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Heatwaves are acknowledged to be the major meteorological disaster, causing a noticeable impact on humans and animals’ lives during the last few decades. The number, frequency, duration, intensity, and areal extent of the heatwaves are on the rise during recent years. The Maximum temperature data of 2013 is analyzed to assess the synoptic nature, intensity, frequency, and various significant facets of the heatwave over the south peninsular states of Andhra Pradesh and Telangana. Indian subcontinent experienced a major heatwave during 2013, which claimed 1216 human lives. Even though the highest intensity of maximum temperatures is observed in May over major areas of India, the increasing (incidence, duration, number of spells, and the sweltering temperatures) number of heatwaves are observed over many parts of the country. The northwest and southeast coastal regions are the two heat wave prone regions. The advection of heat from the northwest with the aid of north-westerly winds causes heatwaves over northwest India to sweep or move towards India’s southeast and east coast. The heatwave record over south-eastern India, i.e, Andhra Pradesh and the adjoining Telangana state during May 22-24, 2013 were described in this study. Maximum temperatures above 40°C are observed with a sudden rise by 6 to 7°C over the study region. An attempt is made to predict the maximum temperatures 72 hours before the existence of a heatwave at 3 km horizontal resolution using the Advanced core of the Weather Research and Forecasting (WRF) model. Model predicted temperature values match with observations and the statistical metrics show a high index of the agreement, lower values for root-mean-square error and mean absolute error. Atmospheric circulation patterns associated with this heatwave are also presented. The arrest of sea breeze, the hovering of diabatic heat because of subsidence is the factor that abetted the heatwave blockade over the south-eastern part of the country. The WRF model forecasts could present the occurance of the heat wave over AP and Telangana region with 72 hour lead time with high accuracy.

This paper brings out the interconnection of flash rate density (FRD) with convection and stability parameters over Andhra Pradesh (AP), India. The convection parameters include rainfall, relative humidity, specific humidity, surface air temperature (SAT) and air temperature (at 850 mb). The stability parameters include convective available potential energy (CAPE), lifted index, K-index, total totals index (TTI), humidity index and total precipitable water. Both convective and stability parameters indicate good correlation with lightning activity. SAT and AT 850 mb have shown good correlations with lightning, which is a clear indication of interaction between warm air and dry air. CAPE and TTI have shown strong positive correlation with lightning activity. The correlation coefficient between FRD and CAPE is 0.81. We have also studied the influence of convective and stability parameters during lightning and no lightning activity. Later, we also attempted the estimation of lightning activity by using artificial neural network model. By using convection and stability parameters, six learning algorithms were used for training the artificial neural network. Scaled conjugate gradient backpropagation training algorithm has given the better estimation, whereas resilient backpropagation training algorithm has shown the poor estimation of FRD.

A “hot blob”, distinct hot region, is identified over Vidarbha in the south-central parts of the Indian subcontinent during the pre-monsoon season from the analysis of gridded surface air maximum temperature data from India Meteorological Department for the period 1951–2019. Spatial distribution and frequencies of temperatures > 40 °C and > 42 °C establish the hot blob over Vidarbha region. A similar analysis of simulated maximum temperatures from the NEX-GDDP substantiates the revelation of the “hot blob” over Vidarbha. Further, analysis of the wind circulation at 850 hPa over South Asia region indicates that the “COL” region between the two seasonal high-pressure systems over the Indian Ocean seas, Bay of Bengal and Arabian Sea promotes accumulation of heat over Vidarbha. Further, horizontal temperature convergence complimented by strong local heating of the black soil aids and abets the sustenance of the “hot blob”. This “hot blob” region is observed to be hotter as well as having higher frequencies of hot days than the north-west desert Rajasthan region and assumes importance as its modulation causes heatwaves over the south-east coastal regions. This study establishes the presence of the hottest region over Vidarbha in south-central parts, paradoxically hotter than the desert north-west region of India.

Thunderstorm events usually take place in cumulonimbus clouds which are complemented with intense rainfall and high-speed winds. In general, rainfall parameter has massive significance when compared to other parameters. In this paper, a group of thunderstorm-related stability parameters were analyzed for pre-monsoon season only. Later, we also tried to study the association between thunderstorm-related stability parameters and rainfall parameter in pre-monsoon season over Anakapalle (Visakhapatnam district) during 2001–2010. We have utilized ERA-Interim ECMWF reanalysis daily datasets for this study. We also tried to compare IMD thunderstorm occurrence days with NOAA CPC-calculated rainfall days in pre-monsoon season over Anakapalle region for every year during 2001–2010. Out of those parameters, upward vertical velocity, convective available potential energy, K-index (KI), humidity index and total totals index parameters have shown good thresholds supporting the rainfall activity during pre-monsoon season. Later, we have also attempted the prediction of DCI and KI parameters over Anakapalle region using artificial neural network (ANN) and auto-regressive moving average (ARMA) techniques. In comparison between the two techniques, ANN technique has shown good correlation with ERA-Interim ECMWF reanalysis data.

In this present work, an attempt has been made to analyze various thunderstorm-related parameters and their influence over the two stations Visakhapatnam (VSK) and Machilipatnam (MTM). The thunderstorm-related parameters used in the present study are convective available potential energy (CAPE), lifted index, K-index, total totals index (TTI), humidity index, convective inhibition, thunderstorm prediction index (TPI), deep convective index (DCI) and updraft vertical velocity. This analysis was carried out using NCEP NCAR reanalysis monthly data for the time period from 1948 to 2012. These parameters have given good guidance for studying the thunderstorm event. We also analyzed IMD thunderstorm occurrence days reported at two stations, i.e., VSK and MTM with NCEP NCAR (daily data) calculated CAPE, TTI, TPI and DCI parameter threshold days in pre-monsoon season for every year during the time period 2010 to 2019. Out of those four parameters, TTI has shown good correlation with the IMD recorded days. So we have attempted the prediction of thunderstorms using artificial neural network (ANN) and auto-regressive moving average (ARMA) techniques for TTI parameter. While using these techniques, we have experimented in three training sets, i.e., 90%, 80% and 70%. Another attempt has been made to assess the skill of ARMA and ANN techniques in forecasting the occurrence of thunderstorm activity at VSK and MTM stations. The present study suggests that ANN has high skill than ARMA. From this study, we can understand that VSK has more chances for thunderstorms than MTM.

El-Nino Southern Oscillation (ENSO) is a major ocean–atmospheric coupled phenomenon in the Tropical Pacific Ocean that influences the Indian summer-time temperatures during April and May. In the current analysis, we examined the impact of ENSO on Indian heat waves. Temperature zones are identified when the maximum temperature exceeds 42°C, particularly during April and May over the Indian subcontinent. The results depicted three different regions (west Rajasthan, northwest and southwest UP, and south-central India) that experienced the frequency in days with maximum temperature above 42°C. These means are compared with those of Preceding El Nino (PEN), El Nino (EN) and Succeeding El Nino (SEN) events. It is found that PEN and SEN years experienced high positive anomalies. It can be seen that an increase in surface temperatures and heat waves are closely associated with the strengthening of north-westerlies and reduction of geopotential height at 500 hPa level, reduction of soil moisture and increase of sea surface temperatures (SST) during PEN and SEN events. The results also show that the variations in temperature and heat waves over northeast India are different from the rest of India during EN and SEN years. The three maximum temperatures regions of (1) west Rajasthan, (2) east Uttar Pradesh, and (3) Vidarbha, are identified based on both the magnitude and frequency days of above 42°C maximum temperatures. The spectrum analysis is performed for the monthly time series of the days exceeding daily surface maximum temperature 42°C for the three regions and identified the dominant periodicities with 2.2–2.8 and 3.3–8.5 years, which could be endorsed to ENSO and QBO frequencies.

Thunderstorms, associated with lightning and heavy rain, are a weather hazard causing human deaths, urban floods and damage to crops. Current work attempted to study the thunderstorms over Andhra Pradesh, coastal state in southeast India, using multiple satellite datasets, gridded rainfall, Doppler Radar Images and Advanced Research Weather Research and Forecasting (ARW) model simulations during the pre-monsoon seasons of 2017 and 2018. Thermodynamic stability indices computed using INSAT-3D/3DR satellite data were used to identify precursors and lead time of prediction. India Meteorological Department (IMD) daily gridded rainfall data were used to identify the thunderstorm occurrence days, and Doppler Radar Images and INSAT imagery were conjointly used to fix the location. Eight severe thunderstorm cases were analyzed to assess the precursors and the predictability. Further, ARW model predictions for two thunderstorm cases were performed and stability indices computed using model output were compared with satellite-based indices for evaluation. Statistical metrics had shown good agreement of ARW model-based stability indices with satellite-based stability indices. Model had simulated rainfall and cloud properties associated with thunderstorm activity. The results illustrated the predictability of the location and intensity of thunderstorms with 3–4 hrs lead time, which would find usefulness in the real-time prediction of thunderstorms.

Heat wave of 2015 over India, a natural disaster with 2500 human deaths, was studied to understand the characteristics, associated atmospheric circulation patterns and to evaluate its predictability. Although temperatures are highest in May over India, occurrence of heat wave conditions over southeast coastal parts of India in May 2015 had been unanticipated. Analyses revealed that isolated region of Andhra Pradesh (AP) had experienced severe heat wave conditions during May 23–27, 2015, with temperatures above 42 °C and the sudden escalation by 7–10 °C within a short span of 2–3 days. Short-range weather predictions with Advanced Research Weather Research and Forecasting model at 3-km resolution, up to 72-h lead time, have been found accurate with statistical metrics of small mean absolute error and root-mean-square error and high index of agreement confirming the predictability of the heat wave evolution. Analyses have indicated that regional atmospheric pressure disparities within the Eurasia region, i.e., increased pressure gradient between the Middle East and India, had been responsible for increased northwest wind flow over to northwest India and to southeast India which have advected higher temperatures. Estimates of warm air advection have shown heat accumulation over AP region, due to sea breeze effect. The study led to the conclusion that changing pressure gradients between Middle East and India, enhancement of northwest wind flow with warm air advection and sea breeze effect along southeast coast blocking the free flow have contributed to the observed heat wave episode over coastal Andhra Pradesh.

Fragments of live colonies of scleractinian coral Acropora sp. and Montipora sp. under the family Acroporiidae were collected from Gulf of Mannar and transplanted in Pirotan, Narara and Mithapur reefs of Gulf of Kachchh Marine National Park. All the transplanted corals survived one complete season and it was observed that 87 nubbins out of the total 110 samples survived in Narara reef and 70 nubbins out of 102 samples stayed alive in Pirotan Island. Growth rate was measured for four months period, and it was found maximum in Narara reef, while minimum in Pirotan Island. The rate of sedimentation was higher during monsoon and low in winter season. Present study showed that species of Acropora and Montipora are suitable for transplantation in Gulf of Kachchh Marine National Park, Gujarat, India.

In this work, we revisit multi-resolution analysis (MRA) methods for object recognition. We find an optimal sparse representation of an image using a second-generation Fast Discrete Curvelet Transform (FDCT) and present a novel curvelet approach based on thin plate splines (TPS). Measurement of local deformation at each FDCT coefficient is detailed. Specific deformations in the TPS-based curve-let transformation are identified by minimization (Curvature) of total bending energy. Shape toning is processed through the Euclidean distance. Results of implementation of proposed descriptor for five standard databases are analyzed, while their comparison with other revealed relative efficiency.

The Gulf of Kachchh is characterised by a strong tidal variation and the reef communities are capable of higher exposure time during negative tides. About 11 sites located along the Marine National Park (MNP) from west to east were studied for assessing the present status of live coral cover along with other life-form categories. In the present study, the maximum live coral cover was recorded in Pirotan Island, followed by Laku Point, Mithapur and Boriya reef. Multivariate analyses such as Principal Component Analysis and Correspondence Analysis supported that the contribution of live coral cover was more towards Pirotan Island whereas the contribution of coral species cover was more towards Laku Point. Bray-Curtis cluster analysis categorized all the study sites into four major clusters with 78 % similarity based on life-form categories. Among them, two clusters from western region (one forming site from Mithapur Reef to Lakku Point and another one forming from Ashaba Gugar Reef to Dabdaba Island), the third one combines the western and eastern regions (Boriya Reef to Kalubar Island), the fourth one comprises the eastern region (Narara Reef to Sikka), and one outlier Pirotan Island. Based on coral species cover, two major clusters with 55 % similarity were formed. Among them, one cluster was formed from Pirotan Island to Kalubar Island in the eastern side of MNP and the second one comprised the western region of MNP (Boriya Reef to Laku Point), and one outlier Mithapur Reef. Thirty one species of live corals belonging to 8 families and 18 genera were recorded in the Marine National Park itself during the study period.

Relevance of ‘face recognition’ (FR) in the modern world requirements is presented as a case of human machine interaction. Physical conditions that influence the face recognition process regarding the facial features, illumination changes and viewing angles etc. are discussed. Face recognition process predominantly depends on machine perception i.e. information through an array of pixels with respect to the facial image. Details of eigenface approach through the involvement of contemporary algebraic and statistical analysis are revisited. Methodology involved in the Principal Component Analysis and advantages of exposing the data to incremental training (using PCA) are discussed. A model for the implementation of IPCA over the face databases is proposed to estimate its performance for the face recognition process. Performance of the present model is studied in the domain of Euclidean distance, decay parameter, recognition rate, eigenvalues and overall computational time. Present IPCA model administered over standard ORL, FERET databases along with that over the JNTU face database with large number of face images revealed relative performance. The merit of present IPCA is inferred through enhanced recognition rate and reduced complexity (in the algorithm), intelligent eigenvectors and lesser computational time. The results are presented in the wake of the body of data available with other methods.