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Forest fires are caused naturally by lightning, high atmospheric temperatures, and dryness. Forest fires have ramifications for both climatic conditions and anthropogenic ecosystems. According to various research studies, there has been a noticeable increase in the frequency of forest fires in India. Between 1 January and 31 March 2022, the country had 136,604 fire points. They activated an alerting system that indicates the location of a forest fire detected using MODIS sensor data from NASA Aqua and Terra satellite images. However, the satellite passes the country only twice and sends the information to the state forest departments. The early detection of forest fires is crucial, as once they reach a certain level, it is hard to control them. Compared with the satellite monitoring and detection of fire incidents, video-based fire detection on the ground identifies the fire at a faster rate. Hence, an unmanned aerial vehicle equipped with a GPS and a high-resolution camera can acquire quality images referencing the fire location. Further, deep learning frameworks can be applied to efficiently classify forest fires. In this paper, a cheaper UAV with extended MobileNet deep learning capability is proposed to classify forest fires (97.26%) and share the detection of forest fires and the GPS location with the state forest departments for timely action. [ABSTRACT FROM AUTHOR]

Air temperature variability and prediction are important considering the potential implications on human health, water resource management, agriculture and land-atmosphere interaction. In this paper, we investigate the delayed influence of El Niño on spring Surface Air Temperature (SAT) variability over India using observations and assess its predictability in coupled climate models. Analysis suggests that El Niño, at its decaying phase, exerts a strong influence on the spring SAT over Southern Peninsular India (SPI). This warming is mainly induced by an anomalous anticyclone, which extends from the Western North Pacific (WNP) region to the SPI. The anomalous anticyclone-induced downdraft reduces the cloud cover and increases incoming shortwave radiation at surface, which in turn causes a dry atmospheric column and dry soil moisture over this region. As a result of this decreased soil moisture, latent heat flux into atmosphere reduces and enhances sensible heat transfer, further contributing to dry soil moisture. Both dry soil moisture and anomalous anticyclone contributes to enhanced SAT over SPI region. It is found that 6 years out of the top 10 warmest years over the SPI region correspond to the decaying phase of El Niño, suggesting the importance of the present study. It is also found that the intense SAT warming over SPI aids in increasing frequencies of Discomfort Index hours (exceeding 28 and 30 °C in a day) and the Universal Thermal Climate Index during El Niño decaying spring years. Further, we have examined the predictability of the spring SAT during decaying El Niño years based on the Asia-Pacific Economic Cooperation Climate Center (APCC) models. It is noted that most of the APCC models reasonably predicted the spatial distribution of warm SAT over India during the El Niño decaying spring season at 1 and 3 month lead. This study highlights the predictability of spring SAT a season in advance over India, which would help in planning better adaptation and mitigation strategies. [ABSTRACT FROM AUTHOR]

Soil-to-air heat exchanger concept is one of the efficient methods to reduce the cooling effort required for any air conditioning system. In this paper, a theoretical model of soil-to-air heat exchanger has been developed to predict the air temperature in the outlet of the system during a hot atmospheric condition. This theoretical model is used to predict the sub-soil temperature for a specific ambient condition in a southern part of India and also compared with the measured sub-soil temperature. Performance of the soil-to-air heat exchanger system has been calculated during peak hours in summer and found to have the potential to reduce the energy required to cool the building. [ABSTRACT FROM AUTHOR]

In the absence of effective drying techniques, a lot of food gets wasted as there is a lack of post-harvest processes. In India, most of the agricultural produces like paddy, maize, wheat, corn, oil seeds, pulses, chillies, etc. require a temperature range of 50–80 °C for effective drying. Hence, in these conditions, solar drying techniques seem to be the most economical; also, it is safe and eco-friendly. Various types of solar dryers are used across India and worldwide; these are direct solar dryer, green house dryer and indirect solar dryer. Nowadays, indirect type solar dryers are most commonly used because of their several advantages over direct solar dryers. In case of indirect type solar dryers, the products to be dried are kept inside a separate compartment known as drying chamber. Hot air is obtained from the solar collectors either by direct heating method or by using a secondary heating medium and then supplied to the drying chamber for heating of the products. This paper presents a detailed review of various innovative designs of indirect type solar dryers and compares the performance of different types of dryer configuration in terms of collector efficiency, dryer efficiency, drying time and maximum air temperature. Also, the effects of various operating parameters on the thermal performance of such dryers have been discussed. [ABSTRACT FROM AUTHOR]

The paper contributes to the growing literature highlighting the significance of assessing risk and vulnerability, micro-level perceptions and adaptation decision-making in building resilience of farm communities to climate change in dryland region of India. To select a region for grassroots enquiry, spatial differential in risk to climate change was assessed in Rajasthan, using IPCC AR5 framework. Among the highly vulnerable and risky districts, Bikaner district was selected for elicitation of micro-level imperatives. Rising atmospheric temperature, inter-seasonal displacements of rainfall and recurrence of extreme events were perceived by the farmers resulting in resource degradation, production risks and erosion of households' socio-economic dynamics. As risk preventive measures, suitable adjustment in agricultural practices, natural resource management, shift to off-farm activities and other relief measures were adopted by the farmers. Farmer's choice of adaptation was influenced by several climatic, socio-economic and infrastructural & institutional factors in varying degree. Moreover, several financial, economic, infrastructural and informational bottlenecks to adaptations were reported during household survey and FGDs. The results suggest that capturing grassroots evidence is crucial for directing locally tailored adaptation strategies, along with the improving deficiencies in the developmental pathways for climate-resilient agriculture. [ABSTRACT FROM AUTHOR]

This paper presents the analysis of the frequency of lightning strikes associated with thunderstorm and precipitation distributions over smooth oceanic surface relative to that over solid earth surface in the tropical region. Long-term (1998–2014) data retrieved from the Lightning Imaging Sensors (LIS) of the Tropical Rainfall Measuring Mission (TRMM) satellite shows lightning flash counts over Indian landmass found to be 9.1 times more than those over the smooth oceanic surface of the Arabian Sea and the Bay of Bengal. On the other hand, the annual variation of rainfall-to-lightning ratio (RLR) is found to be 0.8 over Indian landmass, whereas it is 10 over the oceanic surfaces. We discuss the convective strength of thunderstorm distributions over land and oceanic regions by examining the relationships of RLR to the Bowen ratio, sea surface temperature (SST), and maximum air temperature over land, maximum updraft speed, and Aerosol Optical Depth (AOD) and cloud ice water content. The RLR shows high positive Pearson's correlations with the Bowen ratio, maximum updraft speed, AOD, and cloud ice water content over land region relative to those measured over the oceanic region. The RLR also shows negative correlations with SST, maximum updraft speed, AOD over the oceanic region. The results are applicable in understanding of the convective characteristics of thunderstorm distributions and lightning flashes over the tropical regions of the world. [ABSTRACT FROM AUTHOR]

Global warming is the average temperature of the earth's surface which has increased over the past century by about 0.6 °C. This rising temperature may vary precipitation patterns, more frequent droughts, rise in sea level and intense storms and can be termed as climate change. To understand uneven precipitation pattern for monsoon dominated region like India, there is a need to study cloud processes at high resolution with the help of cloud microphysical properties. Ocean is the major and primary source of cloud whereas local water bodies and re-evaporated water over land could be secondary source. Paper focuses over ocean (Arabian Sea) as well as over land (central India) to know the effect of global warming on cloud microphysical properties such as cloud effective radius and cloud liquid water content. Warming signal in terms of rise in sea surface temperature (0.1 °C/decade) as well as rise in surface air temperature (0.05 °C/decade) are observed over Arabian Sea and central India respectively. Satellite data show an increasing (0.5 µm/decade) trend in cloud effective radius over Arabian Sea, whereas it decreases (− 0.1 µm/decade) over central India. Increasing trend in temperature and cloud properties is may be due to warming signal. Aerosol concentration over ocean and land further helped to understand cloud processes with cloud microphysical properties. Paper will focus on the effect of warming signal in cloud properties over Arabian Sea and central India. This type of high resolution study may help to understand cloud processes which in turn may help to understand precipitation patterns. [ABSTRACT FROM AUTHOR]

A case‐crossover analysis is used as a simple but powerful tool for estimating the effect of short‐term environmental factors such as extreme temperatures or poor air quality on mortality. The environment on the day of each death is compared to the one or more "control days" in previous weeks, and higher levels of exposure on death days than control days provide evidence of an effect. Current state‐of‐the‐art methodology and software (integrated nested Laplace approximation [INLA]) cannot be used to fit the most flexible case‐crossover models to large datasets, because the likelihood for case‐crossover models cannot be expressed in a manner compatible with this methodology. In this paper, we develop a flexible and scalable modeling framework for case‐crossover models with linear and semiparametric effects which retains the flexibility and computational advantages of INLA. We apply our method to quantify nonlinear associations between mortality and extreme temperatures in India. An R package implementing our methods is publicly available. [ABSTRACT FROM AUTHOR]

In this paper, the performance of a PVT (photovoltaic thermal) air collector with V-groove absorber in the air channel is theoretically and experimentally examined under different climate conditions of Ghaziabad city, India, in summer, June 2019. A low power brushless DC fan is fixed at the input of the air-channel to ensure the circulation of air through the V-groove. The energy balance equation is developed with the matrix-inversion method to estimate the PV (photovoltaic) cell and air output temperatures. The theoretical results obtained using mathematical modeling has been compared with that of the real-time experimental results. The electrical, thermal, and overall efficiencies for the theoretical and experimental studies are found in the range of 10.39–10.26%, 41.78–41.57%, and 52.17–51.81%, respectively. It has been observed that the theoretical results are coherent with experimental results for the proposed model achieving an accuracy of 98.98%, 99.43%, and 99.31% for electrical, thermal, and overall efficiencies, respectively. [ABSTRACT FROM AUTHOR]

To investigate variability patterns, to predict short-term and long-term changes of weather, time-series data analysis is considered to be a valuable tool. The paper presents modelling and forecasting the seasonal surface air temperature patterns in India for the period 1951–2016 using the structural time-series modelling. The structural time-series model (STSM) with the hidden components of deterministic linear time trend, trigonometric seasonal, and stochastic autoregression for cycle is selected from the parsimonious models. The model selection can be done based on Bayesian Information Criteria (BIC), significant tests, and statistical fit. The model parameters of the noise terms and the damping coefficient in the autoregression are determined using maximizing the likelihood function. The diagnostics of the selected STSM is determined with normal diagnostics checked by examining the histogram and Q–Q plot of residuals; the whiteness checked by autocorrelation function (ACF), partial autocorrelation function (PACF), and p values of LJung–Box portmanteau test of residuals. Furthermore, the forecast of seasonal surface air temperature patterns in India during the years 2017–2019 has been forecasted from the selected STSM. Statistically significant increase is noticed in annual average surface air temperature over India. The increase in surface temperature is about 0.0132 °C per year for the period 1951–2016. The study also showed slow and steady increase in the mean surface air temperature over India during the recent years. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Monsoon rainfall prediction over a small geographic region is indeed a challenging task. This paper uses monthly means of climate variables, namely air temperature (AT), sea surface temperature (SST), and sea level pressure (SLP) over the globe, to predict monthly and seasonal summer monsoon rainfall over the state of Maharashtra, India. Mutual information correlates the temperature and pressure from a grid of 10 longitude X 10latitude with Maharashtra's monthly rainfall time series. Based on the correlations, selected features over the respective latitude and longitudes are given as inputs to an artificial neural network. It was observed that AT and SLP could predict monthly monsoon rainfall with excellent accuracy. The performance of the test dataset was evaluated through mean absolute error; root mean square error, correlation coefficient, Nash Sutcliffe model efficiency coefficient, and maximum rainfall prediction capability of the network. The individual climate variable model for AT performed better in all evaluation parameters except maximum rainfall capability, where the combined model 2 with AT, SLP and SST as predictors outperformed. The SLP-only model's performance was comparable to the AT-only model. The combined model 1 with AT and SLP as predictors was found better than the combined model 2. [ABSTRACT FROM AUTHOR]

The demand for solar photovoltaic (SPV) systems is growing all over the world due to the continuous increase in the cost of conventional means of power. India has the advantage of around 300 clear sunny days in a year. However, the biggest problem is conversion efficiency. In this paper, an attempt has been made for evaluating the second law efficiency of 160 Wp stand-alone SPV systems installed in RKGIT Ghaziabad premises. The performance of a SPV system depends on climate conditions like temperature, air velocity and a number of sunny days. The solar energy striking the solar panels gets converted into electric and thermal energy. In this work, we have considered the effect of ambient temperature and air velocity on the efficiency of solar panels. The average second law efficiency of SPV systems was found to be 10.7%, whereas maximum second law efficiency was 13.8% at 9am on the same day. The efficiency of the SPV system can be improved by maintaining the temperature of the module. [ABSTRACT FROM AUTHOR]

This study investigates the incidence of heat wave and cold wave condition during 2000 – 2022 in the Bathinda district of South-Western region of Punjab. Notable spikes in heat wave (HW) activity were observed in 2002 and 2022 with 29 and 27 days respectively. Similarly, for severe heat waves (SHW), 2010 and 2022 witnessed the highest frequencies recording 16 and 18 days respectively. Conversely, cold wave (CW) events peaked in 2005 and 2008 with 10 and 11 days respectively. Notably, 2008 also observed the highest frequency of severe cold wave (SCW) days with 15 days. However, results revealed decline in cold wave days towards the latter years, while severe cold wave days also exhibited decreasing frequencies like 2015 and 2016 recorded zero CW and SCW days. One key finding highlights a substantial correlation between land surface temperature (LST) and maximum air temperature during heat wave periods (R² = 0.83), indicating LST’s efficacy as an indicator for monitoring temperature trends during heat wave events. [ABSTRACT FROM AUTHOR]

Understanding the trend of seasonal rainfall in the context of climate change is crucial for the maintenance of regional water resources management. The present study examines the seasonal rainfall trend in the Mahi River basin, India by using the wavelet transform and clustering method. Daily gridded rainfall data (0.25° × 0.25° spatial resolution) for 51 grids from 1970 to 2020 have been taken from India Meteorological Department (IMD). The monthly, seasonal, and annual statistic has been analyzed for 51 years. We have also plotted the relationship between precipitation trend–elevation. After that, homogeneous precipitation regions are delineated with hierarchical clustering analysis. Results reveal that seasonal precipitation over the basin clusters into 4 subregions for monsoon and 3 subregions for winter, pre-monsoon, and post-monsoon seasons. After the regionalization of the subregions, the periodicity and the inter-seasonal relationship were analyzed using continuous wavelet transform (CWT). In addition, it was clear that the cross-correlation between pre-monsoon and monsoon seasons had a significant periodic change in the last 30 years over the basin. The basin is located adjacent to the Arabian sea, which makes the basin more sensitive to a natural event like ENSO, it can cause changes in ocean temperatures and atmospheric circulation patterns that affect precipitation patterns during the study period. It is expected that some natural phenomena like monsoon variability, low-level jet streams, and many more also have an impact on the rainfall patterns of the basin. Thus, the understanding of seasonal precipitation variation provides a practical reference for water resources management, agricultural planning, and a forecast of precipitation in different regions and river basins of India which may give a better climate change indication. [ABSTRACT FROM AUTHOR]

This paper presents the experimental investigation of an earthto-air heat exchanger for heating purposes in the Patna region of India, using an experimental test rig. In the view of the author, real field experiments have several limitations such as lack of repeatability and uncontrolled conditions. It also takes more time for the response of parameters that depends on nature and climate. Moreover, earth-to-air heat exchangers may be expensive to fabricate and require more land area. Thus, in this work authors executed their experimental work in indoor controllable environments to investigate the thermal performance of an earth-to-air heat exchanger. The actual soil conditions were created and maintained the temperature at 26°C throughout the soil in the vicinity of pipes. Three horizontal PVC pipes of equal lengths and diameters of 0.0285 m, 0.038 m and 0.0485 m were installed in the test rig. The experiments were performed for different inlet air velocities at ambient air temperature. This study acknowledges that the maximum rise in outlet temperature occurs at a lower speed for smaller pipes. Also, the maximum effectiveness of 0.83 was observed at 2 m/s for the smallest diameter pipe. [ABSTRACT FROM AUTHOR]

Solar energy which is incident on the earth surface is mostly in diffused form. Hence, ordinary solar water heating technology is able to supply only low-temperature hot water (40–70 °C), primarily used for residential water and space heating. This range of temperature can hardly meet specific energy demand such as food processes, dairy processes and other industrial process heat requirement. Hence to utilize this diffused form of solar energy efficiently for heating in domestic or industrial applications, solar concentrators have to be used for generating temperatures in the range of 80–250 °C. The medium temperature applications can vary from domestic like cooking, boiling or dairy processes etc. to industrial like heat treatment, sterilizing, dyeing etc. This paper illustrates design and development of various concentrating solar thermal technologies along with its application in Indian market. The paper briefly reviews the need of such technologies; potential markets; and barriers and recommendations for deployment of available concentrated solar thermal technologies used for generation of medium temperature (80–250 °C). The paper focuses on the cases of successful implementation of the technology all over the country and also the government efforts to promote its use further for making it a cheaper and an alternative source of energy to fossil fuels for heating applications. [ABSTRACT FROM AUTHOR]

Studying atmospheric temperature characteristics is crucial under climate change, as it helps us to understand the changing patterns in temperature that have significant implications for the environment, ecosystems, and human well-being. This study presents the comprehensive analysis of the spatiotemporal variability of scaling behavior of daily temperature series across the whole Indian mainland, using a Multifractal Detrended Fluctuation Analysis (MFDFA). The analysis considered 1° × 1° datasets of maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmean), and diurnal temperature range (DTR) (TDTR = Tmax − Tmin) from 1951 to 2016 to compare their scaling behavior for the first time. Our results indicate that the Tmin series exhibits the highest persistence (with the Hurst exponent ranging from 0.849 to unity, and a mean of 0.971), and all four-temperature series display long-term persistence and multifractal characteristics. The variability of the multifractal characteristics is less significant in North–Central India, while it is highest along the western coast of India. Moreover, the assessment of multifractal characteristics of different temperature series during the pre- and post-1976–1977 period of the Pacific climate shift reveals a notable decrease in multifractal strength and persistence in the post-1976–1977 series across all regions. Moreover, for the detection of climate change and its dominant driver, we propose a new rolling window multifractal (RWM) framework by evaluating the temporal evolution of the spectral exponents and the Hurst exponent. This study successfully captured the regime shifts during the periods of 1976–1977 and 1997–1998. Interestingly, the earlier climatic shift primarily mitigated the persistence of the Tmax series, whereas the latter shift significantly influenced the persistence of the Tmean series in the majority of temperature-homogeneous regions in India. [ABSTRACT FROM AUTHOR]

Drought is one of the most damaging natural disasters that affect agriculture, water resources, economy, and socioeconomic conditions. Considering the role of temperature on drought under the warming climate is important to employ atmospheric water demands (PET) in the drought estimation. Moreover, the consideration of HT ht concentration on stomatal resistance is also essential in PET estimation ([7]; [18]). Therefore, PET estimations based on PET-TH can be erroneous and lead to an overestimation of drought frequency and risk of hazard under the warming climate ([2]; [5]; [14]). [Extracted from the article]

Ground-based multi-frequency microwave radiometer Profiler (Microwave Radiometer Profiler-MRP) provides valuable information on the altitude distribution of atmospheric humidity and temperature in the troposphere (upto ∼ 10 km) with high temporal resolution (of ∼ 1 min) under all-weather condition. These information are vital inputs for characterization of the atmospheric boundary layer, convective cloud systems, local weather modification and for studying the atmospheric dynamics. Potential of such radiometers are well established over the mid-latitude region. Thiruvananthapuram, located in the Indian peninsular region, is one of the first stations in the equatorial region where such an instrument made continuously operational for seven years since 2010 at the tropical coastal station. The instrument measures the brightness temperatures (T b in Kelvin) at Ka- and V-band frequencies with a temporal resolution of ∼ 1 min. A trained back-propagation neural network technique based on the radiosonde ascends data for more than one decade obtained by India Meteorological station at Thiruvananthapuram, was used to derive the atmospheric humidity and temperature profiles. This paper presents the implementation of another retrieval technique based on deep learning approach - batch normalization and neural network (BNN) to retrieve temperature and water vapour density (WVD) profiles from T b values during clear sky conditions and validation of the retrieval accuracies. This inversion model, much faster in computation, consists of two hidden layers and the rectified linear unit (ReLU) is used as the activation function as it can overcome the problems of saturation and vanishing gradients. The T b s observed by radiometer are corrected by reducing the bias between the simulated T b , using forward radiative transfer model, and the observed T b. The validation of retrieved profiles with the radiosonde demonstrates a good retrieval capability, showing a root-mean-square error of 1.8 K for temperature, < 2 g/m 3 for WVD and ∼ 14 % for RH. • The bias between simulated and observed brightness temperature has been estimated. • Neural network with two hidden layers and ReLU activation layer has been used.. • Retrieved profiles are validated using radiosonde data and exhibit improved accuracy. [ABSTRACT FROM AUTHOR]

This short communication reports suspected technical errors in a widely–referred paper by Arvind Tiwari et al. [1], on the subject of performance of a photovoltaic thermal solar air collector. The suspected errors begin at the equations for calculating the temperature of the air and the temperature of the outlet air. These errors may have a significant effect on the collector efficiency. The corrected equations are suggested. • The paper comments on "Performance evaluation of photovoltaic thermal solar air collector for composite climate of India". • Suspected technical errors were found that will affect the obtained results. • The corrected analysis was given. [ABSTRACT FROM AUTHOR]

The study examined the dynamic impact of climate such as Carbon dioxide (CO2) emissions, temperature, and rainfall on agricultural yield in India from 1980 to 2017 by applying the nonlinear autoregressive distributed lag (NARDL) model. The empirical evidence supports a long- and short-run asymmetry effect, implying that the positive impact of the climatic variables on agricultural yield is distinct from the adverse effects on both the long- and short-run equilibrium. In addition, CO2 emissions and average atmospheric temperature have diverse impacts on agriculture yield. [ABSTRACT FROM AUTHOR]

The present study tests the accuracy of four models in estimating the hourly air temperatures in different agroecological regions of the country during two major crop seasons, kharif and rabi, by taking daily maximum and minimum temperatures as input. These methods that are being used in different crop growth simulation models were selected from the literature. To adjust the biases of estimated hourly temperature, three bias correction methods (Linear regression, Linear scaling and Quantile mapping) were used. When compared with the observed data, the estimated hourly temperature, after bias correction, is reasonably close to the observed during both kharif and rabi seasons. The bias-corrected Soygro model exhibited its good performance at 14 locations, followed by the WAVE model and Temperature models at 8 and 6 locations, respectively during the kharif season. In the case of rabi season, the bias-corrected Temperature model appears to be accurate at more locations (21), followed by WAVE and Soygro models at 4 and 2 locations, respectively. The pooled data analysis showed the least error between estimated (uncorrected and bias-corrected) and observed hourly temperature from 04 to 08 h during kharif season while it was 03 to 08 h during the rabi season. The results of the present study indicated that Soygro and Temperature models estimated hourly temperature with better accuracy at a majority of the locations situated in the agroecological regions representing different climates and soil types. Though the WAVE model worked well at some of the locations, estimation by the PL model was not up to the mark in both kharif and rabi seasons. Hence, Soygro and Temperature models can be used to estimate hourly temperature data during both kharif and rabi seasons, after the bias correction by the Linear Regression method. We believe that the application of the study would facilitate the usage of hourly temperature data instead of daily data which in turn improves the precision in predicting phenological events and bud dormancy breaks, chilling hour requirement etc. [ABSTRACT FROM AUTHOR]

The operational medium-range weather forecasting based on numerical weather prediction (NWP) models are complemented by the forecast products based on ensemble prediction systems (EPSs). This change has been recognised as an essentially useful tool for medium-range forecasting and is now finding its place in forecasting the extreme events. Here we investigate extreme events (heatwaves) using a high-resolution NWP model and its ensemble models in union with the classical statistical scores to serve verification purposes. With the advent of climate-change-related studies in the recent past, the rising number of extreme events and their plausible socio-economic effects have encouraged the need for forecasting and verification of extremes. Applying the traditional verification scores and associated methods to both the deterministic and the ensemble forecast, we attempted to examine the performance of the ensemblebased approach in comparison to the traditional deterministic method. The results indicate an appreciable competence of the ensemble forecast at detecting extreme events compared to the deterministic forecast. Locations of the events are also better captured by the ensemble forecast. Further, it is found that the EPS smoothes down the unexpectedly increasing signals, thereby reducing the false alarms and thus proving to be more reliable than the deterministic forecast. [ABSTRACT FROM AUTHOR]

Water quality parameters were analysed in complex aquatic ecosystem of two major harbour waters (Ennore Fish Landing Centre and Royapuram Fishing Harbour), along the Chennai district. Monthly variations in water physicochemical parameters such as atmospheric temperature (26–34°C), surface water temperature (23.2–33.5°C), pH (7.3–8.12), salinity (28.0–33.5 psu) and dissolved oxygen (3.2–5.6 mg/L) were analysed in the selected harbours. The inorganic nutrients such as nitrate (0.042–0.18 mg/L), nitrite (0.070–0.096 mg/L) phosphate (0.078–0.197 mg/L), silicate (0.480–0.790 mg/L) and ammonia (0.040–0.158 mg/L) were estimated in selected study site. The water quality parameters have obtained important seasonal and spatial variations. The increased harbour activities in both the selected harbour have been made the noticeable water quality changes. The obtained results evidently insisting the necessity of continuous monitoring of harbour waters. The present work was contributed in the harbour water quality monitoring and impact assessment for further policy mitigation measures. [ABSTRACT FROM AUTHOR]

Atmospheric ozone (O3) in the surface level plays a central role in determining air quality and atmospheric oxidizing capacity. In this paper, we review our comprehensive results of simultaneous measurements of surface ozone (O3) and its precursor gas (NOx) and weather parameters that were carried out continuously for a span of six years (January 2013–December 2018) at a typical rural coastal site, Kannur (11.9° N, 75.4° E) in South India. Surface O3 concentration reached its maximum during daytime hours and minimum during the night time. The influence of solar radiation and water content on variations of O3 are discussed. A Multi-Layer Perceptron (MLP) artificial neural network technique has been used to understand the effect of atmospheric temperature on the increase in O3 over the past six years. This has been found that temperature has been a major contributor to the increase in O3 levels over the years. The National Centre for Atmospheric Research- Master Mechanism (NCAR-MM) Photochemical box model study was conducted to validate the variations of O3 in different seasons and years, and the results were shown to be in good agreement with observed trends. [ABSTRACT FROM AUTHOR]

A high surface air temperature (SAT) record over Northwestern India was reported in April 2022. This study examines the contribution of interannual variability on Indian SAT and possible reasons for the extreme SAT during April 2022. Result shows that the interannual variability of SAT is captured by the first two leading modes using the empirical orthogonal function (EOF) analysis. Both of them show highest values over the Northwestern India and are simultaneous energetic during 2022. The EOF1 is related to the Indian Ocean Basin Mode (IOBM) and an anomalous anti-cyclone in the troposphere, while the Rossby wave train from the North Atlantic to the north of India controls the EOF2. The active IOBM and strong Rossby wave source account for the extreme SAT over northwestern India during April 2022. In addition, the Indian regional mean SAT during April is well represented by the indices of IOBM and tropospheric anomalous anti-cyclone, which can help to improve the prediction of SAT over India. [ABSTRACT FROM AUTHOR]

Tropical forests sequester six times higher carbon than that released by humans annually into the atmosphere. These biodiversity-rich tropical forests have high net primary productivity (NPP), which differs among constituent plant communities. Tropical moist deciduous forests occupy 179,335 km2 of India's geographical area and constitute 44% of the country's total protected area (PA) forests. The productivity of these forests has neither been estimated specifically nor precisely. We measured the annual NPP of three predominant distinct community types, viz., mixed (DM), sal (SM), and teak (TP), in a tropical moist deciduous forest in northern India. The NPP was estimated from tree biomass data collected from nine long-term ecological research (LTER) plots of 1 ha each representing the above three community types. The estimated annual NPP were 10.28, 6.25, and 9.79 Mg ha−1 year−1 in DM; 8.93, 7.09, and 10.59 Mg ha−1 year−1 in SM; and 14.57, 7.14, and 13.56 Mg ha−1 year−1 in TP for the years 2010, 2011, and 2012, respectively. The NPP was correlated with tree density, height and DBH, species richness, diversity, microclimatic and edaphic variables, and leaf area index (LAI) using principal component analysis (PCA) and generalized linear modeling (GLM). Air temperature and humidity were strongly related to NPP in all the community types, while "complementarity" and "selection effects" contributed to the NPP in both the sal and mixed forest communities with equal importance, and the NPP in teak plantation ould point to "dominance effect." [ABSTRACT FROM AUTHOR]

The present work is to describe the electrochemical-thermal profiles of a Li-ion cell of a battery pack for an electric vehicle application considering a realistic drive-cycle. For this investigation, a tropical country like India is considered. The atmospheric temperature varies at different locations of India, and hence, a range of temperatures (25–55°C) is taken into consideration for the study. An electrochemical-thermal model featuring physics laws has been considered as it can provide all the electrochemical kinetics and side reactions to get more accurate results. The two-way coupled electrochemical-thermal model consisting of a 1-dimensional electrochemical model for heat generation and a 2D thermal model to show temperature distribution over the cell is developed. Thermal contact resistance is modeled which is formed in between the battery electrode coatings and current collectors due to imperfect contact at the time of electrode fabrication. This model is first tested with standard charge–discharge profiles at different C-rates, and thereafter, it is validated with the experimental results. Finally, the model is tested with a standard drive-cycle called modified Indian driving cycle, and the results are analyzed. Comments are made on the state of charge (SoC) of Li-ion cell as well as SoC of electrodes. The temperature profiles of the cell show a range of ambient temperatures, and results are reported. [ABSTRACT FROM AUTHOR]

The previous studies on temperature trends are based on temperatures observed at a fixed hour (maximum and minimum temperatures). In this study, trends in sub-daily (hourly) temperatures are examined using quality temperature data from 66 Indian stations spread over the country. The season April–May–June and the data period of 1975–2012 have been considered for this analysis. Analysis was made to see the differences in two phases, 1975–1993 and 1994–2012. Most parts of the country were warmer throughout the day during the period 1994–2012 compared to the earlier period 1975–1993. Increase in daytime temperatures over many stations in Central India is subdued due to the cooling effect caused by the increase in aerosol content. Turbidity has shown a significant increasing trend over central Indian station, and it is 0.06 per decade. A statistically significant (at the 5% significant level) warming trend in diurnal temperature is observed during night/early morning hours. Warm hours showed an increasing trend in all four homogenous regions with a significant increase over northwest India ~ 1.5 h per decade and northeast India ~ 2 h per decade. The increase in extreme temperature hours may lead to an increase in the duration of heat wave events in the future. [ABSTRACT FROM AUTHOR]

The changing climate poses significant stress on the yield of wheat, which is a major grain crop in Gangetic Plain and therefore on the food security of this densely populated region. Here, we aim to assess the effects of different climate parameters on wheat yield in the last four decades. The redundancy analysis (RDA) shows that the climatic factors could explain up to 35% of the variations in the wheat yield. The negative correlation with precipitation, Palmer Drought Severity Index (PDSI) and Standardized Precipitation Evapotranspiration Index (SPEI) in winter months might be associated with extreme wetting that delays the yield during the seeding and late growth stages. We recorded a positive and statistically significant correlation between wheat yield and previous year winter mean temperature. The analysis shows that the increase in temperature during the seedling and the late growth stage may result in maximum yield. The linear relationship with the yield in the study area is statistically significant with temperature rise during 1971–2011. The yield of wheat shows a significant positive relationship with both air temperature and sea surface temperature (SST) during winter months in and around India and over the seas regulating winter climate. This indicates that winter temperature may have a direct role in modulating the yield of wheat in the Gangetic Plain demanding further implications of temperature rise on wheat production in future. [ABSTRACT FROM AUTHOR]

Eastward-moving upper air troughs in the subtropical westerlies, commonly known as the western disturbances (WDs) in the Asian subcontinent, are primary sources of precipitation over the north-west Himalayan (NWH) region and the northern plains of India during winter. Many simulation case studies with the help of numerical weather prediction models and a few observational case studies have been conducted to understand the spatial structure, dynamics, energy and weather associated with the WDs over the NWH in the past. However, studies using in situ observations on the impacts of the approaching WDs on various surface meteorological variables at a local scale in the high-altitude mountainous regions of the NWH are lacking. The objectives of this study are to examine the impacts of the approaching WDs on various surface meteorological variables for 11 stations in the NWH and the associated precipitation amount in a 24-hr time interval. Changes (departures) in the values of various meteorological variables are examined on the first precipitation day of the occurrence of active WDs to study the impacts of the approaching WDs and the associated precipitation amount in the 24-hr time interval which are found to depend on the altitude and geographic location of a station. The mean drop in the maximum and ambient air temperatures are found to be 2.0 ∘ and 0.7 ∘ C , respectively, while the mean rise in the minimum air temperature was found to be 0.8 ∘ C . A mean drop in the surface atmospheric pressure and a mean increase in the relative humidity are found to be 0.9 hPa and 19.5%, respectively, in a 24-hr time interval. The mean precipitation amount and mean maximum precipitation amount associated with the active WDs in the 24-hr time interval are found to be 8.9 and 68.8 mm, respectively. The results are briefly discussed in the paper. The findings of this study can be useful for operational weather forecasting and a selection of precursor variables for developing a real-time local scale weather forecast model(s) for remote areas of the NWH for the winter season. [ABSTRACT FROM AUTHOR]

The spatially widely distributed and temporally protracted Santonian–Campanian stratigraphic records of the Sillakkudi Formation of the Ariyalur Group, Cauvery Basin, southern India represent an onland exposure of a Gilbert‐type delta. Here, we present geochemical, mineralogical, and petrographic analyses of two composite stratigraphic sections, and evaluate the climatic, tectonic, and other factors that contributed to the spatio‐emporal uniqueness of this formation. A range of low to intensive weathering, simultaneous exhumation/erosion, physical transport, and chemical weathering, prevalent at different parts of the source region and or the existence of multiple channels that drained varied source regions and hence varied lithologies at exhumation surfaces which in turn experienced seasonally varied flow conditions were interpreted. The prevalence of physical erosion during the initial stages of the deposition of the Sillakkudi Formation that progressed towards higher intensities of chemical weathering, in tune with the increase in atmospheric temperature and sea‐level rise during the later stages of the formation, is evidenced. Occurrences of positive and negative Eu anomalies and a progression from active to passive tectonic setting of samples, concomitant with change from tectonic control to relative sea‐level and the sediment influx controlled nature of deposition from Santonian–Campanian is recorded. Together, three sets of related factors namely, tectonics, increase of atmospheric temperature and sea‐level rise operated independently, and the enforced transience of transformation of the source terrain from active to passive margin, the transformation of depositional setting from fluvial to estuarine/intertidal to open marine, and shifting of principal loci of deposition from onland to offshore, are documented. The causal link between the progressive increase of sea‐level control over the depositional system during the Santonian–Campanian as recorded by this study could be due to the rise in atmospheric temperature that in turn might have been influenced by the movement of the Indian Plate from low latitudes towards the equatorial region. The second implication of the results is that the studies attempting to document tectonic setting, provenance, and weathering, with the help of geochemical discriminant diagrams, should also consider the spatial and temporal changes in the source area as well as depositional setting. [ABSTRACT FROM AUTHOR]

Temperature perturbations from the total solar eclipse of January 15, 2010, were monitored from surface to stratosphere (30 km) over Kadapa (14.28° N, 78.42° E), India, a semi-arid region. During the maximum solar eclipse, the entire upper tropospheric and lower stratospheric (UTLS) region reflected the cooling effect. On the eclipse day, the surface temperature showed 2 °C cooling compared to the control day, though surface pressure did not vary. During the eclipse peak, temperature above 30 km altitude decreased by 10 °C compared to the control days. Two inversions in temperature profiles, first around 1 km, near the atmospheric boundary layer, and second near 13 km altitude, were noted after eclipse onset. The diurnal variations in the tropopause temperature, height, and width of the tropical tropopause layer (TTL) were also examined on eclipse and control days. At the time of maximum obscuration on the eclipse day, cold point tropopause (CPT) temperature decreased by ~ 2 °C, and its height decreased by ~ 1.1 km. However, the lower boundary of the TTL did not reflect any significant variations in response to the eclipse. In addition, wave-like oscillations were noted in the UTLS region during the eclipse period. A wave-like pattern with a wavelength of 4–5 km was seen to occur in the stratospheric region (17–22 km) during the maximum eclipse phase. At the end of the eclipse period, the wave pattern speared in the troposphere also (up to ~ 12 km) with a reduced wavelength of 2–3 km. [ABSTRACT FROM AUTHOR]

India is a developing country with a huge population which means our need is only going to be increasing. The residential floor area is going to be doubled and the energy consumption tripled. Around 34% of the energy in the housing sector is consumed just for heating, cooling, and ventilation of the building. The increase in global warming is causing a drastic change in the environment and the atmospheric temperature. This is causing an increase in the demand for AC and heaters. This brings the question of what should be done. One way out is to increase the thermal efficiency of the building envelope. When the thermal efficiency is increased the temperature outside will not affect the inside temperature to a large extent. It also improves the lighting and ventilation of the building. India never had a code for the building envelope until 2018 Eco Niwas Samitha 2018 was launched to maximize the RETV value for a building. So, this project would also like to increase its awareness in society and also develop different methods to include it during the planning and construction of a building. [ABSTRACT FROM AUTHOR]

The signal of temperature change has emerged from background variations in most tropical regions in boreal summer over decadal‐centennial timescales but not in northern‐central India (NCI). In this study, we investigated the reason for the limited temperature change in NCI. We found that internal variability, largely caused by the Interdecadal Pacific Oscillation (IPO) on a ∼20‐year timescale, has the potential to mask the temperature change signal. Besides, local response to external forcing, linked to non‐greenhouse gas (GHG) forcings, strongly overrides GHG warming in NCI, which results in little trend in the temporal evolution of external variability. The internal variability related to IPO and the limited warming arising from the competition between multiple forcings result in the smallest signal‐to‐noise ratio and thus, the temperature change signal fails to emerge from the background variations. Plain Language Summary: Earth's surface has warmed considerably since 1850s, which can be largely attributed to anthropogenic greenhouse gas emissions. When human activities (e.g., greenhouse gas emissions) cause the local temperature change to exceed the inherent variation of the climate system, the region will face more extreme climate events. Although temperature has experienced warming almost all around the globe, here we show that surface air temperature in boreal summer in northern‐central India has not gone through significant changes. The timing of transition is important for policymakers. Thus, we analyzed the reasons for this and show that it reflects the combined effects of natural processes and local anthropogenic forcings. Key Points: The external forcing exhibits a cooling trend in northern‐central India (NCI), whereas most global regions have warmed considerablyCooling effects of non‐greenhouse gas forcings that override global warming lead to a cooling trend in NCIThe internal variability related to the Interdecadal Pacific Oscillation and the limited warming cause the smallest signal‐to‐noise ratio [ABSTRACT FROM AUTHOR]

Decadal climate prediction has been recognized as the important information for policy makers for agriculture, health and energy sectors and the general public for any near‐term planning. The modelling community is determined to put forward a reliable near‐term climate forecasting system, to predict annual to decadal state and variability of climate. However, deriving reliable information from the decadal prediction/hindcast is still a challenge. This study examines the decadal hindcast simulations of surface air temperature (SAT) over India in seven different ocean–atmosphere coupled models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Each decadal hindcast is available for the next 10‐years period from the initialized climate states of 1961–2006. The performance of models is assessed using different evaluation metrics, such as absolute mean difference, root mean square error, skill score and uncertainty in terms of the range of hindcasts. The multimodel ensemble mean displayed considerable skill in representing the spatial distribution of SAT over the Indian region except over the western Himalaya and Northeast India. Our results indicate that the decadal hindcasts skills improved noticeably when quantile mapping (QM) approach is used for the bias corrections. The major improvements are seen in terms of reducing absolute mean difference and uncertainty, regardless of lead time and region. The present study advocates that QM approach is useful not only for reducing bias but also for improving the decadal hindcast skill for SAT over India in the coupled models. [ABSTRACT FROM AUTHOR]

In warm and hot climates, ceiling fans and/or air conditioners (ACs) are used to maintain thermal comfort. Ceiling fans provide air movement near the skin, which enhances the evaporation of sweat, reduces heat stress, and enhances thermal comfort. This is also called the cooling effect. However, AC usage behaviour and the effects of elevated air speed through the use of ceiling fans on indoor operative temperature during AC usage are not widely studied. This study investigated the optimum AC (cooling) set point temperature and air velocity necessary for maintaining thermal comfort while achieving energy conservation, in mixed-mode buildings in India, through field studies by using used custom-built Internet of Things (IOT) devices. In the current study, the results indicate a 79% probability that comfort conditions can be maintained by achieving a temperature drop of 3K. If this drop can be achieved, as much as possible, through passive measures, the duration of AC operation and its energy consumption are reduced, at least by 67.5 and 58.4%, respectively. During the air-conditioned period, there is a possibility that the cooing effect is reduced because of increase in operative temperature due to ceiling fan operation. Therefore, the optimum solution is to maintain the highest AC set point and minimum fan speed setting that are acceptable. [ABSTRACT FROM AUTHOR]

The atmospheric methane (CH4) concentration has increased in recent years due to natural and anthropogenic causes. Hence, it is essential to quantify the potential sources of CH4 to understand the factors responsible for its fluxes on a local to regional scale through in situ observations. Coastal wetlands, particularly the mangrove ecosystems in the tropical and subtropical coasts, are significant sources of CH4. In this study, we used an eddy covariance-based technique to measure the CH4 fluxes in a mangrove ecosystem in Pichavaram, South India. The daily mean CH4 flux ranged from 12 to 26 nmol m−2 s−1 during the wet season and from 6 to 20 nmol m–2 s–1 during the dry season. The monthly mean flux during the wet period was between 0.8 and 1.8 g CH4 m−2 month−1, and in the dry season, it was between 0.4 and 0.6 g CH4 m−2 month−1. The visual correlogram and structural equation modelling technique revealed that air temperature, creek water dissolved oxygen, soil organic carbon, and redox potential are important factors that control the CH4 fluxes. The results suggest that the Pichavaram mangrove wetland acts as a source for CH4. Our results also indicate that tidal inundation and seasonal variations in atmospheric temperature and water salinity are key factors affecting the CH4 flux in the Pichavaram mangrove ecosystem. [ABSTRACT FROM AUTHOR]

Northern India is a densely populated subtropical region with heavy aerosol loading (mean aerosol optical depth or AOD is ∼0.7), frequent heat waves, and strong atmosphere–biosphere coupling, making it ideal for studying the impacts of aerosols and the temperature variation in latent heat flux (LH) and evaporative fraction (EF). Here, using in situ observations during the onset of the summer monsoon over a semi-natural grassland site in this region, we confirm that strong co-variability exists among aerosols, LH, air temperature (Tair), and the vapor pressure deficit (VPD). Since the surface evapotranspiration is strongly controlled by both physical (available energy and moisture demand) and physiological (canopy and aerodynamic resistance) factors, we separately analyze our data for different combinations of aerosols and Tair /VPD changes. We find that aerosol loading and warmer conditions both reduce sensible heat (SH). Furthermore, we find that an increase in atmospheric VPD tends to decrease the gross primary production (GPP) and, thus, LH, most likely as a response to stomatal closure of the dominant grasses at this location. In contrast, under heavy aerosol loading, LH is enhanced partly due to the physiological control exerted by the diffuse radiation fertilization effect (thus increasing EF). Moreover, LH and EF increases with aerosol loading even under heat wave conditions, indicating a decoupling of the plant's response to the VPD enhancement (stomatal closure) in the presence of high aerosol conditions. Our results encourage detailed in situ experiments and mechanistic modeling of AOD–VPD–EF coupling for a better understanding of Indian monsoon dynamics and crop vulnerability in a heat stressed and heavily polluted future India. [ABSTRACT FROM AUTHOR]

Despite the development of sophisticated statistical and dynamical climate models, a relative long-term and reliable prediction of the Indian summer monsoon rainfall (ISMR) has remained a challenging problem. Toward achieving this goal, here we construct a series of dynamical and physical climate networks based on the global near-surface air temperature field. We show that some characteristics of the directed and weighted climate networks can serve as efficient long-term predictors for ISMR forecasting. The developed prediction method produces a forecasting skill of 0.54 (Pearson correlation) with a 5-month lead time by using the previous calendar year's data. The skill of our ISMR forecast is better than that of operational forecasts models, which have, however, quite a short lead time. We discuss the underlying mechanism of our predictor and associate it with network–ENSO and ENSO–monsoon connections. Moreover, our approach allows predicting the all-India rainfall, as well as the rainfall different homogeneous Indian regions, which is crucial for agriculture in India. We reveal that global warming affects the climate network by enhancing cross-equatorial teleconnections between the southwest Atlantic, the western part of the Indian Ocean, and the North Asia–Pacific region, with significant impacts on the precipitation in India. A stronger connection through the chain of the main atmospheric circulations patterns benefits the prediction of the amount of rainfall. We uncover a hotspot area in the midlatitude South Atlantic, which is the basis for our predictor, the southwest Atlantic subtropical index (SWAS index). Remarkably, the significant warming trend in this area yields an improvement of the prediction skill. [ABSTRACT FROM AUTHOR]

This study performs the multifractal characterization of reference evapotranspiration (ET0) and its controlling factors of five locations in India with climatic diversity. First, the ET0 and the predictor variables like minimum air temperature ( T min ) , maximum air temperature ( T max ) and average wind speed (AW) of five stations are analysed using multifractal detrended fluctuation analysis (MFDFA). The investigation could detect long-term persistence and multifractality of different series, irrespective of the climatic condition and geographical location. Higher persistence (>0.8) is noted in the ET0 and T min series indicating higher predictability in all the stations and highest multifractality was noted for the highly complex wind speed time series. Further, the ET0 estimates by Hargreaves Samani (HS) and Droogers and Allen (DA) methods differs in their persistence and multifractal properties, controlled by the geographic location of the station. Subsequently, multifractal cross correlation analysis (MFCCA) is used to investigate the correlations between ET0 and other variables. MFCCA analysis showed that, for all the time series considered, the joint scaling exponent is roughly the average of individual scaling exponents, and base width of the joint spectra is lower than that of individual series, validating two universal properties of multifractal cross correlation studies for agro-meteorological datasets. [ABSTRACT FROM AUTHOR]

Human-induced land use land cover changes particularly from forestland to cropland and pastures have increased over the times in tropical regions. The present study aims to understand the impact of enhanced forest conditions on regional weather over central India using a coupled land-atmospheric model in NASA-Unified WRF (NU-WRF) system during monsoon and post-monsoon seasons for the year 2013. In this study, two sets of simulations were carried out over the Indian region: (i) Control (Ctl) run that represents the cropland (normal) scenario and (ii) experimental (Exp) run that represents enhanced forest conditions, where the cropland cover was replaced by evergreen broadleaf forest over the targeted region(central India). Enhanced forest conditions resulted in a decrease of near-surface (2 m) air temperature and an increase in near-surface (2 m) specific humidity during both the seasons, which is attributed to an increase in latent heat flux and a decrease in sensible heat flux. An increase of surface roughness length due to enhanced forest conditions led to a reduction in low-level winds during both the seasons. An increase in both moisture convergence and evapotranspiration led to an increase in precipitation during the monsoon season, while an increase in moisture divergence led to a slight decrease in precipitation during the post-monsoon season. Furthermore, the effect of enhanced forest conditions on regional weather, except for precipitation and low-level circulation patterns, is more pronounced during the post-monsoon season than during the monsoon season. [ABSTRACT FROM AUTHOR]

This paper presents a relationship of lightning activity with maximum air temperature, Bowen ratio, rainfall, cloud ice contents and Aerosol Optical Depth (AOD) over India during transition period from dry to wet seasons. Lightning flash count data and weather parameters are retrieved from the Tropical Rainfall Measuring Mission (Lightning Imaging Sensor - LIS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) satellites for the period of 17 years (1998–2014). The Pearson correlation between lightning flash count and Bowen ratio is found to be coefficient of R = 0.95 for both dry and wet seasons. For dry season, the Pearson correlation of lightning flash count with surface maximum air temperature is found to be coefficient of R = 0.97 whereas, that for wet season negative coefficient of R = −0.89. The comparative analysis of Pearson correlations of lightning flash counts with AOD, rainfall and cloud ice content are found to be coefficients higher by 20%, 28% and 34% for dry season than those of during wet season, respectively. The results of lightning activity and weather parameters in comparative analyses over the Indian region may be useful for better understanding the differential convection characteristics during transition period of dry to wet season. The results are also important for estimations of impact associated with lightning strikes to ground during dry and wet seasons. • Lightning activity and its relationship with weather parameters during transition from dry to wet season over Indian region. • The flash count is found to be high during dry season as compared to wet season. • Bowen ratio plays important role in the formation of thunderstorm and lightning. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) module qualification standards, IEC 61215 and IEC 61730, were designed to apply to "general open‐air climates" and IEC 61730 specifically indicated applicability of ambient air temperature of 40°C. Additionally, these standards provided allowances for so‐called "open rack mounted PV modules" without a clear definition of "open‐rack." These implied restrictions and allowances meant that hotter climates or thermally restrictive installation methods may not be covered by these often customer‐mandated certification standards. This is particularly salient for the significant growth regions of the Middle East and India that would be expected to operate at significantly higher temperatures. The applicability of these documents raised issues over the definition of "open rack" and the fact that the geographic location is just as important as the mounting configuration in assessing the impact of the micro‐environment of a PV module. This work summarizes the scientific background for IEC Technical Specification 63126:2020 ED1, titled "Guidelines for qualifying PV modules components and materials for operation at high temperatures." This standard was recently published by the IEC and is the first step in a systematic effort to rework these standards to address the question of temperature more directly. Instead of specifying a mounting condition, we specify different suites of tests suitable for a system (PV module, mounting style, and location) defined by the 98th percentile cell temperature. With a defined temperature regime to work from, this allowed us to use existing literature research combined with additional modeling work to determine, which tests would need to be modified. This resulted in suggested changes to material thermal indices, thermal cycling temperatures, hot spot testing, ultraviolet testing, and bypass diode testing among other tests and characteristics described herein. [ABSTRACT FROM AUTHOR]

In the long future, dependency on conventional air conditioning systems for thermal comfort certainly needs to be reduced, with alternate strategies like passive photonic radiative cooling. Recent metamaterial development with highly reflective in the solar spectrum pushes the radiative cooling technique toward application assessment. In this paper, the photonic radiative cooler performance is analyzed for year 2019 at various Indian locations considering the diversity of climate and forecasted for year 2030. The effect of three types of climate change is considered: geographical, seasonal and year-wise. Some photonic coolers with different emissive profiles are also compared. As the radiative cooling depends upon air temperature, humidity, wind speed and solar flux intensity, the effects of influencing climatic or weather parameters during summer months are studied extensively and major performance influencing factors are identified. Photonic radiative cooler performance in energy-saving as rooftop envelope assisted to conventional air conditioning system is assessed and observed cooling energy saving of 25–32 kWh th /month for selected locations. Study reveals that the windshield is the necessary condition to get net cooling flux through the rooftop. The reduction of cooling load on active systems of 34% is observed for low humidity locations with the integration of radiative cooler as a roof envelope. Image 1 • Potential of photonic radiative cooler in Indian cities is assessed and forecasted for 2030. • Effects of weather parameters on the cooler performance are studied for summer months. • Wind velocity and humidity are found major performance influencing weather parameters. • Selective cooler is found better for low humidity and broadband one is for high humidity. • In situ maximum cooling load reduction of 36% is observed for low humidity location. [ABSTRACT FROM AUTHOR]

Persimmon (Diospyros kaki) is an important, underutilized fruit crop of India. The thin layer drying of persimmon slices would make it a perfect snack that can be used in various recipes. The drying process of persimmon slices was optimized using Box–Behnken design in response surface methodology. Three categorical independent drying variables, such as temperature (50–70°C), air velocity (0.5–1.5 m/s), and sample thickness (4–8 mm), were considered for the study. The response variables include drying time, ascorbic acid content, browning index, and rehydration ratio were investigated. The air temperature and sample thickness showed a significant effect on drying time. Browning index and rehydration ratio were significantly affected by air temperature and air velocity. As per the given experimental design constraints, the optimum solutions obtained were air temperature (58°C), air velocity (1.40 m/s), and slice thickness (4 mm). The predicted responses for the optimized combination of the drying process were drying time (442.55 min), ascorbic acid (8.61 mg/g dry matter), browning index (0.396 absorbance unit), and rehydration ratio (5.47%). Therefore, this study would be beneficial for scaling up industrial drying of persimmon slices for commercial production to ensure smooth processing and achieve a profitable path. [ABSTRACT FROM AUTHOR]

A spatio-temporal analysis of representative concentration pathways (RCPs)-based projections of surface air temperatures under different combinations of global circulation models (GCMs) and RCPs for Satluj River Basin has been presented. The projections of temperature anomalies have been obtained for several meteorological stations in the basin using 16 different combinations of emission scenarios and global climate models (GCMs). For each combination, the projections of temperature anomalies have been analysed for two future time periods centred at 2030 and 2050. The results of the analysis conducted herein clearly indicate that the temperature will rise for all the future time scales with the maximum increase being projected under RCP8.5 compared to the baseline 1986–2005. However, there is large inter-model variability in the projections of temperature anomalies under different RCPs. Under RCP 8.5, the average temperature in Satluj basin is projected to rise by around 4°C by mid of the twenty-first century. The projections of temperature anomalies analysed herein could be potentially used for the evaluation of hydrological impacts of climate change in Satluj River Basin—a key basin in the Himalayan region from the view point of global warming. [ABSTRACT FROM AUTHOR]

Sedimentation in lakes occurs from the time of its formation and continues till the final deposition. The rate of sedimentation varies depending upon climate, geology, soil cover and relief. In the present study, an attempt has been made to investigate the distribution and variation of sedimentation rate in Indian lakes. For this purpose, Indian lakes are classified into three zones viz., North Indian lakes (NIL), Central Indian lakes (CIL), South Indian lakes (SIL) and 27 sampling points distributed throughout India has been considered among all three zones. Correlation of rate of sedimentation data with catchment area, surface area and lithology is done in order to understand the controlling factors, rainfall and temperature data are also taken into consideration to know the effect on the sedimentation rate. In all three zones, the highest precipitation observed 2488, 1800 and 4000 mm/yr in the Nainital lake, Chilika lake and Pookot lake, respectively and temperature fluctuations in the northern region of India is higher than the central and southern region. The results showed that the NIL have relatively higher sedimentation rate compared to the CIL and SIL due to slopy terrain, higher precipitation, temperature and lithological inconsistency in northern Indian region whereas all these factors are less effective in other two zones with a minor contribution from other independent factors. NIL have ~0.634-0.804 cm/yr and ~0.636-1.117 cm/yr more sedimentation rate than CIL and SIL, respectively. [ABSTRACT FROM AUTHOR]