Assessment of extremely severe cyclonic storms over Bay of Bengal and performance evaluation of ARW model in the prediction of track and intensity

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

doi:10.1007/s00704-020-03510-y

Cited by 16 publications

(12 citation statements)

References 64 publications

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“…A very recent study by Singh et al (2021) reported about 26% increment in the intensity of landfalling Extremely Severe Cyclonic Storms (ESCS) over the North Indian Ocean (NIO) region during the study period 1990-2020. It indicates that on an average the increment per decade is around 8%.…”

Section: Wave-current-surge Propagation Characteristics In a Changing...mentioning

confidence: 99%

Wave–current-surge interaction in a changing climate over a shallow continental shelf region

Sahoo

Bhaskaran

2021

Regional Studies in Marine Science

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Section: Wave-current-surge Propagation Characteristics In a Changing...mentioning

confidence: 99%

Wave–current-surge interaction in a changing climate over a shallow continental shelf region

Sahoo

Bhaskaran

2021

Regional Studies in Marine Science

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“…An extensive study conducted using the past 30 years of data suggests that the severity of extremely severe cyclonic storms (ESCSs) over NIO increased by 26 %. The observed statistics reveal that the duration of the ESCS stage and maximum wind speeds of ESCSs have shown an increasing trend, and the landfall category was very severe (Singh et al, 2021a). On considering climate change, Singh et al (2019) showed that the present warming climate impacts the formation and severity of the tropical cyclones over the BoB region.…”

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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“…speeds of ESCSs have shown an increasing trend, and the land falling category was very severe. Singh et al (2021a). On considering climate change, Singh et al (2019) showed that present warming climate impacts on the formation and severity of 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.

show abstract

Assessment of extremely severe cyclonic storms over Bay of Bengal and performance evaluation of ARW model in the prediction of track and intensity

Cited by 16 publications

References 64 publications

Wave–current-surge interaction in a changing climate over a shallow continental shelf region

Wave–current-surge interaction in a changing climate over a shallow continental shelf region

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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