Articles | Volume 15, issue 5
https://doi.org/10.5194/gmd-15-2133-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-2133-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
15 Mar 2022
Model evaluation paper |
| 15 Mar 2022
| 15 Mar 2022
Determining the sensitive parameters of the Weather Research and Forecasting (WRF) model for the simulation of tropical cyclones in the Bay of Bengal using global sensitivity analysis and machine learning
Harish Baki
Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600036, India
Sandeep Chinta
Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600036, India
C Balaji
CORRESPONDING AUTHOR
Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600036, India
Center of Excellence in Atmospheric and Climate Sciences, Indian Institute of Technology Madras, Chennai 600036, India
Balaji Srinivasan
Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600036, India
Robert Bosch Centre for Data Science and AI, Indian Institute of Technology Madras, Chennai,
600036, India
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Cited
14 citations as recorded by crossref.
- Comparative assessment of WRF’s parameterization scheme combinations in assessing land-surface feedback flux and its drivers: a case study of Phailin tropical cyclone S. Mandal et al. 10.1007/s00704-024-05032-3
- Role of assimilation of microwave humidity sounder (MHS) satellite radiance in forecast of structure and intensity of VSCS Vardah 2016 A. Thankachan & K. Satya Singh 10.1016/j.asr.2024.06.068
- Combinatorial Optimization of Physics Parameterization Schemes for Typhoon Simulation Based on a Simple Genetic Algorithm (SGA) Z. Lu et al. 10.1007/s13351-024-3105-2
- Parameter Calibration to Improve the Prediction of Tropical Cyclones over the Bay of Bengal Using Machine Learning–Based Multiobjective Optimization H. Baki et al. 10.1175/JAMC-D-21-0184.1
- Comparative evaluation of high-resolution rainfall products over South Peninsular India in characterising precipitation extremes M. Sneha & A. Nair 10.1007/s11069-023-05936-9
- The Regional Coupled Suite (RCS-IND1): application of a flexible regional coupled modelling framework to the Indian region at kilometre scale J. Castillo et al. 10.5194/gmd-15-4193-2022
- The Sensitivity of the Icosahedral Non-Hydrostatic Numerical Weather Prediction Model over Greece in Reference to Observations as a Basis towards Model Tuning E. Avgoustoglou et al. 10.3390/atmos14111616
- Estimation of possible extreme droughts for a dam catchment in Korea using a regional-scale weather model and long short-term memory network M. Shin & Y. Jung 10.2166/nh.2023.192
- Sensitivity of NEMO4.0-SI3 model parameters on sea ice budgets in the Southern Ocean Y. Nie et al. 10.5194/gmd-16-1395-2023
- Prediction and interpretation of pathogenic bacteria occurrence at a recreational beach using data-driven algorithms J. Jang et al. 10.1016/j.ecoinf.2023.102370
- Machine learning based parameter sensitivity of regional climate models—a case study of the WRF model for heat extremes over Southeast Australia P. Reddy et al. 10.1088/1748-9326/ad0eb0
- The Sensitivity of Extreme Rainfall Simulations to WRF Parameters During Two Intense Southwest Monsoon Events in the Philippines K. Henson et al. 10.1007/s13143-024-00380-6
- Determining the sensitive parameters of the Weather Research and Forecasting (WRF) model for the simulation of tropical cyclones in the Bay of Bengal using global sensitivity analysis and machine learning H. Baki et al. 10.5194/gmd-15-2133-2022
- Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review S. Salcedo-Sanz et al. 10.1007/s00704-023-04571-5
12 citations as recorded by crossref.
- Comparative assessment of WRF’s parameterization scheme combinations in assessing land-surface feedback flux and its drivers: a case study of Phailin tropical cyclone S. Mandal et al. 10.1007/s00704-024-05032-3
- Role of assimilation of microwave humidity sounder (MHS) satellite radiance in forecast of structure and intensity of VSCS Vardah 2016 A. Thankachan & K. Satya Singh 10.1016/j.asr.2024.06.068
- Combinatorial Optimization of Physics Parameterization Schemes for Typhoon Simulation Based on a Simple Genetic Algorithm (SGA) Z. Lu et al. 10.1007/s13351-024-3105-2
- Parameter Calibration to Improve the Prediction of Tropical Cyclones over the Bay of Bengal Using Machine Learning–Based Multiobjective Optimization H. Baki et al. 10.1175/JAMC-D-21-0184.1
- Comparative evaluation of high-resolution rainfall products over South Peninsular India in characterising precipitation extremes M. Sneha & A. Nair 10.1007/s11069-023-05936-9
- The Regional Coupled Suite (RCS-IND1): application of a flexible regional coupled modelling framework to the Indian region at kilometre scale J. Castillo et al. 10.5194/gmd-15-4193-2022
- The Sensitivity of the Icosahedral Non-Hydrostatic Numerical Weather Prediction Model over Greece in Reference to Observations as a Basis towards Model Tuning E. Avgoustoglou et al. 10.3390/atmos14111616
- Estimation of possible extreme droughts for a dam catchment in Korea using a regional-scale weather model and long short-term memory network M. Shin & Y. Jung 10.2166/nh.2023.192
- Sensitivity of NEMO4.0-SI3 model parameters on sea ice budgets in the Southern Ocean Y. Nie et al. 10.5194/gmd-16-1395-2023
- Prediction and interpretation of pathogenic bacteria occurrence at a recreational beach using data-driven algorithms J. Jang et al. 10.1016/j.ecoinf.2023.102370
- Machine learning based parameter sensitivity of regional climate models—a case study of the WRF model for heat extremes over Southeast Australia P. Reddy et al. 10.1088/1748-9326/ad0eb0
- The Sensitivity of Extreme Rainfall Simulations to WRF Parameters During Two Intense Southwest Monsoon Events in the Philippines K. Henson et al. 10.1007/s13143-024-00380-6
2 citations as recorded by crossref.
- Determining the sensitive parameters of the Weather Research and Forecasting (WRF) model for the simulation of tropical cyclones in the Bay of Bengal using global sensitivity analysis and machine learning H. Baki et al. 10.5194/gmd-15-2133-2022
- Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review S. Salcedo-Sanz et al. 10.1007/s00704-023-04571-5
Latest update: 23 Nov 2024
Short summary
WRF model accuracy relies on numerous aspects, and the model parameters are one of them. By calibrating the model parameters, we can improve the model forecast. However, there exist hundreds of parameters, and calibrating all of them is unimaginably expensive. Thus, there is a need to identify the sensitive parameters that influence the model output variables to reduce the parameter dimensionality. This study addresses the different methods and outcomes of parameter sensitivity analysis.
WRF model accuracy relies on numerous aspects, and the model parameters are one of them. By...
