Sayantika Goswami

Appraisal of the prevalence of severe tropical storms over Indian Ocean by screening the features of tropical depressions

Tropical cyclones are one of the nature’s most violent manifestations and potentially the deadliest of all meteorological phenomena. It is a unique combination of violent wind, heavy rainfall, and mountainous waves in sea. The maximum sustained wind speed, minimum sea level pressure, and the radius of maximum winds are important parameters for understanding a particular tropical cyclone and to differentiate it from a depression to tropical storms. The objective of this particular paper is to identify a possible range of maximum sustained wind speed, minimum sea level pressure, and radius of maximum winds which facilitates tropical depressions to lead to tropical storms over Bay of Bengal and Arabian Sea of Indian Ocean basin. The method of rough set theory which is based on condition—decision support system is implemented for the purpose. The result reveals that the threshold ranges of the maximum sustained wind speed, minimum sea level pressure and radius of maximum winds associated with tropical depression are possible that can aid in the predictability of tropical storm over Indian Ocean. The results are validated with significant tropical storms of 2009 and 2010 observations through Doppler and satellite imageries.

Nowcasting Bordoichila with a composite stability index

In operational forecast, the stability indices either individually or in combination are utilized to assess the predictability of local severe storms over a region. The objective of the present study is to identify such stability indices to assess the predictability of Bordoichila of Guwahati, India, during the pre-monsoon season (April–May) aiming to formulate a composite stability index using the most pertinent indices for nowcasting Bordoichila with considerable precision. Bordoichila, meaning the angry daughter of Assam, represents local severe storms of Guwahati during the pre-monsoon season. Precise forecast of Bordoichila is essential to mitigate the associated catastrophe over Guwahati. The forecast quality detection parameters are computed with the available indices during the period from 1997 to 2006 to select the most relevant stability indices for the prevalence of Bordoichila. The method of normal probability distribution is implemented to identify the threshold ranges of the selected indices. The stability indices that are selected with appropriate ranges are lifted index, Showalter index (SI), cross total index (CTI), vertical total index, totals total, convective available potential energy, convective inhibition energy, SWEAT and bulk Richardson number. The forecast skill scores are estimated with the selected indices. The best predictor indices identified for the prevalence of Bordoichila are the cross total index (CTI) and Showalter index (SI). A composite stability index, Bordoichila prediction index, is formulated with CTI and SI with proper weightages. The forecast with BPI is validated with the observations of India Meteorological Department for the year 2007 and is implemented for real-time forecast for the years 2009 and 2011.

The Coupled Influence of Instability Indices and DWR Data in Estimating the Squall Speed of Thunderstorms

The specific forecast of occurrences and the associated consequences of thunderstorms is still a difficult task for both NWP models and professional weather forecasters due to the small spatial and temporal scales involved. In operational forecast, many indices are being used to assess the stability of the atmosphere and predict the possibility of thunderstorm development. It is also well established that the Doppler weather radar (DWR) has the capability of capturing the fast developing convective systems such as thunderstorms. The instability indices as well as the DWR data are utilized in the present study to estimate the speed of squall associated with thunderstorms during the pre monsoon season over Kolkata (22° 32′N, 88° 20′E), India. The ranges of the selected indices and the DWR data are estimated using the normal probability distribution function. The statistical skill score analysis is implemented to select the instability indices relevant for estimating the squall speed of thunderstorms over Kolkata. The threshold ranges of the selected indices and the DWR data are used as the inputs while the target output being the squall speed associated with thunderstorms. The method of rough set theory is adopted in this study to identify the best combination of the instability indices and DWR data for estimating the squall speed. The method of rough set theory is capable of dealing with inconsistency in the data set, if any, while simulates the condition — decision support system. The certainty factor of the rough set theory is computed in this study for the condition which is the coupled influence of the instability indices and DWR data on the decision that is, the squall speed associated with thunderstorms. The results are validated with the observations of 2010.