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Coral disease is a major cause of coral reef degradation, yet many diseases remain understudied. Yellow-banded tissue loss disease (YBTLD) has a distinct gross lesion morphology and to date has only been reported from the Arabian/Persian Gulf; little else is known about the ecology of the disease. We report on the first occurrence of YBTLD outside of the Arabian/Persian Gulf at 2 sites (Laku Point, Narara Reef) within the Gulf of Kachchh (GoK) located on the northwest coast of India. At Narara Reef, YBTLD was observed at 12 out of 24 transects with an average prevalence of 4.7 ± 1.3%. At Laku Point, YBTLD was observed at 19 out of 24 transects with an average prevalence of 5.4 ± 1%. Four out of 15 coral genera within transects had signs of YBTLD and included Goniopora, Dipsastraea, Lobophyllia, and Turbinaria. Lobophyllia and Turbinaria had the highest susceptibility to the disease, with prevalence significantly higher than expected based on their abundance on the reefs. The distribution and prevalence of YBTLD in the GoK was higher than in coral reefs in the Arabian/Persian Gulf. The GoK is an extreme environment for coral reefs with both natural stressors (high salinities, strong, seasonal storm activities, and extreme tides) and anthropogenic pollutants from industrial, mining, agricultural, and domestic activities. These poor environmental conditions may help explain the high occurrence of YBTLD on GoK reefs.

The climate of a place has a decisive role in human adaptations. Man's health, adaptability, behavioural patterns, food, shelter, and clothing are mainly influenced by the temperatures of the area. Hence, a study is undertaken to analyse the spatial distribution, frequency, and trend in the heat waves over the country. The statistical characteristics of heat waves over India are addressed in this study. Gridded daily temperature data sets for the period 1951–2019 were used to compute the arithmetic mean (AM), standard deviation (SD), coefficient of variation (CV), and trends of monthly maximum temperature. The number of heat wave days were identified using the criteria given by India Meteorological Department (IMD) i.e., a heat wave is recognized when the daily normal maximum temperature of a station is less than or equal to (greater than) 40 ℃ than it will be considered as a heat wave if the daily maximum temperature exceeds the daily normal maximum temperature by 5 ℃ (4 ℃). The analysis was confined to the two summer months of April and May only. The spatial distribution of the AM shows higher values during May, and the core hot region with temperatures exceeding 40 ℃ lies over central India extending towards the northwest. The SD distribution shows higher values over the northeast of central India decreasing towards the southwest. The CV distribution shows higher values over the north decreasing toward the south. Higher numbers of heat waves are observed during May and the number is higher over Andhra Pradesh and south Telangana regions of southeast India. This study concludes that a moderate hot region experiences a higher number of heat wave days over India.

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.

Rate of adoption in the present study refers to the acceptance and application by a respondent, some or all the innovations recommended in the package of selected dairy innovations. Rate of diffusion is operationalized as the time taken by the dairy farmer to implement the innovation recommended, to the time form which the farmer is aware of the innovation. A total of 360 dairy farmers, who were first to adopt innovations suggested by the State Animal Husbandry Department from three districts of Andhra Pradesh i.e., Visakhapatnam, Krishna and Chittoor districts were selected purposively for the study. A total of nine dairy innovations were suggested for adoption to the dairy farmers in the study area. Based on adoption quotient computed, the farmers were categorized into low, medium and high adopters and item analysis on adoption of dairy innovations was carried out. Item analysis identified that, out of the nine practices recommended, only five practices i.e., package of practices for rearing heifers, pregnant and lactating animals; feeding of area specific mineral mixture, strategies for enhancement of milk yield and quality, marketing strategies to improve milk sales, use of ICT’s in adoption of innovations were adopted by majority of dairy farmers while remaining four practices i.e., cultivation and feeding of Azolla, hydroponic fodder, use of milking machine and formulation and feeding of complete feed blocks had shown almost low to nil adoption by sizeable section of the respondents. The reasons for adoption of the above mentioned five practices is due to dominant role played by them in influencing production levels, minimum input costs and high economic returns where as the remaining four practices involved high input costs, sound operational skills, low economic returns and non-applicability in all situations which might have been the reasons for their adoption to a meager extent.

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.

The object recognition techniques are popular in computer vision and pattern recognition research field. The present paper focuses on the design of a novel shape signature based on angular information. The Wavelet coefficients are also used to formulate the shape signature. Further, the angular information is captured at two different derivatives of the input image. The angular information is used to estimate the tangential measure for each of the representative point of the input image. The represented shape signature is described with the Fourier transformation. The Fourier descriptors are used for the classification stage. The classification stage uses Euclidean distance measure for the classification. The proposed approach is evaluated on the standard database. The estimated performance measures show the efficiency of the proposed approach.