The rising frequency of heat waves in India presents significant risks to public health, agriculture, and the economy. In March 2022, temperatures reached a record-breaking 33.10 degrees C, the highest in 122 years resulting in two major heat wave events: March 11-21 and March 26-31, which claimed 33 lives. This study delves into the impact of anthropogenic emission/aerosols and meteorological data assimilation on model-predicted surface meteorological variables using "Weather Research and Forecasting (WRF) model" coupled with Chemistry (WRFChem). Four distinct simulation scenarios namely, WRF, WRFDA (WRF with meteorological Data Assimilation), WRF-Chem, and WRF-ChemDA (WRF-Chem with meteorological Data Assimilation) were executed across the Haryana domain to assess the sensitivity of model outputs. Analyses within the WRFDA and WRF-ChemDA frameworks utilized a 6-hourly cyclic 3-Dimensional Variational (3DVAR) Data Assimilation (DA) of NCEP ADP Surface Observational Fields. Most critically, the incorporation of aerosols and DA techniques markedly improved forecasts of key meteorological variables, including 2 m Temperature (T2), 2 m Relative Humidity (RH2), Planetary Boundary Layer Height (PBLH), and Outgoing Longwave Radiation (OLR). Skill assessment metrics, including the Heidke Skill Score (HSS similar to 0.5), Accuracy (ACC >0.9), and Probability of Detection (POD similar to 1), demonstrate that WRF-ChemDA outperformed other models, especially during heat wave events. Conclusively, this study advocates for the meticulous selection of modeling approaches to accurately simulate heat wave events, ensuring that selected models adeptly capture the intricate dynamics and complexities of extreme temperature phenomena.

In the present era, climate change is the biggest challenge for the world. The vulnerability of climate change to India’s agriculture sector is quite evident. Due to its geographical existence, climate change is creating a vulnerable situation in the eastern part of India. Odisha's economy is one of the most affected in this regard. The agriculture sector, being the common practice of livelihood, is always sensitive to climate change. The unpredictable rainfall patterns, floods, droughts, and frequent cyclones have caused severe damage to crops and livestock, leading to a lack of employment and a vulnerable situation for farmer households. Therefore, this study employed the LVI approach to evaluate farmers' climate change vulnerability and used the probit model to identify the factors influencing farmers' adaptation options. To accomplish the stated objective, both primary and secondary data have been used. Primary data have been collected from four blocks (Athagarh, Cuttack Sadar, Barang, and Banki) of Cuttack districts, Odisha, where most of the farmers are marginal farmers. The LVI combined nine major components and 33 subcomponents under it, which establishes a specific functional relationship with vulnerability. The vulnerability assessment indicates that Cuttack district is moderately vulnerable to climate change, with a 0.41 vulnerability score. Social network and livelihood strategies are the major scoring indices in this regard, with 0.90 and 0.65 LVI values, respectively. The probit model found that farming experience and non-farm income are detrimental factors for adopting any strategies to combat climate change. The policy options are precautionary measures that are required to withstand the negative effects of climate change. Therefore, effective government steps required in creating an awareness program, extending training facilities to the farmers, and proper provisioning of irrigation and credit facilities are highly essential for increasing crop productivity and reducing the vulnerability of the farmer household.

The performance of agriculture sector in any economy is better understood through the analysis of growth and instability in agriculture production. It is evident that the growth of agriculture in Odisha is very erratic in nature and huge instability is there in agricultural production. Instability in agriculture affects the trend and pattern of production which creates risk in farmers’ ability to adopt new technologies. Therefore, this paper aims to study the growth and instability in agriculture production in Odisha where growth and instability on area, yield and production of twenty crops have been calculated. Instability in agriculture production is caused by various agricultural reforms, weather variation and price fluctuation. Therefore, in this study attempt has been made to analyse the contribution of agriculture and allied sector to GSVA in Odisha over the years, sectoral employment, and district wise crop intensity. To examine growth and instability in agriculture of twenty major crops, this study has undertaken decadal analysis by using secondary data. Total four decades i.e. 1970-80, 1980-90, 1990-00, 2000-10 and 2010-20 are considered. The results show that, agriculture sector is leading sector in providing employment and significantly contribute to GSDP in Odisha. However, the area under cultivation of all the crops area gradually decreasing due to the urbanization but the percentage of area under cereal cultivation is decreasing whereas percentage of area under pulses are increasing but the area under total food grains is decreasing showing farmers are switching towards non-food grain products. So far, the growth of production and yield of different crop areas shows erratic growth in agricultural crops, as evident from the high instability rate in the growth of different crops. The cropping intensity shows that it is lower than the national level and therefore the role of technology needs to be identified.

Ninety climate models, from four consortiums—CMIP5, CMIP6, NEX-GDDP, and CORDEX—are evaluated for the simulation of seasonal temperature and precipitation over India, and subsequently, using the best ones, their future projections are made for the country. NEX-GDDP is found to be the best performer for the simulation of surface air temperature for all the four seasons. For the simulation of precipitation, CMIP6 performs the best in DJF and MAM seasons, while NEX-GDDP performs the best in JJAS and ON seasons. The selected models suggest that temperature will increase over the entire Indian landmass, relatively more over the north-western part of the country. Furthermore, the rate of warming will be more in winter than in summer. The models also suggest that precipitation will increase over central eastern and north-eastern India in the monsoon season, and over peninsular India during post-monsoon months.

Various observational and modelling studies indicate rise in surface air temperature and its extremes at several places in the world. With the availability of more data both from observations and models, it is possible to comprehensively examine future projections in order to get meaningful climate information and identify strong climate signals anywhere. This study is focused on rise in surface air temperatures and the associated temperature related extreme events such as warm days & nights and cold days and nights at four city clusters in India such as Delhi, Mumbai, Chennai and Guwahati. Dynamically downscaled future projections at the four selected city clusters are examined using RegCM CORDEX simulations. Further, selected CMIP model projections are used. In this study, uniformity of the trends in the selected indices found in each city are tested against those found in the corresponding Meteorological Sub-Divisions and Temperature Homogeneous Zones. Increase in the surface air minimum and maximum temperatures along with the frequency of occurrence of warm nights at all the four city clusters are the strong signals of climate projection obtained from this study.

Regional variations of monsoon onset dates across India were analyzed for 67 years (1951–2017) under different modes of climate variations, i.e., El Niño, La Niña, and the Indian Ocean Dipole (IOD), along with flood and drought years using the objective method and statistical techniques. Monsoon onset analysis revealed that the northern, northeastern, and southern parts were highly susceptible to the early onset of La Niña, and the northern and northern northwest parts were highly susceptible to the early onset of El Niño. The onset dates were early (late) in the sub-regions of the central, southern, and northeastern (northern, northwestern, and western) parts of India during flood (drought) years. Further, onset dates in flood years occurred earlier than those in La Niña years, and onset dates in drought years were later than those in El Niño years. The onset occurrence probability and influence of the synoptic events are discussed. This research could help in understanding the onset of monsoon and its predictability for societal applications.

Hydrogenated diamond-like carbon (DLC) films are sought for several technological applications including electronics, optics, mechanics, and tribology. However, conventional hydrogenated DLC films, that commonly deposit at low base pressure (similar to 10(-5) to 10(-8) Torr), have many intrinsic limitations in terms of moderate hardness (15-25 GPa), low electrical conductivity (in insulating regime), and they display amorphous morphology. Here we develop high-performance hydrogenated carbon-based films using a cost-effective and fast deposition approach. We report the room temperature synthesis of nitrogen incorporated nanostructured hydrogenated carbon films (n-C:N:H) at a high base pressure of similar to 5 x 10(-3) Torr, employing a non-conventional radio frequency plasma enhanced chemical vapor deposition (RF-PECVD) system equipped with only a primary pump. The n-C:N: H films show appealing properties such as wide band gap (2.35-2.9 eV), high optical transparency, high hardness (up to similar to 40 GPa), ultrahigh elasticity (elastic recovery similar to 95%), reasonably good electrical conductivity, and diode-like behavior when examined in n-C:N:H/Si heterojunction device configuration. Interestingly, some of nC:N:H films revealed the multifunctional activities with properties surpassing to those of many low base pressure grown traditional amorphous DLC films. This discovery solves many fundamental concerns of hydrogenated DLC films and opens new paths for their enhanced commercial applications.

Herein, we are reporting a case of Stevens–Johnson syndrome due to COVID‐19 vaccine, which has been proved by history taking, clinical examination and histopathology. To the best of our knowledge we are reporting the first case of COVID‐19 vaccine‐induced Stevens–Johnson syndrome.

Rainfall associated with landfalling tropical cyclones (TCs) is one of the significant causes of loss of life, property, and crops in India. In the past few decades, the accuracy of track and intensity forecast of tropical cyclones has improved considerably, but the estimation and forecast of associated rainfall is still a challenge. Accurate rainfall estimation at the time of landfall is crucial as it enables disaster management agencies to plan for disaster management strategies in the affected areas. It is known that the rainfall associated with TCs, particularly at the time of landfall, is highly variable and asymmetric due to various factors such as vertical wind shear, translational speed, land surface processes, extratropical transition and moisture. This paper discusses the challenges faced in forecasting rainfall associated with the land falling TCs in the North Indian Ocean and the factors responsible for variable and asymmetric rainfall by considering several TCs of various intensities. Advances in methodology for measuring and estimating precipitation with the help of Satellites and Radars and forecasting it using statistical and numerical weather prediction (NWP) techniques are also discussed here. Rainfall characteristics associated with the landfall of two unique TCs formed in the Bay of Bengal in December 2011 and 2016, based on composites of the frequency distribution of rain rates and quadrant mean rain rates, have been discussed in detail to highlight the challenges involved. It is inferred that rapid intensification or weakening of a TC over or close to the coast during the landfall process may alter the rainfall characteristics overland to a large extent.

Every single aspect of environment is affected by climate change. Change in rainfall pattern is an immense important research area in climate-change based study. Rainfall pattern has direct impacts on food production and frequencies natural disasters (landslide, cloudburst, flood, drought etc.). Consequently, that appropriate and systemic consideration since it distresses the most of the human life. Situation in Himalayan region is worst. High altitude, less agricultural area, harsh climate with high fragility makes mountain region more vulnerable in term of climate change. The objective of this study is to identify yearly, seasonal, and monthly rainfall trends in the Upper Kumaon region (UKR). Long-term gridded daily rainfall data (1950–2018) were used. Rainfall data was processed and analyzed for a period of 68 years (1950–2018) at four places (four in each Kumaon division) in the surrounding area of Almora, Bageshwar, Pithoragarh, and Champawat. The regression analysis (parametric) method and variability analysis were used to examine historical trends in daily rainfall. The rising and falling trends in rainfall, as well as anomalies, have been studied using regression.The result shows that rainfall demonstrate statistically significant changes occurred in last 34 years. Rainfall variability is higher in low altitude region than high altitude region of Upper Kumaon region.

Heat stress is one of the leading natural causes of mortality in India. Aerosols can potentially impact heat stress by modulating the meteorological conditions via radiative feedback. However, a quantitative understanding of such an impact is lacking. Here, using a chemical transport model, Weather Research Forecasting model coupled with chemistry, we show that high aerosol loading in India was able to mask the heat stress (quantified by the wet bulb globe temperature (WBGT)) by 0.3 °C–1.5 °C in 2010 with a regional heterogeneity across the major climate zones in India. However, the cooling effect of aerosol direct radiative forcing is partially compensated by an increase in humidity. To understand the potential impact of air quality improvement (i.e. reducing aerosol load) on heat stress in the future, WBGT was projected for 2030 under two contrasting aerosol emission pathways. We found that heat stress would increase by >0.75 °C in all the climate zones in India except in the montane zone under the RCP4.5 scenario with a bigger margin of increase in the mitigation emission pathway relative to the baseline emission pathway. On the contrary, under the RCP8.5 scenario, heat stress is projected to increase in limited regions, such as the tropical wet and dry, north-eastern part of the humid sub-tropical, tropical wet, and semi-arid climate zone in peninsular India. Our results demonstrate that aerosols modulate heat stress and, therefore, the heat stress projections in India and anywhere else with high aerosol loading should consider aerosol radiative feedback.

Indian Summer Monsoon (ISM) and East Asian Summer Monsoon (EASM) are the most important weather systems in India and Korea respectively. The interannual variations of ISM and EASM largely depend on the anomalies of sea surface temperatures (SSTs) of Tropical Equtorial Pacific and Indian Ocean and also on the Eurasian Snow Depth/Cover. In this study, the relative roles of these two most important surface boundary conditions on the Indian and Korean monsoons have been examined using the high resolution (40 km) global numerical weather prediction model of German Weather Service (GME). Four sets of experiments were carried out by varying the climatological and observed values of SSTs and snow. This study shows that there is a negative (positive) relationship between the Western (Eastern) Eurasian snow depth and ISMR, whereas a positive (negative) relationship between the Western (Eastern) Eurasian snow depth and Korean monsoon rainfall has been observed. Results show that when observed SST and snow are prescribed to the model as boundary conditions, the simulated winds and rainfall are close to the NCEP/NCAR reanalyzed winds and GPCP rainfall, respectively. This study reveals that the model simulated summer monsoons in Indian and Korean domains are not good enough unless an observed Eurasian snow is prescribed in addition to the observed SST.

Present study attempts to project extreme precipitation indices over 34 different meteorological subdivisions and six homogeneous regions such as Northwest, Central Northeast, Northeast, West Central, Peninsular India and Hilly Region during summer monsoon season in the twenty-first century. For this purpose, the Regional Climate Model version4 (RegCM4) had been run at 50 km horizontal resolution forced with the global model GFDL-ESM2 M, during reference period 1976–2005 for the model validation, and the mid- (2031–2060) and far-future (2070–2099) for projections under RCP8.5 scenario over the South Asia CORDEX domain. In this paper, model simulated precipitation has been validated against IMD, APHRODITE and NCEP/NCAR data sets. The results indicate that RegCM4 captures the important features of seasonal precipitation and various extreme indices over the study area. The RegCM4 has projected an increase in the mean seasonal precipitation by 0.56 mm/day whereas in case of GFDL model the rate is 0.39 mm/day during the far-future relative to the reference period. The heavy precipitation indices are projected to increase more frequently (0.264/decade) than the mean precipitation rate (0.01/decade) over India. The correlations between the extreme precipitation indices and the seasonal mean precipitation are found to be strong. In addition, the consecutive dry days are projected to occur more frequently (3–5 days) over West Central (Telangana, Vidarbha and Marathwada) and west Rajasthan while consecutive wet days are projected to decrease over larger parts of India during far-future. Similarly, 1 day maximum precipitation and the simple daily intensity index are projected to increase consistently from mid- to far- futures over some sub-divisions of West coast, Hilly and Northeast regions. From a spatial probability perspective, model projection indicates more frequent severe drought and flood conditions over India.

Dynamical and Statistical models are operationally used by Snow and Avalanche Study Establishment (SASE) for winter precipitation forecasting over the Northwest Himalayas (NWH). In this paper, a statistical regression model developed for seasonal (December–April) precipitation forecast over Northwest Himalaya is discussed. After carrying out the analysis of various atmospheric parameters that affect the winter precipitation over the NWH two parameters are selected such as North Atlantic Oscillation (NAO) and Outgoing Long wave Radiation (OLR) over specific areas of North Atlantic Ocean for the development of statistical regression model. A set of 27 years (1990–1991 to 2016–2017) of observed precipitation data and parameters (NAO and OLR) are utilized. Out of 27 years of data, first 20 years (1990–1991 to 2009–2010) are used for the development of regression model and remaining 7 years (2010–2011 to 2016–2017) are used for the validation purpose. Precipitation over NWH mainly associated with Western Disturbances (WDs) and the results of the present study reveal that NAO during SON has negative relationship with WDs and also with the winter precipitation over same region. Quantitative validation of the multiple regression model, result shows good Skill Score and RMSE-observations standard deviation ratio (RSR) which is 0.79 and 0.45 respectively and BIAS − 0.92.

Using a dynamical model (VECTRI) for malaria transmission that accounts for the influence of population and climatic conditions, malaria transmission dynamics is investigated for a highly endemic region (state of Odisha) in India. The model is first calibrated over the region, and subsequently numerical simulations are carried out for the period 2000–2013. Using both model and observations we find that temperature, adult mosquito population, and infective biting rates have increased over this period, and the malaria vector abundance is higher during the summer monsoon season. Regionally, the intensity of malaria transmission is found to be higher in the north, central and southern districts of Odisha where the mosquito populations and the number of infective bites are more and mainly in the forest or mountainous ecotypes. We also find that the peak of the malaria transmission occurs when the monthly mean temperature is in the range of ~28–29 °C, and monthly rainfall accumulation in the range of ~200–360 mm.

The regional climate model version 4 (RegCM4) is analyzed in this study to assess Indian summer monsoon precipitation (ISMP) over six homogeneous precipitation regions and various meteorological subdivisions of India embedded therein during a reference period (1976–2005) and mid- (2031–2060) and far-future (2070–2099) periods under the RCP8.5 scenario over the South Asia CORDEX domain. A Coupled Model Intercomparison Project (CIMIP5) global model GFDL-ESM2M provides initial and boundary conditions to the RegCM4 under the high-emission scenario RCP8.5. RegCM4 precipitation fields are validated against observed India Meteorological Department (IMD) and Asian Precipitation-Highly Resolved Observational Data Integration Toward Evaluation (APHRODITE) precipitation datasets, while wind and specific humidity fields obtained from RegCM4 are validated against National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis and the parent GFDL model integrated fields. Model comparisons indicate that RegCM4 captures the regional characteristics of ISMP satisfactorily in terms of biases, trends, interannual variability, and circulation patterns. RegCM4 precipitation fields show high correlations of 0.9 and 0.8 with those of IMD and APHRODITE, respectively, and RegCM shows better skill in comparison with GFDL over about 68% of meteorological subdivisions. RegCM4 projects increases in precipitation by about 15.3% (28.4%), 5.1% (16.2%), and 5.4% (18.4%) respectively over the Northwest (NW), Northeast (NE) and Hilly Regions (HR) in the mid-(far-)future but decreases in precipitation over the Peninsular (PE) region by about −1.5% (−15.7%) during the same period. A similar precipitation change pattern is also found when analyzing the probability distribution functions (PDFs) over the same regions. The precipitation intensity (95th percentile) shows increases above 40% over numerous subdivisions of the West Central (WC), NW, and HR regions. The present analysis also reveals significant increases of more than 50% in mean precipitation over several meteorological subdivisions. Analysis of the circulation fields depicts a northward shift of the high-precipitation belt while high-pressure systems dominate the peninsular region when approaching the central India global warming scenario. It is interesting to note that the extreme precipitation index Rx5day exactly follows the pattern of projected increase in mean precipitation. In addition, it is noted that the projected variability and change in the mean precipitation are less frequent than for RX5day, while a consistently stronger spread in variability is projected in the mid- to far-future under the warming scenario.

Using data from 33 models from the CMIP5 historical and AMIP5 simulations, we have carried out a systematic analysis of biases in total precipitation and its convective and large-scale components over the south Asian region. We have used 23 years (1983–2005) of data, and have computed model biases with respect to the PERSIANN-CDR precipitation (with convective/large-scale ratio derived from TRMM 3A12). A clustering algorithm was applied on the total, convective, and large-scale precipitation biases seen in CMIP5 models to group them based on the degree of similarity in the global bias patterns. Subsequently, AMIP5 models were analyzed to conclude if the biases were primarily due to the atmospheric component or due to the oceanic component of individual models. Our analysis shows that the set of individual models falling in a given group is somewhat sensitive to the variable (total/convective/large-scale precipitation) used for clustering. Over the south Asian region, some of the convective and large-scale precipitation biases are common across groups, emphasizing that although on a global scale the bias patterns may be sufficiently different to cluster the models into different groups, regionally, it may not be true. In general, models tend to overestimate the convective component and underestimate the large-scale component over the south Asian region, although with spatially varying magnitudes depending on the model group. We find that the convective precipitation biases are largely governed by the closure and trigger assumptions used in the convection parameterization schemes used in these models, and to a lesser extent on details of the individual cloud models. Using two different methods: (i) clustering, (ii) comparing the bias patterns of models from CMIP5 with their AMIP5 counterparts, we find that, in general, the atmospheric component (and not the oceanic component through biases in SSTs and atmosphere-ocean feedbacks) plays a major role in deciding the convective and large-scale precipitation biases. However, the oceanic component has been found important for one of the convective groups in deciding the convective precipitation biases (over the maritime continent).