita Swain P. Sinha Raghu Nadimpalli Sahidul Islam V. Vinoj Manoj Khare Dev Niyogi U. C. Mohanty Assessment of the WRF model configuration optimization in predicting the heavy rainfall over urban city Bhubaneswar, India Computational Urban Science Heavy rainfall events WRF model Urban Rainfall Weather Forecasting |
| title | Assessment of the WRF model configuration optimization in predicting the heavy rainfall over urban city Bhubaneswar, India |
| title_full | Assessment of the WRF model configuration optimization in predicting the heavy rainfall over urban city Bhubaneswar, India |
| title_fullStr | Assessment of the WRF model configuration optimization in predicting the heavy rainfall over urban city Bhubaneswar, India |
| title_full_unstemmed | Assessment of the WRF model configuration optimization in predicting the heavy rainfall over urban city Bhubaneswar, India |
| title_short | Assessment of the WRF model configuration optimization in predicting the heavy rainfall over urban city Bhubaneswar, India |
| title_sort | assessment of the wrf model configuration optimization in predicting the heavy rainfall over urban city bhubaneswar india |
| topic | Heavy rainfall events WRF model Urban Rainfall Weather Forecasting |
| url | https://doi.org/10.1007/s43762-025-00180-2 |
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