Numerical simulation of an extremely severe cyclonic storm over the Bay of Bengal using WRF modelling system: influence of model initial condition

Singh, Kuvar Satya; Albert, Jiya; Bhaskaran, Prasad K.; Alam, Parvez

doi:10.1007/s40808-020-01069-1

Cited by 14 publications

(9 citation statements)

References 38 publications

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“…The accuracy of the WRF model depends on (i) the specification of initial and lateral boundary conditions, (ii) the representation of model physics schemes, and (iii) the specification of parameters. With the availability of vast computational resources and observations, the accuracy in the specification of initial and lateral boundary conditions is improved to a great extent (Mohanty et al, 2010;Singh et al, 2021b). Many researchers have studied the sensitivity of physics schemes in simulating tropical cyclones over the BoB and invariably reported the performance of different combinations of physics schemes by comparing the tracks and intensities of cyclones (Pattanayak et al, 2012;Osuri et al, 2012;Rambabu et al, 2013;Kanase and Salvekar, 2015;Chandrasekar and Balaji, 2016;Sandeep et al, 2018;Venkata Rao et al, 2020;Mahala et al, 2021;Singh et al, 2021b;Messmer et al, 2021;Baki et al, 2021a).…”

Section: Introductionmentioning

confidence: 99%

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

2022

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Abstract. The present study focuses on identifying the parameters from the Weather Research and Forecasting (WRF) model that strongly influence the simulation of tropical cyclones over the Bay of Bengal (BoB) region. Three global sensitivity analysis (SA) methods, namely, the Morris One-at-A-Time (MOAT), multivariate adaptive regression splines (MARS), and surrogate-based Sobol', are employed to identify the most sensitive parameters out of 24 tunable parameters corresponding to seven parameterization schemes of the WRF model. Ten tropical cyclones across different categories, such as cyclonic storms, severe cyclonic storms, and very severe cyclonic storms over BoB between 2011 and 2018, are selected in this study. The sensitivity scores of 24 parameters are evaluated for eight meteorological variables. The parameter sensitivity results are consistent across three SA methods for all the variables, and 8 out of the 24 parameters contribute 80 %–90 % to the overall sensitivity scores. It is found that the Sobol' method with Gaussian progress regression as a surrogate model can produce reliable sensitivity results when the available samples exceed 200. The parameters with which the model simulations have the least RMSE values when compared with the observations are considered the optimal parameters. Comparing observations and model simulations with the default and optimal parameters shows that simulations with the optimal set of parameters yield a 16.74 % improvement in the 10 m wind speed, 3.13 % in surface air temperature, 0.73 % in surface air pressure, and 9.18 % in precipitation simulations compared to the default set of parameters.

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Section: Introductionmentioning

confidence: 99%

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

2022

View full text Add to dashboard Cite

show abstract

“…The accuracy of the WRF model depends on (i) the specification of initial and lateral boundary conditions, (i) the representation of model physics schemes, and (iii) the specification of parameters. With the availability of vast computational resources and observations, the accuracy in the specification of initial and lateral boundary conditions is improved to a great extent (Mohanty et al, 2010;Singh et al, 2021b).…”

Section: Introductionmentioning

confidence: 99%

“…Many researchers have studied the sensitivity of physics schemes in simulating tropical cyclones over the BoB and invariably reported the performance of different combinations of physics schemes by comparing the tracks and intensities of cyclones (Pattanayak et al, 2012;Osuri et al, 2012;Rambabu et al, 2013;Kanase and Salvekar, 2015;Chandrasekar and Balaji, 2016;Sandeep et al, 2018;Venkata Rao et al, 2020;Mahala et al, 2021;Singh et al, 2021b;Messmer et al, 2021). However, systematic studies on parameter sensitivity, to determine their optimal values is yet to be explored for tropical cyclones over the BoB region.…”

Section: Introductionmentioning

confidence: 99%

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

Baki

Chinta

Balaji

et al. 2021

Preprint

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Abstract. The present study focuses on identifying the parameters from the Weather Research and Forecasting (WRF) model that strongly influence the prediction of tropical cyclones over the Bay of Bengal (BoB) region. Three global sensitivity analysis (SA) methods, namely the Morris One-at-A-Time (MOAT), Multivariate Adaptive Regression Splines (MARS), and surrogate-based Sobol' are employed to identify the most sensitive parameters out of 24 tunable parameters corresponding to seven parameterization schemes of the WRF model. Ten tropical cyclones across different categories, such as cyclonic storms, severe cyclonic storms, and very severe cyclonic storms over BoB between 2011 and 2018, are selected in this study. The sensitivity scores of 24 parameters are evaluated for eight meteorological variables. The parameter sensitivity results are consistent across three SA methods for all the variables, and 8 out of the 24 parameters contribute 80 %–90 % to the overall sensitivity scores. It is found that the Sobol' method with Gaussian progress regression as a surrogate model can produce reliable sensitivity results when the available samples exceed 200. The parameters with which the model simulations have the least RMSE values when compared with the observations are considered as the optimal parameters. Comparing observations and model simulations with the default and optimal parameters shows that predictions with the optimal set of parameters yield a 16.74 % improvement in the 10 m wind speed, 3.13 % in surface air temperature, 0.73 % in surface air pressure, and 9.18 % in precipitation predictions compared to the default set of parameters.

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Section: Introductionmentioning

confidence: 99%

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Balaji

2021

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Numerical simulation of an extremely severe cyclonic storm over the Bay of Bengal using WRF modelling system: influence of model initial condition

Cited by 14 publications

References 38 publications

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

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

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

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