IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v74y2014i3p1743-1757.html
   My bibliography  Save this article

Remote sensing and ground-based observations for nowcasting the category of thunderstorms based on peak wind speed over an urban station of India

Author

Listed:
  • Debanjana Das
  • Sutapa Chaudhuri

Abstract

Thunderstorms are the recurrent features of India and are responsible for the redistribution of excess heat and moisture in the atmosphere. However, the thunderstorms that occur over the urban station Kolkata (22°34′N, 88°22′E), India, during the pre-monsoon months of April and May are extremely devastating while accompanied with high wind speed, lightning flashes, torrential rain and occasional hail and tornadoes. The development and verification of a model output are described in this study. The system consists of multiple linear regression (MLR) equations, and the purpose is to nowcast the categories of thunderstorms over Kolkata, both ordinary (wind speed >65 km h −1 ) and severe (wind speed ≥65 km h −1 ) as per the warning provided by the India Meteorological Department for the prevalence of thunderstorms. The MODIS terra/aqua satellite data of cloud parameters, ground-based Radiosonde/Rawinsonde upper air observations and records of wind speed accompanied with thunderstorms over Kolkata are considered for the study. The MLR models are formulated with the cloud parameters as input and the target output being the peak wind speed associated with the pre-monsoon thunderstorms. The MLR model is trained with the data and records from 2002 to 2009, and the results are validated with the observations of 2010 and 2011. The results reveal that the accuracy in nowcasting the ordinary and severe categories of thunderstorms during the pre-monsoon season over Kolkata with MLR models are 94.26 and 91.29 %, respectively, with lead time >12 h. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Debanjana Das & Sutapa Chaudhuri, 2014. "Remote sensing and ground-based observations for nowcasting the category of thunderstorms based on peak wind speed over an urban station of India," 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. 74(3), pages 1743-1757, December.
    • Handle: RePEc:spr:nathaz:v:74:y:2014:i:3:p:1743-1757
    DOI: 10.1007/s11069-014-1272-7
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11069-014-1272-7
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11069-014-1272-7?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. Sutapa Chaudhuri & Sayantika Goswami & Anirban Middey, 2014. "Medium-range forecast of cyclogenesis over North Indian Ocean with multilayer perceptron model using satellite data," 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. 70(1), pages 173-193, January.
    2. Sutapa Chaudhuri, 2006. "Predictability Of Chaos Inherent In The Occurrence Of Severe Thunderstorms," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 9(01n02), pages 77-85.
    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. Surajit Chattopadhyay & Goutami Chattopadhyay & Subrata Kumar Midya, 2018. "Shannon entropy maximization supplemented by neurocomputing to study the consequences of a severe weather phenomenon on some surface parameters," 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. 93(1), pages 237-247, August.
    2. Baranowski, Piotr & Gos, Magdalena & Krzyszczak, Jaromir & Siwek, Krzysztof & Kieliszek, Adam & Tkaczyk, Przemysław, 2019. "Multifractality of meteorological time series for Poland on the base of MERRA-2 data," Chaos, Solitons & Fractals, Elsevier, vol. 127(C), pages 318-333.

    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:74:y:2014:i:3:p:1743-1757. 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.