Articles | Volume 15, issue 5
https://doi.org/10.5194/gmd-15-2133-2022
https://doi.org/10.5194/gmd-15-2133-2022
Model evaluation paper
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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

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-242', Anonymous Referee #1, 25 Oct 2021
  • RC2: 'Comment on gmd-2021-242', Anonymous Referee #2, 02 Dec 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Chakravarthy Balaji on behalf of the Authors (08 Dec 2021)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (21 Dec 2021) by Paul Ullrich
RR by Anonymous Referee #1 (03 Jan 2022)
ED: Publish as is (07 Feb 2022) by Paul Ullrich
AR by Chakravarthy Balaji on behalf of the Authors (07 Feb 2022)  Manuscript 
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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.