A composite stability index for dichotomous forecast of thunderstorms

Thunderstorms are the perennial feature of Kolkata (22° 32???N, 88° 20???E), India during the premonsoon season (April?CMay). Precise forecast of these thunderstorms is essential to mitigate the associated catastrophe due to lightning flashes, strong wind gusts, torrential rain, and occasional hail and tornadoes. The present research provides a composite stability index for forecasting thunderstorms. The forecast quality detection parameters are computed with the available indices during the period from 1997 to 2006 to select the most relevant indices with threshold ranges for the prevalence of such thunderstorms. The analyses reveal that the lifted index (LI) within the range of ?5 to ?12?°C, convective inhibition energy (CIN) within the range of 0?C150?J/kg and convective available potential energy (CAPE) within the ranges of 2,000 to 7,000?J/kg are the most pertinent indices for the prevalence thunderstorms over Kolkata during the premonsoon season. A composite stability index, thunderstorm prediction index (TPI) is formulated with LI, CIN, and CAPE. The statistical skill score analyses show that the accuracy in forecasting such thunderstorms with TPI is 99.67?% with lead time less than 12?h during training the index whereas the accuracies are 89.64?% with LI, 60?% with CIN and 49.8?% with CAPE. The performance diagram supports that TPI has better forecast skill than its individual components. The forecast with TPI is validated with the observation of the India Meteorological Department during the period from 2007 to 2009. The real-time forecast of thunderstorms with TPI is provided for the year?2010.     

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.     

Cloud — Aerosol interaction during lightning activity over land and ocean: Precipitation pattern assessment

The present study attempts to identify the land - ocean contrast in cloud - aerosol relation during lightning and non-lightning days and its effect on subsequent precipitation pattern. The thermal hypothesis in view of Convective Available Potential Energy (CAPE) behind the land - ocean contrast is observed to be insignificant in the present study region. The result shows that the lightning activities are significantly and positively correlated with aerosols over both land and ocean in case of low aerosol loading whereas for high aerosol loading the correlation is significant but, only over land. The study attempts to comprehend the mechanism through which the aerosol and lightning interact using the concept of aerosol indirect effect that includes the study of cloud effective radius, cloud fraction and precipitation rate. The result shows that the increase in lightning activity over ocean might have been caused due to the first aerosol indirect effect, while over land the aerosol indirect effect might have been suppressed due to lightning. Thus, depending on the region and relation between cloud parameters it is observed that the precipitation rate decreases (increases) over ocean during lightning (non-lightning) days. On the other hand during non-lightning days, the precipitation rate decreases over land.     

Implementation of intuitionistic fuzzy logic to assess the predictability of severe thunderstorms

Thunderstorms are responsible for remarkable devastation when accompanied with lightning flashes, high wind gusts, torrential rain, hail and tornadoes. Weather hazards due to thunderstorms of such severe measure take place every year over Kolkata (22°32′N, 88°20′E), India during the pre-monsoon season (April–May). Prediction of severe thunderstorms is extremely important to cope with the devastations. However, forecasting severe thunderstorms is very difficult because the weather system is confined within a very small spatial-temporal scale. The network of observation systems is not adequate to detect such high frequency small scale weather. The purpose of the present study is to bring in the concept of Intuitionistic fuzzy logic as a decision — making technique to assess the predictability of severe thunderstorms over Kolkata in the premonsoon season. Different measures of entropies are used to extract the route of fuzziness. The intuitionistic fuzzy logic is implemented with ten years (1997–2006) observation of the occurrence/nonoccurrence of severe thunderstorms to assess the predictability. The result reveals that two consecutive severe thunderstorm days are highly probable after two consecutive non-thunderstorm days whereas the probability of severe thunderstorm is very less after three consecutive non-thunderstorm days during the pre-monsoon season over Kolkata. The result is compared with the box-and-whisker plot and validated with four years (2007–2010) observations of India Meteorological Department (IMD).     

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

Natural Hazards - 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...     

Vulnerability assessment through index modeling: a case study in Muriganga–Saptamukhi estuarine interfluve,Sundarban, India

The coastal regions, deltas, and estuaries are severely affected by the sea level rise and cyclonic activities and climate changes. Sundarban delta is one of the most mysterious landscapes in the world, which has successively evolved due to sediment accumulation by the great Ganga and Brahmaputra river system. The area is characterized by low-lying islands and a flat topography coupled with macro-tidal activities, powerful surges, and seasonal cyclonic events. All these conditions put together this landscape defenseless to frequent flood and erosion. Since the last hundred years, the face of Sundarban has been changed remarkably from wildest to human-occupied territory by protecting this low-lying flat plain from tidal inundation through artificial embankment. In this background, the current study attempts to highlight the spatial extent and magnitudes of internal risk factors of the region using the composite vulnerability index. Coastal vulnerability defines a system’s openness to flood and erosion risk due to hydrogeomorphic exposures and socio-economic susceptibility in conjunction with its capacity/incapacity to be resilient and to cope, recover, or adapt to an extent. Coastal vulnerability assesses the potential risk from erosion and flooding of any low-lying coastal region due to its physiographical and hydrological exposures, socio-economic and political susceptibility, and resilience capacity. A natural system affects the socio-economic scenario of any region. Hence, multidimensional databases can be more effective to understand the extent of exposure, susceptibility, and resilience of any system. To throw some light on the situation of vulnerability of western estuarine Sundarban, between Muriganga and Saptamukhi interfluve, the composite vulnerability index has been carried out to delineate the magnitude and spatial extent of vulnerability with the help of quantitative techniques and geospatial tools. The estuarine tracts and coastal parts of the Ganga delta are two of the most densely populated areas in the world. The study highlights the critical situation of the population under different potential risk classes residing in the study area with the intention of suggesting some proper course of action of planning and management to conserve coastal communities in their original habitat.     

Convective energies in forecasting severe thunderstorms with one hidden layer neural net and variable learning rate back propagation algorithm

Thunderstorms are perennial features of India. However, the severe thunderstorms of pre — monsoon season (April–May) over Kolkata (22°32′N, 88°20′E) are of great concern for imparting devastating effect on life and property on the ground and aviation aloft. The study is thus, focused on developing one hidden layer neural network model with variable learning rate back propagation algorithm to forecast such thunderstorms. Convective available potential energy (CAPE) and convective inhibition energy (CIN) are selected as the input parameters of the model after the estimation of various skill scores like, Probability of Detection (POD), False Alarm Ratio (FAR), Heidke Skill Score (HSS) and Odds Ratio Skill Score (Yule’s Q) on different stability indices. During training the model, the squared error for forecasting severe thunderstorms is observed to be 0.0022 when the values of CIN within the range of 0 to 140 J kg^?1 is taken as the input whereas the error is observed to be 0.0114 while the values of CAPE within the range of 2000 to 7000 J kg^?1 is considered as the input. The values of CIN and CAPE at twelve to six hours prior to the occurrence of severe thunderstorms are considered in this study. During validation of the model, the percentage of prediction error with the values of CIN as input is observed to be 0.042% and that with CAPE as input is 0.162%. The values of CIN within the range of 0–140 J kg^?1 are observed to be more persistent in forecasting severe thunderstorms over Kolkata than the values of CAPE within the range of 2000–7000 J kg^?1.     

Investigation on the Influence of the Column Ozone Anomaly on the Energetics of Tropical Cyclones Over NIO and Related Air–Sea Interaction

An investigation on the temporal and spatial variation of ozone using the total column ozone (TCO) values during the cyclonic activities over North Indian Ocean (NIO) is carried out during the period from 1997 to 2012. The stepwise variation of TCO during the passage of the tropical cyclones over the Bay of Bengal and the Arabian Sea of the NIO is examined. The anomalies in TCO are estimated at each step of the life span of the cyclones starting from the genesis to landfall stages. The result reveals that the TCO values are quite high prior to the formation of the depression over NIO; however, at the stage of cyclogenesis it decreases which, with the increase in the intensity of the cyclones, further decreases and becomes minimum near the coast during the landfall. The maximum negative anomaly in TCO is observed for maximum intensity of the tropical cyclones as well as during the landfall. The result further shows that when the cyclones die out after the landfall the TCO regains the normal value. It is further observed that the reduction in TCO enhances the accumulated cyclone energy over NIO. The result finally shows that, the higher the energy of the cyclones, the lower becomes the stratospheric warming, that is, the higher the stratospheric cooling.