Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 5
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.
https://doi.org/10.5194/gmd-15-2133-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 5
Model evaluation paper
 | 
15 Mar 2022
Model evaluation paper |  | 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, Sandeep Chinta, C Balaji, and Balaji Srinivasan

Data sets

Data for publication of "Determining the sensitive parameters of WRF model for the prediction of tropical cyclones in the Bay of Bengal using Global Sensitivity Analysis and Machine Learning" Harish Baki, Sandeep Chinta, Balaji, and Balaji Srinivasan https://doi.org/10.5281/zenodo.5105285

ERA5 hourly data on pressure levels from 1979 to present H. Hersbach, B. Bell, P. Berrisford, G. Biavati, A. Horányi, J. Muñoz Sabater, J. Nicolas, C. Peubey, R. Radu, I. Rozum, D. Schepers, A. Simmons, C. Soci, D. Dee, and J.-N. Thépaut https://doi.org/10.24381/cds.bd0915c6

ERA5 hourly data on single levels from 1979 to present H. Hersbach, B. Bell, P. Berrisford, G. Biavati, A. Horányi, J. Muñoz Sabater, J. Nicolas, C. Peubey, R. Radu, I. Rozum, D. Schepers, A. Simmons, C. Soci, D. Dee, and J.-N. Thépaut https://doi.org/10.24381/cds.adbb2d47

GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V06 G. J. Huffman, , E. F. Stocker, D. T. Bolvin, E. J. Nelkin, and Jackson Tan https://doi.org/10.5067/GPM/IMERGDF/DAY/06

Model code and software

WRF Model Users' Page WRF Users Page https://doi.org/10.5065/D6MK6B4K

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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.
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