Explore the vast range of titles available.

Socioeconomic challenges continue to mount for half a billion residents of central India because of a decline in the total rainfall and a concurrent rise in the magnitude and frequency of extreme rainfall events. Alongside a weakening monsoon circulation, the locally available moisture and the frequency of moisture-laden depressions from the Bay of Bengal have also declined. Here we show that despite these negative trends, there is a threefold increase in widespread extreme rain events over central India during 1950–2015. The rise in these events is due to an increasing variability of the low-level monsoon westerlies over the Arabian Sea, driving surges of moisture supply, leading to extreme rainfall episodes across the entire central subcontinent. The homogeneity of these severe weather events and their association with the ocean temperatures underscores the potential predictability of these events by two-to-three weeks, which offers hope in mitigating their catastrophic impact on life, agriculture and property. Against the backdrop of a declining monsoon, the number of extreme rain events is on the rise over central India. Here the authors identify a threefold increase in widespread extreme rains over the region during 1950–2015, driven by an increasing variability of the low-level westerlies over the Arabian Sea.

Variability and trends of the south Asian monsoon at different time scales makes the region susceptible to climate-related natural disasters such as droughts and floods. Because of its importance, different studies have examined the climatic factors responsible for the recent changes in monsoon strength. Here, using observations and climate model experiments we show that monsoon strength is driven by the variations of south Atlantic Ocean sea surface temperature (SASST). The mechanism by which SASST is modulating the monsoon could be explained through the classical Matsuno-Gill response, leading to changes in the characteristics of vertical wind shear in the Arabian Sea. The decline in the vertical wind shear to the warming of SASST is associated with anomalous lower (upper)-level easterlies (westerlies). This further leads to a strong increase in the frequency of the Arabian Sea cyclones; and also prohibits the transport of moisture to the Indian landmass, which eventually reduces the strength of monsoon. The conditions in the SASST which drove these responses are aggravated by greenhouse gas emission, revealing the prominent role played by anthropogenic warming. If, with proper mitigation, these emissions are not prevented, further increases in the SASST is expected to result in increased Arabian sea cyclones and reduced monsoon strength.

Three key issues of moisture supply and Indian summer monsoon rainfall (ISMR) variability are discussed in the present work: identification of the oceanic and terrestrial sources of moisture; the extent to which each source affects the ISMR; and their individual contributions to the interannual variability of ISMR. The modified Dynamic Recycling Model, based on a Lagrangian trajectory approach, is used to estimate the relative contributions from 27 terrestrial and oceanic moisture source regions to the monsoon during 1979–2013. ERA-Interim data are used for the study. The results show that the ISMR is strongly influenced by the land–ocean–atmosphere interactions, and a significant fraction of atmospheric moisture to the ISMR comes from five main moisture sources: the western Indian Ocean (WIO), central Indian Ocean (CIO), upper Indian Ocean (UIO), Ganges basin (GB), and Red Sea and the neighboring gulf (RDG). The moisture flux from WIO is very high during the initial period of monsoon seasons. From the mid-monsoon season, the contribution from this moisture source decays and land sources through evapotranspiration (ET) become more active. Early decay of moisture contributions from the WIO and the GB is observed during weak monsoon years. El Niño years are associated with low contributions of moisture from all sources, whereas warm Indian Ocean years are associated with low moisture flux from the major sources except WIO. ISMR is characterized by the prolonged and increasing moisture supply from WIO during the first half of the monsoon along with contributions from GB during the end of season. The results are consistent across several reanalyses (CFSR, ERA-Interim, and MERRA).

Weakening of Indian summer monsoon rainfall (ISMR) is traditionally linked with large-scale perturbations and circulations. However, the impacts of local changes in land use and land cover (LULC) on ISMR have yet to be explored. Here, we analyzed this topic using the regional Weather Research and Forecasting model with European Center for Medium range Weather Forecast (ECMWF) reanalysis data for the years 2000–2010 as a boundary condition and with LULC data from 1987 and 2005. The differences in LULC between 1987 and 2005 showed deforestation with conversion of forest land to crop land, though the magnitude of such conversion is uncertain because of the coarse resolution of satellite images and use of differential sources and methods for data extraction. We performed a sensitivity analysis to understand the impacts of large-scale deforestation in India on monsoon precipitation and found such impacts are similar to the observed changes in terms of spatial patterns and magnitude. We found that deforestation results in weakening of the ISMR because of the decrease in evapotranspiration and subsequent decrease in the recycled component of precipitation.

The Indian summer monsoon rainfall is dominated by oceanic sources of moisture. However, land surface processes also have a significant role in the generation of precipitation within the Indian subcontinent. Evapotranspiration over a region supplies moisture to the atmosphere, which may lead to precipitation in the same region. This is known as recycled precipitation. The role of evapotranspiration as an additional source of moisture to precipitation has been investigated in earlier studies at continental scales; however, the amount of monsoon precipitation generated from evapotranspiration has not been quantified at the daily scale for the Indian subcontinent. To examine the role of land surface hydrology in regional precipitation and to quantify recycled precipitation, the dynamic recycling model at a daily scale with NCEP Climate Forecast System Reanalysis (CFSR) data for the period of 1980–2010 is used. A high precipitation recycling ratio, that is, the ratio of recycled precipitation to total precipitation, is found at the end of the monsoon (September). As the monsoon progresses in India, enhanced soil moisture and vegetation cover lead to increased evapotranspiration and recycled precipitation. The recycling ratio is highest (around 25%) in northeastern India, which has high vegetation cover leading to high evapotranspiration. Recycled precipitation over central and northeastern India in September is responsible for delaying the withdrawal of the summer monsoon over these regions. A trend analysis of recycled precipitation shows a statistically significant decreasing trend in northeastern India.

The intensification of precipitation extremes in a warming world has been reported on a global scale and is traditionally explained with the Clausius-Clapeyron (C-C) relation. The relationship is observed to be valid in mid-latitudes; however, the debate persists in tropical monsoon regions, with the extremes of the Indian Summer Monsoon Rainfall (ISMR) being a prime example. Here, we present a comprehensive study on the dependence of ISMR extremes on both the 2 m surface air temperature over India and on the sea surface temperature over the tropical Indian Ocean. Remarkably, the ISMR extremes exhibit no significant association with temperature at either spatial scale: neither aggregated over the entire India/Tropical Indian Ocean area nor at the grid levels. We find that the theoretical C-C relation overestimates the positive changes in precipitation extremes, which is also reflected in the Coupled Model Intercomparison Project 5 (CMIP5) simulations. We emphasize that the changing patterns of extremes over the Indian subcontinent need a scientific re-evaluation, which is possible due to availability of the unique long-term in-situ data. This can aid bias correction of model projections of extremes whose value for climate adaptation can hardly be overemphasized, especially for the developing tropical countries.

The atmospheric moisture budget is a fundamental regulator of regional hydroclimate, yet its component-wise dynamics across India’s diverse agro-climatic zones remain poorly quantified. This study presents a comprehensive assessment of evaporation minus precipitation (E–P) variability over 1990–2024 using era reanalysis data5 reanalysis data, employing a full atmospheric moisture budget framework. The atmospheric moisture transport is decomposed into its mean (M1), and transient (M2, M4), and interaction (M3) components. This approach allows for the explicit attribution of E–P trends and anomalies to specific dynamic drivers. We also perform a decomposition of vertically integrated moisture transport into mean component, their low-frequency (LF) (>10 d) and synoptic (<10 d) scale contributions. Results reveal pronounced spatial heterogeneity: negative E–P trends dominate western India and the coast, while positive trends prevail over the Gangetic plains and Northeast, consistent with observed monsoon weakening in high-rainfall regions. Our analysis demonstrates that while mean moisture transport by mean winds establishes the climatic-scale background, transient eddy fluxes constitute a significant secondary driver, amplifying intraseasonal variability across central and northern India. Moreover, scale separation clarifies that LF oscillations modulate broad-scale convergence patterns, whereas synoptic disturbances generate sharp, localized extremes. This study offers the first mechanistic, India-wide attribution of E–P variability, linking large-scale circulation to weather-scale events. A critical finding is a widespread positive trend in transient eddy divergence (M4), indicating a strengthening moisture-export that reduces rainfall efficiency. By disentangling these drivers, our analysis provides essential insights for improving predictions of water stress and informing targeted climate adaptation strategies in this vulnerable, monsoon-dependent region.

IntroductionThe World Health Organization (WHO) is a specialized agency of the United Nations responsible for international public health. Established on April 7, 1948, it has since played a pivotal role in several public health achievements and has had considerable success. But never since the establishment of the WHO has it faced a pandemic of such a huge scale. The spread of the coronavirus and the inability of the WHO to contain it has raised many questions about its efficiency and role. The present study explores the range of emotions and sentiments evoked by public health information posts of WHO over the course of the pandemic.MethodsThis study uses Bidirectional Encoder Representations from Transformers (BERT), which is a neural network-based technique for natural language processing. Three timeframes of five months each, starting from March 2020, were defined. A total of six posts, two posts from each timeframe, were then analysed. Comments were classified as positive, neutral and negative. The broader positive and negative classes were further subclassified into two classes each. Natural language processing was further applied to obtain results.ResultsThe general trend of the sentiments over the period of pandemic showed a significant and dominant proportion of negative comments that overshadowed the neutral, positive and irrelevant comments over all timeframes. Specifically, the negative sentiments peaked during the second timeframe. The negativity was directed more towards the WHO, governments and people not complying with coronavirus disease 2019-appropriate norms. Positive comments were mostly expressed towards health workers.ConclusionAn unusually high proportion of negative sentiment was observed in response to relatively innocuous public health posts. This may be a result of heightened anxiety, questionable credibility of the sources of information and geopolitical power play maligning the image of the WHO.

A debutanizer column is an integral part of any petroleum refinery. Online composition monitoring of debutanizer column outlet streams is highly desirable in order to maximize the production of liquefied petroleum gas. In this article, data-driven models for debutanizer column are developed for real-time composition monitoring. The dataset used has seven process variables as inputs and the output is the butane concentration in the debutanizer column bottom product. The input-output dataset is divided equally into a training (calibration) set and a validation (testing) set. The training set data were used to develop fuzzy inference, adaptive neuro fuzzy (ANFIS) and regression tree models for the debutanizer column. The accuracy of the developed models were evaluated by simulation of the models with the validation dataset. It is observed that the ANFIS model has better estimation accuracy than other models developed in this work and many data-driven models proposed so far in the literature for the debutanizer column.

Summer Monsoon Rainfall over the Indian subcontinent displays a prominent variability at intraseasonal timescales with 10–60 day periods of high and low rainfall, known as active and break periods, respectively. Here, we study moisture transport from the oceanic and terrestrial sources to the Indian landmass at intraseasonal timescales using a dynamic recycling model, based on a Lagrangian trajectory approach applied to the ECMWF–ERA–interim reanalysis data. Intraseasonal variation of monsoon rainfall is associated with both a north-south pattern from the Indian landmass to the Indian Ocean and an east-west pattern from the Core Monsoon Zone (CMZ) to eastern India. We find that the oceanic sources of moisture, namely western and central Indian Oceans (WIO and CIO) contribute to the former, while the major terrestrial source, Ganga basin (GB) contributes to the latter. The formation of the monsoon trough over Indo-Gangetic plain during the active periods results in a high moisture transport from the Bay of Bengal and GB into the CMZ in addition to the existing southwesterly jet from WIO and CIO. Our results indicate the need for the correct representation of both oceanic and terrestrial sources of moisture in models for simulating the intraseasonal variability of the monsoon.