Search

The present study majorly focused on the degree of rainfall alteration (DIRA) using an indicator-based novel approach. Most of the previous works majorly focused on only magnitude parameters for measuring monthly rainfall change; however, alteration may also come from other relevant aspects like frequency, timing, rate of change, and duration of rainfall. The present paper considered 33 indicators to assess alterations in rainfall, encompassing various such aspects. Subsequently, it computed the degrees of rainfall alteration categorized as low, moderate, and high range of variability (RVA). This computation was based on the gridded rainfall data endorsed by the Indian Meteorological Department (IMD). Getis-ord was applied to compute the significance of rainfall patterns. The study revealed that middle RVA-based DIRA is low (< 0.33) in the case of magnitude indicators across IMD-defined meteorological sub-divisions, but moderate DIRA (0.33–0.67) was detected in some parts in the case of rate of change, frequency, duration, and timing indicators. This change was detected rather high (moderate to high) over wider parts of India (six and sixteen sub-divisions) in the case of low and high DIRA. In monsoon and post-monsoon seasons, north-eastern hilly hot spots (95–99% level) were weakened, while it was found enhanced in north-western India. A new rainfall hot spot (95%) was identified in Odisha, Telangana, and Vidarbha transition in 1991–2021. Taking into account the notable success achieved through the IRA approach, the study strongly recommends its application across various related domains. [ABSTRACT FROM AUTHOR]

The Indian economy and food grain production are intricately tied to the spatial distribution and the amount of rainfall during the summer monsoon period (June to September). Numerous studies have delved into modeling methods for forecasting Indian summer monsoon rainfall. This paper utilizes monthly rainfall data spanning 72 years (1950–2021) to analyze, model, and predict the monsoon over Northwest India using Dynamic Linear Modeling (DLM). In view of this, the well-accepted dynamic linear model with significant components is selected from the parsimonious models of DLM based on Bayesian Information Criteria (BIC), significant tests, and statistical fit. The dynamic linear model comprises a significant linear time trend component, a deterministic trigonometric seasonal component, a deterministic cycle component, and a stochastic irregular component has been selected. The validation of the selected model passed through the normal and correlation diagnostics of model residuals. The analysis reveals a subtle decreasing trend in monsoon rainfall, showcasing consistent behaviour in deterministic seasonality, and highlighting the deterministic nature of cyclical component with a period of approximately 2.5 years, alongside irregular variations. Utilizing this model, future monsoon rainfall over Northwest India has been forecasted for the years 2022–2027, providing a 95% confidence interval. [ABSTRACT FROM AUTHOR]

Indian southwest monsoon rainfall has a huge socioeconomic influence, and any changes in monsoon rainfall patterns will have major consequences for agriculture, water supplies, and other connected sectors of economy. The paper explores the charactering and forecasting of the southwest monsoon rainfall (June to September) patterns over the Central region of India for the period (1901–2021: 121 years) using linear state space modeling. The study period is split into two periods, i.e., earlier period (EP: 1901–1950) and the recent period (RP: 1951–2021). The linear state space model (LSSM) with significant latent components such as trend, seasonal, periodic and random variations or noise is selected for modeling the southwest monsoon rainfall (SWMR) based on Bayesian information criteria (BIC) and statistical fit for the two periods. The result indicates that an increase in linear time trend during the EP and the RP has a decreasing local linear time trend in SWMR, and it is interesting to notice that the EP had no noteworthy periodic variations, whereas the RP exhibits significant periodic variations in SWMR. Further, the selected LSSM forecasts SWMR patterns for the period 2022–2028, and it has been observed that the Central Indian region is expected to receive substantial rainfall in the years 2026, 2027 and 2028. [ABSTRACT FROM AUTHOR]

Better understanding on the phases of Indian summer monsoon i.e., onset, progress and withdrawal helps in the decision making and proper management. In this paper, date of onset of monsoon (DOM) and date of withdrawal of monsoon (DWM) is estimated using the long-term daily rainfall data observed by the India Meteorological Department (IMD) and cumulative rainfall anomaly concept, as the onset/withdrawal represents a signature of rainfall transition. The results are promising with a better match between the estimated DOM and IMD announced DOM over Kerala. The length of monsoon season variability is also analysed. The concept of onset at grid point (OGP) in India is also evaluated and progress of monsoon is monitored by considering the latitudinal variation, persistent & strength of rainfall in the continental India. The zonal classification of the spread of monsoon in terms of the intensity and time (duration) is presented. Zone wise pentad analysis of onset clearly signifies the progress of monsoon and its dependency on various physical factors like SST, OLR, TTG, ITCZ, Energy budget and wind circulation. The impact of ENSO on the OGP is brought out, which clearly shows that DOM, DWM and progress is almost influenced by the largescale processes. The intensity–duration–frequency analysis quantified the actual ISM length, seasonal rainfall and rainy days at all locations in India. This study will help modellers in better understanding of onset and progress of monsoon dynamics in India and can be integrated with NWP for the accurate and advance prediction of DOM and the progress. [ABSTRACT FROM AUTHOR]

A declining trend in Indian summer monsoon precipitation (ISMP) in the latter half of the 20th century is a scientifically challenging and societally relevant research issue. Heavy aerosol loading over India is one of the key factors in modulating the ISMP. Using the state-of-the-state-of-the-art chemistry-climate model, ECHAM6-HAMMOZ, the impacts of South Asian anthropogenic sulfate aerosols on the Indian summer monsoon precipitation were investigated against: (1) 2010 La Niña (excess monsoon), (2) 2015 El Niño (deficit monsoon) in comparison to (3) normal monsoon 2016. Sensitivity simulations were designed with 48% enhancement in South Asian SO 2 emissions based on a trend estimated from Ozone Monitoring Instrument (OMI) satellite observations during 2006–2017. The model simulations showed that sulfate aerosols reduce ISMP by 27.5%–43.3 %, while simulations without sulfate loading enhanced ISMP by 23% in 2010 La Niña and reduction by 35% in 2015 El Niño. This paper reports that sulfate aerosols loading over India reduce precipitation by aerosol-induced direct and indirect effects by inducing atmospheric cooling, weakening in the convection, and reduction in moisture transport to Indian landmass. This paper emphasizes the necessity of alternate use of energy to reduce sulfate aerosol emissions to solve water issues in South Asia. [Display omitted] • Sulfate emission reduces Indian summer monsoon precipitation through aerosol-radiation and aerosol-cloud interactions. • Over 27% of the summer monsoon rainfall deficit in a normal year are linked to Asian anthropogenic sulfate emissions. • Sulfate-induced rainfall reduction is the maximum for La Niña year. • SO 2 loading increases atmospheric stability and weakens moisture transport to India. [ABSTRACT FROM AUTHOR]

Climate change is a global phenomenon that has been associated with a growing list of concerns in society today, often leaving more questions than answers. Thus, it is no surprise that questions are forming regarding the effects of climate change on global security, and more specifically, terrorism. India is the ideal case study for investigating the relationship between climate change and extremism, with average temperatures in the country reaching record highs as well as having 9,096 terrorist incidents occur during our 20-year study period between 1998 and 2017. Using daily temperature, precipitation, elevation, and distance to the equator data from the National Climatic Data Center and terrorist incidents from the Global Terrorism Data base (GTD), this study assesses the spatial relationship between these factors through geospatial analyses. Suitability analyses indicate that all the climatological variables tested—temperature, precipitation, and elevation—relate to shifting patterns of terrorist activity. We also found that beyond intensity, seasons result in a shifting of patterns in terrorist behavior to other locales. Implications for the global community and for India specifically are discussed. [ABSTRACT FROM AUTHOR]

Predicting the temporal and spatial patterns of South Asian monsoon rainfall within a season is of critical importance due to its impact on agriculture, water availability, and flooding. The monsoon intraseasonal oscillation (MISO) is a robust northwardpropagating mode that determines the active and break phases of the monsoon and much of the regional distribution of rainfall. However, dynamical atmospheric forecast models predict this mode poorly. Data-driven methods for MISO prediction have shown more skill, but only predict the portion of the rainfall corresponding to MISO rather than the full rainfall signal. Here, we combine state-of-the-art ensemble precipitation forecasts from a high-resolution atmospheric model with data-driven forecasts of MISO. The ensemble members of the detailed atmospheric model are projected onto a lower-dimensional subspace corresponding to the MISO dynamics and are then weighted according to their distance from the data-drivenMISOforecast in this subspace. We thereby achieve improvements in rainfall forecasts over India, as well as the broader monsoon region, at 10-to 30-d lead times, an interval that is generally considered to be a predictability gap. The temporal correlation of rainfall forecasts is improved by up to 0.28 in this time range. Our results demonstrate the potential of leveraging the predictability of intraseasonal oscillations to improve extended-range forecasts; more generally, they point toward a future of combining dynamical and data-driven forecasts for Earth system prediction. [ABSTRACT FROM AUTHOR]

Current knowledge suggests a drought Indian monsoon (perhaps a severe one) when the El Nino Southern Oscillation and Pacific Decadal Oscillation each exhibit positive phases (a joint positive phase). For the monsoons, which are exceptions in this regard, we found northeast India often gets excess pre‐monsoon rainfall. Further investigation reveals that this excess pre‐monsoon rainfall is produced by the interaction of the large‐scale circulation associated with the joint phase with the mountains in northeast India. We posit that a warmer troposphere, a consequence of excess rainfall over northeast India, drives a stronger monsoon circulation and enhances monsoon rainfall over central India. Hence, we argue that pre‐monsoon rainfall over northeast India can be used for seasonal monsoon rainfall prediction over central India. Most importantly, its predictive value is at its peak when the Pacific Ocean exhibits a joint positive phase and the threat of extreme drought monsoon looms over India. Plain Language Summary: Monsoon brings rain over India. But some years are droughts. These drought monsoon years are historically associated with warmer sea surface temperatures (SSTs) in the eastern Pacific and cooler SST in the northern Pacific. This motivated scientists to predict drought monsoons when we observe a warm eastern and cold northern Pacific Ocean. However, in some years, the monsoon is not drought despite the SST anomalies in the Pacific suggesting so. We find that, in such years, rainfall over northeastern India during pre‐monsoon months is often excessive. So we argue that when the Pacific Ocean state suggests a drought monsoon over India (central region) but if pre‐monsoon rainfall over northeastern India is excessive, then we can rely less on the drought signal of the Pacific Ocean. Key Points: Pre‐monsoon rainfall over northeastern India is a potential indicator of false alarms of monsoon drought over central Indian regionAssociation between northeastern India pre‐monsoon rainfall and monsoon rainfall over central India oscillates multidecadallySea surface temperature anomalies in the Pacific are a key driver of pre‐monsoon rainfall over the northeastern India [ABSTRACT FROM AUTHOR]

Introduction: The current research investigates into the application of various thunderstorm indices to predict severe thunderstorm occurrences during the monsoon season across four distinct regions in India. Methods: The study assesses the prediction model's efficacy using various skill scores and the Weather Research and Forecasting (WRF) model has been integrated for 30 h with double moment microphysics scheme NSSL-17 which accurately reproduces vertical and meteorological measures. Objective: Furthermore, it investigates fifteen thunderstorm indices derived from the ERA5 dataset to identify the most effective index for forecasting severe thunderstorms. Results: The results indicate that combining thunderstorm indices with skill scores, such as the Heidke Skill Score and True Skill Statistic, enhances the accuracy of severe thunderstorm predictions in the Indian monsoon season. The accurate predictions rely on determining optimal thresholds for each index. The study emphasizes the importance of using multiple indices rather relying solely on single measure for predicting severe thunderstorms. Advanced indices like the Energy Helicity Index (EHI) and Supercell Composite Parameter (SCP) perform well in forecasting extreme severe thunderstorms due to their strong reliance on wind shears. The EHI (> 1), and SCP (≥ 3.5), STP (≥ 1.2) along with low SRH at 3 km (100 m2/s2), indicated no evidence of helicity or tornado activity during the event. On the other hand, the CAPE, K Index, and VT Index demonstrate robust predictive capabilities for non-severe category thunderstorms. Conclusions: Integrating numerous thunderstorm indices improves meteorologists' forecasts, ensuring public safety. Based on this work, future research can improve severe weather forecasting models' accuracy and reliability. [ABSTRACT FROM AUTHOR]

We present the Monsoon Mission Coupled Forecast System version 2.0 (MMCFSv2) model, which substantially upgrades the present operational MMCFSv1 (version 1) at the India Meteorology Department. The latest 25 years (1998–2022) of retrospective seasonal coupled hindcast simulations of the Indian summer monsoon with April initial conditions from Coupled Forecast System Reanalysis are discussed. MMCFSv2 simulates the tropical wind, rainfall, and temperature structure reasonably well. MMCFSv2 captures surface winds well and reduces precipitation biases over land, except over India and North America. The dry bias over these regions remained like in MMCFSv1. MMCFSv2 captures significant features of the Indian monsoon, including the intensity and location of the maximum precipitation centers and the large-scale monsoon circulation. MMCFSv2 improves the phase skill (anomaly correlation coefficient) of the interannual variation of Indian summer monsoon rainfall (ISMR) by 17 % and enhances the amplitude skill (normalized root mean square error) by 20 %. MMCFSv2 shows improved teleconnections of ISMR with the equatorial Indian and Pacific oceans. This 25-year hindcast dataset will serve as the baseline for future sensitivity studies of MMCFSv2. [ABSTRACT FROM AUTHOR]

We describe the use of regional relaxation ("nudging") experiments carried out in initialised hindcasts to shed light on the contribution from particular regions to the errors developing in the Asian Summer Monsoon. Results so far confirm previous hypotheses that errors in the Maritime Continent region contribute substantially to the East Asia Summer Monsoon (EASM) circulation errors through their effects on the Western North Pacific Subtropical High. Locally forced errors over the Indian region also contribute to the EASM errors. Errors arising over the Maritime Continent region also affect the circulation and sea surface temperatures in the Equatorial Indian Ocean region, contributing to a persistent error pattern resembling a positive Indian Ocean Dipole phase. This is associated with circulation errors over India and the strengthening and extension of the westerly jet across southeast Asia and the South China Sea into the Western Pacific, thereby affecting the ASM circulation and rainfall patterns as a whole. However, errors developing rapidly in the deeper equatorial Indian Ocean, apparently independently of the atmosphere errors, are also contributing to this bias pattern. Preliminary analysis of nudging increments over the Maritime Continent region suggests that these errors may at least partly be related to deficiencies in the convection and boundary layer parametrisations. [ABSTRACT FROM AUTHOR]

Northern India has undergone intense urbanization since the middle of the 20th century. The impact of such drastic land-use change on the regional weather and climate remains to be assessed. In this work, we study the impact of the modification of land use – from vegetation to urban – on the Indian summer monsoon rainfall as well as on other meteorological variables. We use the regional Meso-scale Non-Hydrostatic (Meso-NH) model coupled with an urban module (the Town Energy Balance model) to perform monthlong sensitivity simulations centered around Kolkata, the most urbanized area in northeastern India. Paired simulations, one with and another without urban settings, have been performed to identify the impacts related to urbanization through both thermodynamic and kinetic effects. We find that the perturbation induced by urban land use enhances the mean rainfall over the model domain, principally by intensifying the convective activity through thermodynamic perturbation, leading to a 14.4 % increase in the monthly mean rainfall. The urban area also induces a 15.0 % rainfall increase during two modeled periods of heavy precipitation caused by low-pressure systems. In addition, the modeling results demonstrate that the urban area not only generally acts as a rainfall enhancer, particularly during nighttime, but also induces the generation of a specific storm in one modeled case that would not have formed in the absence of the urban area. The initiation of this storm over the city was primarily due to the urban terrain's disturbance of the near-surface wind flow, leading to a surge in dynamically produced turbulent kinetic energy (TKE). The thermal production of TKE over the nighttime urban boundary layer, on the other hand, serves as a contributing factor to the storm formation. [ABSTRACT FROM AUTHOR]