The purpose of the present study is to investigate the existence of deterministic chaos in the time series of occurrence or non-occurrence of severe thunderstorms of the pre-monsoon season over the Northeastern part of India. Results from the current study reveal the existence of chaos in the relevant time series. The corresponding predictabilities are also computed quantitatively. The study recommends that the formulation of numerical weather prediction models for forecasting the occurrence of this high frequency meso-scale convective system must take into account the intrinsic chaos. © World Scientific Publishing Company.

SUTAPA CHOUDHURY

Anthropology

University of Calcutta (informally known as Calcutta University or CU) is a collegiate public state university located in Kolkata, West Bengal, India.&nbsp;Within India it is recognized as a &quot;Five-Star University&quot; and accredited &quot;A&quot; Grade by National Assessment and Accreditation Council.

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The University of Calcutta has secured the Fifth position among the Universities of India in the prestigious &quot;Indian University Ranking 2019&quot; list, released by the NIRF of the Ministry of Human Resource Development, Government of India.

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It was established on 24 January 1857, and was one of the first institutions in Asia to be established as a multidisciplinary and Western-style university. Its alumni and faculty include several heads of state, heads of government, social reformers, prominent artists, only academy award winner and Dirac medal winner in India, many Fellows of the Royal Society and five Nobel laureates as of 2019 which is highest in South Asia.

University Of Calcutta

Predictability of chaos inherent in the occurrence of severe thunderstorms

Advances in Complex Systems

An information theoretic approach based on Shannon entropy is adopted in this study to discern the influence of pre-monsoon thunderstorm on some surface parameters. A few parameters associated with pre-monsoon thunderstorms over a part of east and northeast India are considered. Maximization of Shannon entropy is employed to test the relative contributions of these parameters in creating this weather phenomenon. It follows as a consequence of this information theoretic approach that surface temperature is the most important parameter among those considered. Finally, artificial neural network in the form of multilayer perceptron with backpropagation learning is attempted to develop predictive model for surface temperature. © 2018, Springer Science+Business Media B.V., part of Springer Nature.

Natural Hazards

Shannon entropy maximization supplemented by neurocomputing to study the consequences of a severe weather phenomenon on some surface parameters

Thunderstorms are well-known severe weather phenomena of the Gangetic West Bengal (GWB) region of India. The objective of the present study is to identify the ranges of Max_Z parameters of Doppler Weather Radar (DWR) associated with precipitating clouds that eventually grow into thunderstorms and to obtain a model to assess the predictability of thunderstorm and non-thunderstorm events with maximum possible accuracy during the pre-monsoon season (April–May) over the metropolis Kolkata (22.6°N; 88.4°E) enclosed within GWB (20–26°N, 85–91°E), India. The DWR imageries are analyzed to identify the stages of thunderstorm development. The survival of the fittest principle of genetic algorithm (GA) is implemented to find a suitable combination of the DWR Max_Z parameters; the reflectivity, distance of the first detected echo from Kolkata where the DWR is installed and the echo top height for the genesis of thunderstorms. The problem is posed as an optimization problem and the values of the parameters are converted into binary strings. The result reveals that the echoes with reflectivity between 44 and 48 dBZ at a distance of 250–300&nbsp;km from Kolkata with echo top height between 13 and 15&nbsp;km have the maximum possibility to grow into a thunderstorm. The artificial neural network (ANN) model is developed with the values of the Max_Z parameters optimized by GA as the inputs. The target of the ANN model is to forecast the type of the echo cells leading either to thunderstorm or non-thunderstorm events. The result further reveals that the ANN model with three hidden layers and one node in each layer is the most suitable model for estimating the likelihood of thunderstorm/non-thunderstorm events with mean absolute error (MAE) of 0.71/2.81. The result of the study is validated with the observation of India Meteorological Department. © 2014, Springer-Verlag Wien.

Meteorology and Atmospheric Physics

An investigation on the evolution process of thunderstorms over a metropolis of India using DWR Max_Z products and genetic algorithm

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-monsoonmonths 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 systemconsists of multiple linear regression (MLR) equations, and the purpose is to nowcast the categories of thunderstorms over Kolkata, both ordinary (wind speed &lt; 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 ofwind speed accompaniedwith thunderstorms over Kolkata are considered for the study. TheMLRmodels are formulated with the cloud parameters as input and the target output being the peak wind speed associatedwith 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 &lt; 12 h. © Springer Science+Business Media Dordrecht 2014.

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

Thunderstorms prevailing over tropics and midlatitudes depict dissimilar features relating to the thermodynamic and dynamic aspects. The identification of the physical characteristics of the tropical and midlatitude thunderstorms is the main objective of the present study. The stations Kolkata (22.6oN, 88.4oE) and Denver (39.47oN, 104.32OW) are selected from the tropics and midlatitudes for the comparative analyses. The study reveals that the average storm relative helicity (SRH) and the lapse rate between 700 and 500 hPa level is much higher over Denver compared to Kolkata during thunderstorm days. The study further reveals that the surface to mid troposphere (upto 500 hPa) become drier (∼2 times) over Denver than Kolkata prior to the occurrence of thunderstorms while the upper tropospheric (300-100 hPa) humidity remains comparable for both the locations. © The Korean Meteorological Society and Springer 2014.

Asia-Pacific Journal of Atmospheric Sciences

Comparison of tropical and midlatitude thunderstorm characteristics anchored in thermodynamic and dynamic aspects

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.© The Korean Meteorological Society and Springer 2013.

Journal	Advances in Complex Systems
Publisher	WORLD SCIENTIFIC PUBL CO PTE LTD
ISSN	0219-5259