Probing for overshooting as extreme event of thunderstorms

Thunderstorm overshooting is rare but not an unusual phenomenon in a metropolitan of India, Kolkata (22.57° N; 88.36° E) during the pre-monsoon months (April–May). An attempt is made in this study to identify the important parameters differentiating the thunderstorms in overshooting and non-overshooting categories through data analytics from 2000 to 2015. The present investigation on parametric classification would facilitate in estimating the predictability of thunderstorms with overshooting which subsequently might assist in operational forecast of thunderstorm severity over Kolkata. The altitudes of lifting condensation level (LCL), wind shear, bulk Richardson number (BRN), gust speed, boundary layer characteristics and their correlation with thunderstorm cloud top height (CTH) and also their variation and distribution during overshooting (OTS) and non-overshooting (TS) thunderstorms are analyzed in this study. The result depicts that over Kolkata the intensity of storms during OTS is higher than during TS though the frequency of OTS is less than that of TS. The results further show that the potential temperature (θ), equivalent potential temperature (θ_e), mixing ratio (e_s) in the boundary layer, convective available potential energy, convective inhibition energy, BRN and gust speed play significant roles in regulating the CTH during OTS and TS thunderstorms over Kolkata.

     

Effects of spatial variation of soil properties on seismic performance of port structures

From past seismic events such as the 1995 Kobe (Hyogoken-Nanbu) and 2001 Nisqually earthquakes, it was found that liquefaction-induced lateral spread has caused significant damage to structures such as buildings and bridges, in addition to underground utility facilities like pipelines. In this respect, seaport facilities are particularly vulnerable to these liquefaction-related damages, because they are usually constructed on poorly consolidated natural deposit or fills. This study investigates the effect of the liquefaction and lateral spread on the seismic response of caisson type quay walls. For this purpose, 2D nonlinear dynamic analyses of soil–structure system are carried out with the aid of finite difference software, FLAC. The unique feature of this study lies in the fact that the 2D soil system is idealized as homogeneous non-Gaussian random field. A simulation algorithm is then used to generate a set of digital realizations of 2D random field sample. Each realization is used for the dynamic analysis to generate a unique response of the soil–structure system. Repeating this analysis for the entire set of realizations, the probabilistic nature of the response is characterized in the Monte Carlo sense. This result based on random field is compared with the response obtained under the uniform field assumption with the mean value of soil property. The comparison shows that in general the uniform model provides unconservative result compared with the response from the random field model due to nonlinear behavior of the soil–structure system. It is also found that the consideration of spatial variation of soil can capture the dispersion of observed response of quay walls.     

The Neoproterozoic Cratonic Successions of Peninsular India

The Peninsular India hosts extensive record of Mesoproterozoic, and Neoproterozoic successions in several mobile belts, and cratonic basins. The successions provide excellent opportunities for chronostratigraphic classification, in tune with the chronometric classification adopted by IUGS for inter-regional correlation on a global scale. Major tectono-thermal events at 1000–950 Ma in the mobile belts, correlatable with the Grenville orogeny may be considered as the datum for Meso-Neoproterozoic classification in India. Principles of chronostratigraphic classification, however, can not be applied yet to the cratonic successions of India because of inadequate radiometric data, paucity of biostratigraphic studies, and lack of regionally correlatable stratigraphic or palaeoclimatic datum. The kimberlite magmatism which affected the Peninsular India on a continental scale at about 1100 Ma, holds the key to the identification of Neoproterozoic successions of the cratonic basins. Thus, the stratigraphically confined diamond-bearing conglomerates and/or the tuffs associated with kimberlites, may be considered as the datum to define the base of the Neoproterozoic, fixed at about 1000 Ma. Accordingly, the Rewa, and Bhander Groups in the Vindhyan basin, the Kurnool Group in the Cuddapah basin, the Jagdalpur Formation in the Indravati basin, and the Sullavai Group in the Pranhita-Godavari basin are taken to represent the Neoproterozoic successions in the Peninsular India. The Chattisgarh Group in the central India, the lower part of the Marwar Supergroup in western Rajasthan, the Badami Group in the Kaladgi basin, and the Bhima Group are the other “possible Neoproterozoics” in the Peninsula.The closing phase of the Mesoproterozoic in all these basins are characterised by stable shelf lithologic associations attesting to high crustal stability. The Neoproterozoic basins, by contrast, mark a new phase of rifting, and extension, and the basin fills exhibit signatures of initial instability which evolved with time into a more stable platformal condition. A major episode of sea level rise has been recorded in most of the basins. The riftogenic origin, and evolution of the basins are comparable with the history of Neoproterozoic basins of Australia though there is no unequivocal record of glaciation in the Indian formations.     

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.     

Evolution of the Banded Gneissic Complex of central Rajasthan,India

The Banded Gneissic Complex of central Rajasthan, the only gneissic basement in India considered to underlie an early Precambrian sedimentary suite unconformably, comprises composite gneisses formed by extensive migmatization of metasedimentary rocks of diverse composition. The migmatites and the metasedimentaries maintain a structural continuity in a plan of superposed deformations, with the migmatite front involved in the early folding but transgressing the stratigraphic boundaries. Structures in the metasedimentary palaeosomes within the gneisses match in their entirety those in the migmatite host and the metasedimentary bands outside. On a smaller scale of microsections, migmatites show para tectonic crystallization with reference to the first deformation. The Banded Gneissic Complex thus loses its unique position in the Indian Precambrians as older than the earliest decipherable sedimentary series, but is older than the Aravalli rocks of the type area, the partially migmatized metasedimentaries belonging to an earlier series.     

Study of near surface boundary layer characteristics during pre-monsoon seasons using micrometeorological tower observations

Studying the boundary layer is imperative because severe weather in this portion of the atmosphere impacts on environment and various facets of national activities and affects the socioeconomic scenario of a region. Near surface boundary layer characteristics are investigated through the vertical variation of fluxes of heat, moisture, momentum, kinetic energy and Richardson number during the pre-monsoon season (April-May) at Kharagpur (22° 30’ N, 87° 20’ E) and Ranchi (23° 32’ N, 85° 32’ E) with 50 and 32 m tower data, respectively, on thunderstorm and non-thunderstorm days. The temporal variation of fluxes within the boundary layer and the kinetic energy at different logarithmic heights are observed to vary significantly between thunderstorm and non-thunderstorm days. The heat and momentum fluxes show a maximum peak while the moisture flux shows a sudden attenuation just before the occurrence of thunderstorms. The wind field depicts to play a crucial role at the inland station Kharagpur, which is in the proximity of the Bay of Bengal, compared to the station Ranchi, situated over hilly terrain on Chotanagpur. The micrometeorological study of the boundary layer reveals a significant finding pertaining to observe the passage of thunderstorms. It is observed that the ratio of the potential temperature (θ) and equivalent potential temperature (θe) remains confined within a critical range between 0.85 and 0.90 during the passage of thunderstorms.     

Network of seismo-geochemical monitoring observatories for earthquake prediction research in India

Present paper deals with a brief review of the research carried out to develop multi-parametric gas-geochemical monitoring facilities dedicated to earthquake prediction research in India by installing a network of seismo-geochemical monitoring observatories at different regions of the country. In an attempt to detect earthquake precursors, the concentrations of helium, argon, nitrogen, methane, radon-222 (²²²Rn), polonium-218 (²¹⁸Po), and polonium-214 (²¹⁴Po) emanating from hydrothermal systems are monitored continuously and round the clock at these observatories. In this paper, we make a cross correlation study of a number of geochemical anomalies recorded at these observatories. With the data received from each of the above observatories we attempt to make a time series analysis to relate magnitude and epicentral distance locations through statistical methods, empirical formulations that relate the area of influence to earthquake scale. Application of the linear and nonlinear statistical techniques in the recorded geochemical data sets reveal a clear signature of long-range correlation in the data sets.     

Disparity in the characteristic of thunderstorms and associated lightning activities over dissimilar terrains

Thunderstorms and associated lightning flash activities are studied over two different locations in India with different terrain features. Lightning imaging sensor (LIS) data from 1998 to 2008 are analyzed during the pre-monsoon months (March, April and May). The eastern sector is designated as Sector A that represents a 2° × 2° square area enclosing Kolkata (22.65°N, 88.45°E) at the centre and covering Gangetic West Bengal, parts of Bihar and Orissa whereas the north-eastern sector designated as Sector B that also represents a 2° × 2° square area encircling Guwahati (26.10°N, 91.58°E) at the centre and covering Assam and foot hills of Himalaya of India. The stations Kolkata and Guwahati are selected for the present study from Sector A and Sector B, respectively, as these are the only stations over the selected areas having Radiosonde observatory. The result of the present study reveals that the characteristics of thunderstorms over the two locations are remarkably different. Lightning frequency is observed to be higher in Sector B than Sector A. The result further reveals that though the lightning frequency is less in Sector A, but the associated radiance is higher in Sector A than Sector B. It is also observed that the radiance increases linearly with convective available potential energy (CAPE) and their high correlation reveals that the lightning intensity can be estimated through the CAPE values. The sensitivity of lightning activity to CAPE is higher at the elevated station Guwahati (elevation 54 m) than Kolkata (elevation 6 m). Moderate resolution imaging spectrometer (MODIS) data products are used to obtain aerosol optical depth and cloud top temperature and employed to find their responses on lightning radiance.     

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

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.     

Swarm intelligence and neural nets in forecasting the maximum sustained wind speed along the track of tropical cyclones over Bay of Bengal

Tropical cyclones are well-known extreme weather and the cause of considerable damages, injuries and loss of life. The assessment of the maximum sustained wind speed along the track of the tropical cyclones is very important for estimating the strength of the cyclones. The swarm intelligence in the form of ant colony optimization (ACO) technique is introduced in this study to compute the pheromone deposition along the track of tropical cyclones followed by neural nets to forecast the maximum sustained wind speed of the cyclones occurring over the Bay of Bengal of North Indian Ocean. The ACO is a nonlinear problem-based meta-heuristic optimization method for finding approximate solutions to discrete optimization problems and simulates the decision-making processes of ant colony similar to other adaptive learning techniques. The method has shown its application potential in various fields including the prediction of monsoon rainfall. In this study, the amount of pheromone deposition during the successive stages of the cyclones has been estimated. A range of minimum central pressure (MCP), central pressure drop (PD), maximum sustained wind speed (MSWS) and intensity (T-No) associated with the cyclones of Bay of Bengal are utilized to form the input matrix of the neural nets. The neural nets are trained to forecast the maximum sustained wind speed along the track of the tropical cyclones over Bay of Bengal. The result reveals that the errors in forecasting the MSWS along the track of tropical cyclones with 6, 12, 18 and 24 h lead time are 2.6, 2.9, 3.1 and 4.8, respectively. The result is compared with the existing dynamical, statistical and adaptive models to evaluate the skill of the present model. The result is well validated with observation.