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

Debanjana Das and Sutapa Chaudhuri ()

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2014, vol. 74, issue 3, 1743-1757

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

Keywords: Cloud parameters; MLR; Thunderstorm; Peak wind speed (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s11069-014-1272-7

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