IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v121y2025i4d10.1007_s11069-024-06977-4.html
   My bibliography  Save this article

Sensitivity of radar data on landfall processes of tropical cyclones in the Bay of Bengal

Author

Listed:
  • Sankhasubhra Chakraborty

    (Indian Institute of Technology Bhubaneswar)

  • Sandeep Pattnaik

    (Indian Institute of Technology Bhubaneswar)

  • B. A. M. Kannan

    (Regional Meteorological Centre)

Abstract

The eastern coastal parts of India, specifically West Bengal, Odisha, Andhra Pradesh, and Tamil Nadu, are always susceptible to intense tropical cyclone (TC) landfalls formed over the Bay of Bengal. It is extremely challenging to accurately forecast the TC structure, intensity, and rainfall, particularly during landfall hours, which have a direct and immense impact on disaster preparedness and mitigation efforts. During landfall the eyewall associated convective processes are complex and puts a constraint in the accurate TC rainfall estimation for the numerical models. This work aims to fill that gap simulating three landfalling TCs, Hudhud (2014), Vardah (2016), and Titli (2018), with a lead time of up to 72 h, by assimilating Doppler Weather Radar (DWR) reflectivity (Rf) observations. Two sets of experiments are performed, i.e., CNTL (without assimilation) and RDA (with assimilation). Results demonstrate a significant improvement in TC intensity (28.9%, 21.4% in minimum central pressure, and 29.6%, 17% in maximum sustained surface wind) for Hudhud and Vardah, respectively, compared to CNTL. However, the improvement on Titli is minimal. Additionally, the RDA has shown an improved rainfall skill score for all cases, particularly over land, i.e., Hudhud (15.5%) and Vardah (53.68%). The hydrometeor distribution near the eyewall reveals a realistic representation in RDA, thereby contributing to improvements in intensity and rainfall forecast. In general, during landfall hours, the radial and tangential wind structures in RDA reveal more organised eyewall convection and are coherent with observations. Furthermore, the horizontal transport term is the dominant contributor to angular momentum influx into the core region and regulating the intensity. Simultaneously, the vertical eddy momentum loss has been reduced in RDA, facilitating the maintenance of the eyewall structure, especially in the case of Hudhud and Titli.

Suggested Citation

  • Sankhasubhra Chakraborty & Sandeep Pattnaik & B. A. M. Kannan, 2025. "Sensitivity of radar data on landfall processes of tropical cyclones in the Bay of Bengal," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(4), pages 4531-4557, March.
    • Handle: RePEc:spr:nathaz:v:121:y:2025:i:4:d:10.1007_s11069-024-06977-4
    DOI: 10.1007/s11069-024-06977-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-024-06977-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-024-06977-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Atul Kumar Varma & Neeru Jaiswal & Ayan Das & Mukesh Kumar & Nikhil V. Lele & Rojalin Tripathy & Saroj Maity & Mehul Pandya & Bimal Bhattacharya & Anup Kumar Mandal & M. Jishad & M. Seemanth & Arvind , 2023. "A pathway for multi-stage cyclone-induced hazard tracking—case study for Yaas," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 1035-1067, May.
    2. Peter Bauer & Alan Thorpe & Gilbert Brunet, 2015. "The quiet revolution of numerical weather prediction," Nature, Nature, vol. 525(7567), pages 47-55, September.
    3. Kuldeep Srivastava & Rashmi Bhardwaj & S. Roy Bhowmik, 2011. "Assimilation of Indian Doppler Weather Radar observations for simulation of mesoscale features of a land-falling cyclone," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 59(3), pages 1339-1355, December.
    4. Neerja Sharma & Atul Kumar Varma, 2022. "Impact of vertical wind shear in modulating tropical cyclones eye and rainfall structure," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(3), pages 2083-2100, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jinhua Wen & Yian Hua & Chenkai Cai & Shiwu Wang & Helong Wang & Xinyan Zhou & Jian Huang & Jianqun Wang, 2023. "Probabilistic Forecast and Risk Assessment of Flash Droughts Based on Numeric Weather Forecast: A Case Study in Zhejiang, China," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    2. Mayer, Martin János & Yang, Dazhi, 2023. "Calibration of deterministic NWP forecasts and its impact on verification," International Journal of Forecasting, Elsevier, vol. 39(2), pages 981-991.
    3. Bressler, R. Daniel & Papp, Anna & Sarmiento, Luis & Shrader, Jeffrey G. & Wilson, Andrew J., 2025. "Working Under the Sun: The Role of Occupation in Temperature-Related Mortality in Mexico," IZA Discussion Papers 17759, Institute of Labor Economics (IZA).
    4. Anand, Vaibhav, 2022. "The Value of Forecast Improvements: Evidence from Advisory Lead Times and Vehicle Crashes," MPRA Paper 114491, University Library of Munich, Germany.
    5. Husain Najafi & Pallav Kumar Shrestha & Oldrich Rakovec & Heiko Apel & Sergiy Vorogushyn & Rohini Kumar & Stephan Thober & Bruno Merz & Luis Samaniego, 2024. "High-resolution impact-based early warning system for riverine flooding," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    6. Meng, Xiaochun & Taylor, James W., 2022. "Comparing probabilistic forecasts of the daily minimum and maximum temperature," International Journal of Forecasting, Elsevier, vol. 38(1), pages 267-281.
    7. Tang, Wenliang & Yang, Mian & Duan, Hongbo, 2023. "Temperature and corporate tax avoidance: Evidence from Chinese manufacturing firms," Energy Economics, Elsevier, vol. 117(C).
    8. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    9. Zack Guido & Sara Lopus & Kurt Waldman & Corrie Hannah & Andrew Zimmer & Natasha Krell & Chris Knudson & Lyndon Estes & Kelly Caylor & Tom Evans, 2021. "Perceived links between climate change and weather forecast accuracy: new barriers to tools for agricultural decision-making," Climatic Change, Springer, vol. 168(1), pages 1-20, September.
    10. Sergei Soldatenko & Rafael Yusupov, 2021. "An Optimal Control Perspective on Weather and Climate Modification," Mathematics, MDPI, vol. 9(4), pages 1-15, February.
    11. Yakoub, Ghali & Mathew, Sathyajith & Leal, Joao, 2023. "Intelligent estimation of wind farm performance with direct and indirect ‘point’ forecasting approaches integrating several NWP models," Energy, Elsevier, vol. 263(PD).
    12. Patrick Schmidt & Matthias Katzfuss & Tilmann Gneiting, 2021. "Interpretation of point forecasts with unknown directive," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 728-743, September.
    13. Liu, Chenyu & Zhang, Xuemin & Mei, Shengwei & Zhen, Zhao & Jia, Mengshuo & Li, Zheng & Tang, Haiyan, 2022. "Numerical weather prediction enhanced wind power forecasting: Rank ensemble and probabilistic fluctuation awareness," Applied Energy, Elsevier, vol. 313(C).
    14. Yang, Dazhi & Kleissl, Jan, 2023. "Summarizing ensemble NWP forecasts for grid operators: Consistency, elicitability, and economic value," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1640-1654.
    15. Alexander Henzi & Johanna F. Ziegel & Tilmann Gneiting, 2021. "Isotonic distributional regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 963-993, November.
    16. Boris V. Malozyomov & Nikita V. Martyushev & Svetlana N. Sorokova & Egor A. Efremenkov & Denis V. Valuev & Mengxu Qi, 2024. "Analysis of a Predictive Mathematical Model of Weather Changes Based on Neural Networks," Mathematics, MDPI, vol. 12(3), pages 1-17, February.
    17. Daniëlle van Beekvelt & Irene Garcia-Marti & Jouke de Baar, 2024. "Towards high-resolution gridded climatology stemming from the combination of official and crowdsourced weather observations using multi-fidelity methods," PLOS Climate, Public Library of Science, vol. 3(1), pages 1-21, January.
    18. M. K. Islam & N. M. S. Hassan & M. G. Rasul & Kianoush Emami & Ashfaque Ahmed Chowdhury, 2023. "Forecasting of Solar and Wind Resources for Power Generation," Energies, MDPI, vol. 16(17), pages 1-23, August.
    19. Cailin Li & Na Sun & Yihui Lu & Baoyun Guo & Yue Wang & Xiaokai Sun & Yukai Yao, 2022. "Review on Urban Flood Risk Assessment," Sustainability, MDPI, vol. 15(1), pages 1-24, December.
    20. Eva D. Regnier & Joel W. Feldmeier, 2022. "D Minus Months: Strategic Planning for Weather-Sensitive Decisions," Decision Analysis, INFORMS, vol. 19(1), pages 1-20, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:121:y:2025:i:4:d:10.1007_s11069-024-06977-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.