Preprints
https://doi.org/10.5194/wcd-2021-46
https://doi.org/10.5194/wcd-2021-46
26 Jul 2021
 | 26 Jul 2021
Status: this preprint has been withdrawn by the authors.

Simulations of Bay of Bengal tropical cyclones in a regional convection-permitting atmosphere–ocean coupled model

Jennifer Saxby, Julia Crook, Simon Peatman, Cathryn Birch, Juliane Schwendike, Maria Valdivieso da Costa, Juan Manuel Castillo Sanchez, Chris Holloway, Nicholas P. Klingaman, Ashis Mitra, and Huw Lewis

Abstract. Tropical cyclones (TCs) in the Bay of Bengal can be extremely destructive when they make landfall in India and Bangladesh. Accurate prediction of their track and intensity is essential for disaster management. This study evaluates simulations of Bay of Bengal TCs using a regional convection-permitting atmosphere-ocean coupled model. The Met Office Unified Model atmosphere-only configuration (4.4 km horizontal grid spacing) is compared with a configuration coupled to a three-dimensional dynamical ocean model (2.2 km horizontal grid spacing). Simulations of six TCs from 2016–2019 show that both configurations produce accurate TC tracks for lead times of up to 6 days before landfall. Both configurations underestimate high wind speeds and high rain rates, and overestimate low wind speeds and low rain rates. The ocean-coupled configuration improves landfall timing predictions and reduces wind speed biases relative to observations outside the eyewall but underestimates maximum wind speeds in the eyewall for the most intense TCs. The coupled configuration produces weaker TCs than the atmosphere-only configuration, consistent with lower sea surface temperatures in the coupled model and an overestimated cooling response in TC wakes. Both model configurations accurately predict rain rate asymmetry, suggesting a good representation of TC dynamics. Much of the rain rate asymmetry variation in the simulations is related to wind shear variations, with a preference for higher rain rates in the down-shear left quadrant.

This preprint has been withdrawn.

Jennifer Saxby, Julia Crook, Simon Peatman, Cathryn Birch, Juliane Schwendike, Maria Valdivieso da Costa, Juan Manuel Castillo Sanchez, Chris Holloway, Nicholas P. Klingaman, Ashis Mitra, and Huw Lewis

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wcd-2021-46', Anonymous Referee #1, 18 Aug 2021
    • AC1: 'Comment on wcd-2021-46', Julia Crook, 29 Oct 2021
  • RC2: 'Comment on wcd-2021-46', Anonymous Referee #2, 04 Oct 2021
    • AC1: 'Comment on wcd-2021-46', Julia Crook, 29 Oct 2021
  • AC1: 'Comment on wcd-2021-46', Julia Crook, 29 Oct 2021

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wcd-2021-46', Anonymous Referee #1, 18 Aug 2021
    • AC1: 'Comment on wcd-2021-46', Julia Crook, 29 Oct 2021
  • RC2: 'Comment on wcd-2021-46', Anonymous Referee #2, 04 Oct 2021
    • AC1: 'Comment on wcd-2021-46', Julia Crook, 29 Oct 2021
  • AC1: 'Comment on wcd-2021-46', Julia Crook, 29 Oct 2021
Jennifer Saxby, Julia Crook, Simon Peatman, Cathryn Birch, Juliane Schwendike, Maria Valdivieso da Costa, Juan Manuel Castillo Sanchez, Chris Holloway, Nicholas P. Klingaman, Ashis Mitra, and Huw Lewis
Jennifer Saxby, Julia Crook, Simon Peatman, Cathryn Birch, Juliane Schwendike, Maria Valdivieso da Costa, Juan Manuel Castillo Sanchez, Chris Holloway, Nicholas P. Klingaman, Ashis Mitra, and Huw Lewis

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Latest update: 01 Mar 2024
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This preprint has been withdrawn.

Short summary
This study assesses the ability of the new Met Office IND1 numerical model to simulate tropical cyclones and their associated hazards, such as high winds and heavy rainfall. The new system consists of both atmospheric and oceanic models coupled together, allowing us to explore the sensitivity of cyclones to important air–sea feedbacks. We find that the model can accurately simulate tropical cyclone position, structure, and intensity, which are crucial for predicting and mitigating hazards.