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.     

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.     

Longitudinal vibration of a slender bar with nonuniform characteristics in presence of a magnetic field

Summary In the present article the magnetic influence on the free vibrations of a slender bar with characteristics slightly different from those of a uniform one has been discussed. It presents a simple approximate solution to this problem. Moreover the frequency equation has been derived.     

A context-sensitive Bayesian technique for the partially supervised classification of multitemporal images

An advanced context-sensitive classification technique that exploits a temporal series of remote sensing images for a regular updating of land-cover maps is proposed. This technique extends the use of spatio-contextual information to the framework of partially supervised approaches (that are capable of addressing the updating problem under the realistic, though critical, constraint that no ground-truth information is available for some of the images to be classified). The proposed classifier is based on an iterative partially supervised algorithm that jointly estimates the class-conditional densities and the prior model for the class labels on the image to be classified by taking into account spatio-contextual information. Experimental results point out that the proposed technique is effective and that it significantly outperforms the context-insensitive partially supervised approaches presented in the literature.     

Analysis of biodegradation in a 3-D heterogeneous porous medium using nonlinear stochastic finite element method

Probabilistic analysis by Monte Carlo Simulation method (MCSM) is a computationally prohibitive task for a reactive solute transport involving coupled PDEs with nonlinear source/sink terms in 3-D heterogeneous porous media. The perturbation based stochastic finite element method (SFEM) is an attractive alternative method to MCSM as it is computationally efficient and accurate. In the present study SFEM is developed for solving nonlinear reactive solute transport problem in a 3-D heterogeneous medium. Here the solution of the biodegradation problem involving a single solute by a single class of microorganisms coupled with dynamic microbial growth is attempted using this method. The SFEM here produces a second-order accurate solution for the mean and a first-order accurate solution for the standard deviation of concentrations. In this study both the physical parameters (hydraulic conductivity, porosity, dispersivity and diffusion coefficient) and the biological parameters (maximum substrate utilization rate and the coefficient of cell decay) are considered as spatially varying random fields. A comparison between the MCSM and SFEM for the mean and standard deviation of concentration is made for 1-D and 3-D problem. The effects of heterogeneity on the degradation of substrate and growth of biomass concentrations for a range of variances of input parameters are discussed for both 1-D and 3-D problems.     

Exploration of geothermal activity using time series analysis of subsurface gases data from Bakreswar hot springs area,eastern India

Geothermal power seems to be a potential source of green energy in India. But these renewable energy resources are still ignored in India even after having a lot of potential sources as seen in more than 300 hot springs scattered throughout different geothermal areas of the country. Many of them could be utilized for power generation using the earth’s internal heat. More hours are needed to explore these geothermal areas using geochemical, geophysical techniques, and statistical analysis to qualitatively estimate power harnessing capabilities and sustainability of the areas for generation of geothermal power. In the present paper, attention has been focused to investigate Bakreswar geothermal field of India by continuous (24*7) and online monitoring of terrestrial gases such as He and radioactive gases (²²²Rn) in hot spring emanations of the geothermal area for more than 5 years along with discrete measurement of some other geochemical and geophysical parameters. The discrete measures imply that the seven hot spring vents at the study area are linked to the aquifer through different ways covering non-uniform rock assemblies and ascertain the presence of high amount of radioactive minerals at the underneath terrains. Stable activities with a high-out flux of ²²²Rn and He for a prolonged time period is expected within the reservoir present at the study area as consequence of analysis of the temporal variations and statistical measures of the continuous data sets. Seasonal variation of time series data also recommends that the high amount of radioactive sources present at the crust of the reservoir is able to produce enough quantity of heat irrespective of meteorological effects. The investigation on the power spectra interferes that the geothermal system is still in quite active phase on the influence of tectonic activities. Therefore, the geothermal reservoir present at the Bakreswar geothermal area may be utilized as a constant and continuous heat source for a long time period to run a geothermal power plant.     

Evaluating Environmental Sensitivity of Arid and Semiarid Regions in Northeastern Rajasthan,India

Arid regions of Rajasthan in India are characterized by harsh environments, fragile ecosystems, and limited water resources and arable lands. Desertification is one of the major land‐degradation problems in these regions. Recent changes in the regional climatic regime and the low availability of water in this northwestern state of India, pose the threat of desertification in the agriculturally productive part of the state. This study aims to delineate the environmentally sensitive areas to desertification in the districts of Jhunjhunun and Sikar in Rajasthan, India. It uses geospatial data from various sources, and combines them to calculate an environmental sensitivity index (ESI), which will allow the quantification of the area's vulnerability to desertification. The study uses the analytical hierarchical process (AHP) to assign respective weights to individual factors for each index calculation. Land‐cover maps and socio‐economic data have been used to understand their relationship with ESI. Results showed that 13 percent and 45 percent of the study area are under highly sensitive and moderately sensitive zones, respectively. Mapping and monitoring of environmentally sensitive areas in such arid regions are critical to sustainable use and planning of natural resources namely water and land‐cover and are helpful in determining priority actions and their successful implementation.