MAUSAM (Formerly Indian Journal of Meteorology, Hydrology & Geophysics), established in January 1950, is the quarterly international research journal brought out by the India Meteorological Department (IMD). MAUSAM is a medium for publication of original scientific research work. MAUSAM is a premier scientific research journal published in the fields of Meteorology, Hydrology & Geophysics.
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Weather in India WINTER SEASON (January-February 2025)
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POST MONSOON SEASON (October-December 2024)
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Assessment of frost events on the tropical highlands: Analyzing meteorological conditions and wrf-arw model performance on the Dieng plateau, Indonesia
This study investigates frost formation on the Dieng Plateau and evaluates the Weather Research and Forecasting model with Advanced Research WRF core (WRF-ARW) in simulating these events. The primary goal is to identify atmospheric conditions leading to frost and assess the performance of two parameterization schemes within the WRF-ARW model: the Tropical Physics Suite (TPS) and a scheme tailored for tropical frost prediction, referred to as the Bussoni scheme. Meteorological data from the Batur Automatic Weather Station were analysed for eight frost events between August and September 2023. Key parameters such as surface air temperature, dew point, relative humidity, and wind patterns were examined. The study identified critical thresholds for frost occurrence during night-time, including temperatures near or below 4°C, dew points close to air temperature, relative humidity above 95%, and wind speeds below 2 m/s. Comparative analysis revealed that the Bussoni model marginally outperforms the TPS in simulating temperature and dew point, indicated by lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values, and a higher Index of Agreement. The TPS, however, showed a slight edge in predicting relative humidity and wind speed, with lower RMSE and higher correlation coefficients. Both models faced challenges in accurately predicting wind direction, highlighted by high RMSE and low correlation, which underscores the complexity of modelling wind patterns in mountainous terrain.
Impact of Arabian Sea warming on trough dynamics and flooding intensification over the Dubai flood 2024
Understanding climate extremes is vital for enhancing forecasting capabilities and minimizing the risk to lives and property. On 16 April 2024, the arid desert city Dubai in the United Arab Emirates witnessed an unprecedented extreme rainfall of ~250 mm of precipitation in 24hrs. The sudden downpour triggered extensive flooding throughout the city, impacting infrastructure, transportation, water systems, and socio-economic activities. Among the disruptions, Dubai International Airport, one of the world’s busiest, experienced significant delays and flight operations persisted around the three-day period due to the heavy rainfall. This study investigates the atmospheric conditions responsible for this event, focusing on both large-scale and synoptic-scale drivers, as well as the influence of regional oceanic conditions, particularly the warming of the Arabian Sea. Findings reveal that the convective event was driven by strong low-level convergence and anomalous moisture transport from the Arabian Sea, Red Sea, and Persian Gulf. Warmer sea surface temperatures (>1.2 C above normal) in the Arabian Sea have contributed to increasing lower-to-middle atmospheric moisture. Prolonged vertical wind shear was sustained by a severe cyclonic circulation anomaly in the lower troposphere and a notable mid to upper level troughs also created by the upper level anticyclonic and cyclonic patterns from the geopotential height at 200 hPa. These features, combined with enhanced upper-level subtropical jet streams, provided both dynamic and thermodynamic support for sustained convective development. The vertical alignment of these atmospheric processes throughout the troposphere sets the stage for deep cloud formation and intense rainfall. Overall, this study highlights the combined influence of ocean-atmosphere interactions, particularly Arabian Sea warming, in driving extreme weather events over the Arabian Peninsula.
A 3-decade advancements in prediction of tropical cyclones and other severe weather over India: A recap
India has been witnessing frequent and deadly extreme weather events in recent years. These events are becoming increasingly complex due to the compound effects of climate change, urbanization, and land-use land-cover changes, making their accurate prediction a major challenge for both research and operational communities. Numerical Weather Prediction (NWP) has been a vital tool in providing the early warnings, thereby helping to reduce the damage to properties, minimize adverse impact on human life, and limits the country’s economic losses. This review summarizes progress in NWP research over the past three decades, with a focus on improving forecasts of weather extremes (tropical cyclones and associated storm surge, thunderstorms, heatwaves, and urban rainfall) affecting India. These advancements have been made possible through continuous R&D efforts and the support of India Meteorological Department (IMD) in increasing the observational network, severe weather monitoring, and providing timely assistance to the research community in advancing NWP capabilities and reached satisfactory prediction skills that enhanced the reliability in the decision support systems.
Tropical Cyclone ‘Biparjoy’: a blessing in disguise for the Disastrous heat wave event of June 2023 over India
The June 2023 heat wave over eastern and northern India was driven by a complex interplay between synoptic-scale meteorological patterns and tropical cyclone activity. A persistent mid-to-upper tropospheric ridge over the region during 14–18 June led to strong subsidence, clear skies, and stable atmospheric conditions, fostering intense surface heating. Concurrently, Extremely Severe Cyclonic Storm (ESCS) Biparjoy developed over the northeast Arabian Sea and made landfall over Gujarat on 15 June. Prior to landfall of Biparjoy, strong northwesterly winds at lower levels, advected hot, dry air from arid regions into the eastern Indo-Gangetic Plains, intensifying heat wave conditions. The event was marked by both extreme daytime temperatures and elevated nighttime minima, reducing nocturnal cooling and heightening heat stress. Following landfall, the remnant circulation of Biparjoy disrupted the prevailing northwesterlies and weakened the ridge pattern, resulting in reduced hot air advection and a 3–6°C drop in maximum temperatures across affected regions. This evolution led to the gradual abatement of heat wave conditions over northwest, central, and eastern India. The case highlights a notable contrast in the role of tropical cyclones by origin: while Bay of Bengal systems may tend to intensify heat waves, Arabian Sea cyclones such as Biparjoy may moderate them post-landfall. These findings emphasize the importance of cyclone origin, trajectory, and interaction with continental systems in modulating heat extremes, and advocate for integrating tropical cyclone forecasting into heat wave preparedness strategies.
Elemental characteristics of atmospheric aerosols using Raman Spectroscopy: a case study from Doon valley during Covid-19 pandemic
This study presents a Raman spectroscopic investigation of total suspended particles (TSP) collected from Wadia Institute of Himalayan Geology, Dehradun Campus, during the COVID-19 lockdown period (April to September 2020). The lockdown created a rare low-emission atmospheric condition, offering an ideal opportunity to examine the chemical nature of ambient aerosols in the absence of usual anthropogenic interference. Raman micro-spectroscopy was used to analyze the Polytetrafluoroethylene (PTFE) filter strip of aethalometer AE51, providing molecular-level insights into their carbonaceous and organic composition. Spectra collected during the pre-monsoon months (April-May) were dominated by well-resolved D (~1350 cm-1) and G (~1580 cm-1) bands, indicating the presence of disordered and graphitic black carbon (BC) from combustion sources. In contrast, spectra from the monsoon months (June-August) exhibited broadened bands with enhanced contributions from oxygenated and aliphatic organic compounds, as evidenced by peaks in the 1700-3100 cm-1 range. These features reflect secondary organic aerosol (SOA) formation through aqueous-phase and photochemical aging processes under high humidity. By September, the onset of post-monsoon conditions led to a resurgence of combustion-derived carbon signatures, mixed with aged oxidized organics. The Raman spectral evolution across seasons reveals the influence of meteorology and emission dynamics on aerosol composition. Pre-monsoon aerosols were dominated by primary BC, monsoon aerosols by aged organics, and post-monsoon aerosols by a combination of both. This work highlights the utility of Raman spectroscopy for molecular fingerprinting of aerosols and provides a seasonal reference for understanding aerosol aging and source transformation processes in the Himalayan foothills.
Key factors behind super cyclone Gonu (1–7 June 2007) over the Arabian Sea
Super Cyclone Gonu (1–7 June 2007) was the first recorded Category 5 tropical cyclone in the Arabian Sea and one of only five Super Cyclonic Storms reported by the India Meteorological Department over the past 35 years. Forming shortly after the onset of the Indian Summer Monsoon, Gonu’s rapid intensification during a typically unfavorable period for cyclogenesis represents a remarkable deviation from climatological norms. This study examines the atmospheric and oceanic factors that enabled its development using ERA5 reanalysis, NOAA OISST, and IMD Best Track data. Analyses show that anomalously high sea surface temperatures, elevated mid- to upper-tropospheric humidity, and low-to-moderate vertical wind shear created a highly favorable thermodynamic environment. A southward intrusion of midlatitude westerlies disrupted the Tropical Easterly Jet, enhancing upper-level divergence and supporting vigorous deep convection. Vertically integrated moisture transport revealed strong southwesterly inflow into the cyclone core, with concurrent diversion of moisture from the Indian subcontinent, temporarily delaying monsoon progression. Rainfall analyses from GPCP and IMD datasets indicate intense and asymmetric precipitation, with significant orographic enhancement over coastal regions. Organized low-level vorticity and persistent moisture convergence sustained Gonu’s structural integrity until landfall. These findings highlight the interplay of ocean–atmosphere interactions, large-scale circulation, jet stream variability, and precipitation dynamics in early-season cyclogenesis and emphasize the importance of integrated forecasting for extreme tropical cyclones in a changing climate.
CELEBRATING 150 YEARS OF IMD: A CHRONICLE OF WEATHER GUARDIANS AND TECHNOLOGICAL MARVELS
Decoding the dynamics: Interaction of Western disturbances and Easterly troughs driving extreme weather over India
Western Disturbances (WDs) are critical mid-latitude weather systems that influence winter precipitation across northern India. Their interaction with easterly troughs, however, remains less understood, particularly during extreme weather events. This study examines a rare and high-impact synoptic interaction between a WD and an easterly trough during 26-29 December 2024, which resulted in record-breaking rainfall over Delhi and widespread severe weather across northwest and central India. Using multi-source datasets including synoptic charts, satellite imagery, lightning data, and moisture diagnostics (MIMIC-TPW2 and ERA5-derived VIMDF), we decode the dynamic and thermodynamic processes underpinning this event. Our analysis highlights the sequential intensification and eastward progression of the WD and its induced cyclonic circulations, supported by active subtropical jet stream dynamics and strong upper-level divergence. The coupling with an easterly trough led to enhanced low-level convergence and deep convection. Lightning activity and moisture transport patterns confirmed the role of both Arabian Sea and Bay of Bengal sources in fueling the precipitation. The study underscores the significance of synoptic-scale interactions in driving extreme weather and advocates for improved monitoring of such coupled systems to enhance forecasting accuracy. These findings have implications for disaster preparedness and weather prediction over the Indian subcontinent.
Potential impacts of mesoscale assimilation of non-conventional data on rainfall characteristics of Monsoon depressions during Southwest Monsoon
This study deals with the assimilation of satellite radiances in improving the initial conditions of the Advanced Research Weather Research and Forecasting (ARW) model and its impact on the simulation of rainfall and other meteorological features associated with Monsoon depressions (MDs). Eight MDs occurring during 2015-2018 are considered for the study. A set of two numerical experiments: CNTL, which does not consider any data assimilation, and SAT, where satellite radiances are assimilated into the model initial condition, is conducted for each MD case. In this study, satellite radiance data from the sounding instruments Advanced Microwave Sounding Unit (AMSU), Microwave Humidity Sounder (MHS), and Atmospheric Infrared Sounder (AIRS) on polar satellites NOAA 15, NOAA 18, NOAA 19, EOS 2, METOP 1, and METOP 2 are used. The assimilation has been done using the region-specific background error statistics computed for June to August 2017 using the National Meteorological Centre (NMC) method. Improvements in the tracks of MDs after assimilation are seen from 12h to 42h, with a minimum track error of 320 km for SAT runs, in contrast to 400 km for CNTL runs. Winds, mainly at upper tropospheric levels, were simulated well after assimilation, while for lower-level winds, assimilation runs are reliable for a longer range forecast (>24h). The assimilation runs capture both the intensity and spatial spread of precipitation better than the CNTL runs, compared to TRMM precipitation as well as with India Meteorological Department (IMD) station observations. Also, the location and intensity of the maximum precipitation regions simulated in SAT runs are in better agreement with TRMM data than CNTL runs. CNTL runs overestimate the intensity of maximum precipitation with errors from 1.6 cm up to 50.6 cm, while SAT runs underestimate the intensity with errors from -2.6 cm to -19.1 cm. The statistical scores, such as bias, critical success index (CSI), and probability of detection (POD), computed by comparing with GPM data, also highlight the improvement in precipitation forecast of the assimilation runs compared to the CNTL runs. The study presents the significance of satellite radiance assimilation in improving the short-range rainfall prediction.
Decadal Variation of Southwest Monsoon Season Rainfall over Central India under Global Warming
This study examines the decadal variation of rainfall events over the central India region from 1971 to 2020. In the present study, we used daily station-level rainfall data during the monsoon season (June-September) from 1971to 2020. Results showed that during the last two decades, extreme (> 204.5 mm/day)and very heavy (64.5 – 204.5 mm/day) rainfall events have increased alarmingly over Central India, with their peak during the decade 2001 – 2010, followed by 2011-2020. Central India has observed increased heavy rainfall category activity from July 1971 to 2020. A significant increase in rainfall activity was found in Madhya Pradesh in all three rainfall categories. The western coastal state of India - Gujarat, also showed a continuous rise in all categories from 1981 onwards. A similar trend was observed for the eastern coastal state of Odisha due to the excessive moisture supply from the Bay of Bengal. Of all the rainy-day occurrences, both heavy to very heavy rainfall events and extreme rainfall are highest in Gujarat during July and August. The extreme rainfall events were more pronounced in coastal states (Gujarat and Odisha) than the inland states like Jharkhand and Chhattisgarh. At the same time, increase in the number of depressions/cyclones was found to be increased in the last two decades. For agriculture and water management, it is essential to accurately monitor the day-to-day rainfall variability and extreme rainfall cases in a densely populated country like India.
From flames to recovery: ecosystem resilience in Uttarakhand’s 2022 forest fires
Foothills of the Himalaya are one of the major hotspots of forest fire in India. Forest fire in the hot-dry summer season (March-June) often causes significant damage to the Himalayan Forest ecosystem – one of such caused in Uttarakhand state of India in 2022. Despite the 2022 forest fire heavily destroyed the natural vegetation and caused significant economic losses, it’s drivers, impact on vegetation, and vegetation resilience are not well understood. Using several hydroclimatic, vegetation, and fire datasets, we thoroughly examined the spatial and temporal variation of burnt vegetation and its recovery during the 2022 forest fire in Uttarakhand. Differential normalized burnt ratio (dNBR) indicated that high-severity burns were concentrated in the southern regions of Uttarakhand, with widespread moderate to low-severity areas (early stages of regrowth). The fire caused a 16.75% loss in biomass, followed by a 40.85% post-fire recovery, resulting in a net 14.35% gain, highlighting strong but uneven ecosystem resilience. Extreme heat and dryness from February to June, marked by high VPD and low soil moisture, intensified fire risk across Uttarakhand. Monsoon onset and possible fire-induced rainfall helped restore soil moisture and reduce VPD, supporting early vegetation recovery. The satellite observation indices observed dynamic restoration immediately after the fire event, showing SAVI (~0.23 – 0.43) and VCI (~40 - 80), indicating secondary succession. Later, during the monsoon season, the greenness gradually restored was identified over different vegetation indices. Moreover, this variation was also evidently observed in the carbon pools. The long-term assessment was performed using GPP and ET, which are crucial for vegetation restoration. This study highlights the importance of assessing and understanding the vegetation dynamics. Providing key insights into the supporting atmospheric conditions and internal characteristics of vegetation recovery offers guidance for adapting to forest fire impacts.
Heatwave research in India: understanding current status, trends and future directions
Research on natural hazards such as heatwaves has increased in recent decades due to their impact on human life, with the majority of publications occurring after the 2010 deadly heatwave. Based on the Web of Science database, we statistically analyze the research trends, collaborating countries, and institutions using a systematic method of bibliometric analysis. The study reveals the temporal publication trends, co-authorship networks, citation patterns, and keyword evolution in this rapidly growing field. The results highlight the significant contributions of Indian researchers and institutions to the global understanding of heatwaves, as well as the strong international collaborations fostering this research. India has the highest level of cooperation with other countries, whereas the USA ranks second. Despite the rise in heatwave-related deaths, the medical and public health institutions could not make in the top 10 contributors, indicating a gap between health impact and research priority. These findings underscore the need for interdisciplinary research on heatwaves, particularly on region-specific heatwave plans and strengthening collaboration between institutions. We also analyze the temporal keyword trend in three time periods, highlighting the differences and similarities in keywords employed in heatwave research. We found that 'temperature', 'heat waves', 'impact', and 'variability' have the highest occurrence. In the last three decades, the trends have shifted from the broad term of 'climate change' to the use of specific technical keywords such as 'land surface temperature', 'interannual variability', and 'CMIP6'. Furthermore, the paper identifies key research priorities and knowledge gaps, such as the need to focus on adaptation strategies, social vulnerability, and regional impacts. These are critical for developing effective heatwave management policies in India to achieve SDG targets and heatwave resilience.
Impact of climate change on potato crop in India: Using multi-model ensemble climate projection
Potato is an important food crop in India, to quantifying the impacts of climate change on potato production is necessary to develop proper strategies to ensure food security under changing climatic conditions. This study employed the validated SUBSTOR-Potato crop model and downscaled climate data from various Regional Climate Models (RCMs) to project the impact of climate change on potato yields for 2040-2069 (mid-future) and 2070-2099 (far-future). Results indicated the increasing trend of maximum and minimum temperature while diurnal temperature range (DTR) shows decreasing trend over the study area. Without elevated CO2, tuber yields could decrease by 3.6% to 20.5%, depending on the scenario and time period. However, with elevated CO2, yields could increase by 28.7% to 36.5% under optimistic conditions and 34.4% to 35.9% under pessimistic conditions. The potato growing season would increase under a warming climate. Overall, the future warming with elevated CO2 concentration across the states showed positive impact (maximum in Madhya Pradesh +36.5%), while without elevated CO2 concentration, a negative effect has been observed over the states except in Madhya Pradesh where +4.6% to +13.5% increase in yield have been observed. However, uncertainties in this study might arise from the choice of different RCMs selection but the results are important for the scientific community and policy makers to develop appropriate adaptation strategies to improve potato production in India.
Application of Weather and Climate Services
Climatic Variability and extremes, crops/cropping system response and system analysis approach for characterization
Study of Annual Rainfall Patterns for North-Western Indian Himalayan Region
Climate change, though inevitable, requires localized understanding of its variability to enable effective adaptation strategies, particularly in vulnerable sectors such as agriculture. This study focuses on the North Western Himalayan (NWH) region of India, encompassing Jammu & Kashmir, Ladakh, Himachal Pradesh, and Uttarakhand-areas that have witnessed substantial climatic shifts, including a 1.6 °C temperature rise over the last century and increased frequency of extreme rainfall events. Using global datasets from the GPCC and the Precipitation Concentration Index (PCI) as a key analytical tool, this research investigates spatio-temporal rainfall variability from 1971 to 2020 across varying altitudes and climate zones of the NWH region. Findings indicate a strong irregularity in precipitation distribution, particularly in J&K and Ladakh, aligning with recorded extreme rainfall events (EREs) such as 1998 J&K cloudbursts and the 2013 Kedarnath floods. The PCI values calculated for these events (e.g., 19.10 for Kedarnath) underscore the role of rainfall irregularity in disaster occurrence. Despite the observed trends, ground-based data gaps and limited infrastructure, particularly in remote or geopolitically sensitive areas, hamper precise monitoring and disaster preparedness. This study emphasizes the urgent need for enhanced climate data collection, improved documentation of EREs, and the development of microclimate-specific adaptation strategies for effective climate resilience in the North Western Himalayan region.
Spatial variability of sensible and latent heat fluxes with convective thermodynamic indices over East India during Pre-Monsoon Season
This study investigates the spatial variation of surface sensible heat flux (SHF) and surface latent heat flux (LHF), along with the Convective Available Potential Energy (CAPE) and Convective Inhibition Energy (CIN), during the pre-monsoon season over east India (states of Odisha, West Bengal, and Jharkhand) for 2001–2021. The results reveal that at 0900 UTC, the mean values of upward SHF increase over the inland regions in the interior parts of east India, ranging from approximately 250 to 300 W/m2. At 2100 UTC, parts of Odisha experience upward SHF, ranging from approximately 1 W/m2 to 5 W/m2, and rest of the study area experiences downward SHF. The LHF variability shows upwards values during the daytime, and very low values during the nighttime, with coastal regions exhibiting upward LHF values, ranging from 10 to 50 W/m2. Mann-Kendall trend (MKT) analysis indicates that at 0900 UTC, southwest and northeast parts of east India show high positive trend values for SHF, approximately 2–6 W/m2 per season/year. LHF exhibits a high positive trend in only a few parts of Jharkhand and West Bengal, with a similar range. At 2100 UTC, lower SHF values are present over the interior regions, displaying mixed trend patterns ranging between -0.2 and 0.2 W/m2 per season/year. SHF–CAPE demonstrates higher positive correlation values, ranging between 0.4 and 0.6 at 0900 UTC in many regions of the study area. In contrast, nearly all regions exhibit a negative correlation at 2100 UTC, with values ranging from 0.2 to 0.4. Inland regions exhibit a positive correlation for SHF–CIN at 0900 UTC, ranging from 0.5 to 0.1, with lower values for 2100 UTC. The LHF correlations with CAPE and CIN are not significant for the study region.
Trends in the Indian summer monsoon after the late seventies
Indian rainfall patterns and their teleconnections have exhibited significant changes following the major climatic shift observed in the late 1970s. The trend analyses of the Indian summer (June through September) monsoon rainfall have followed a statistically significant increasing/decreasing trend in western/eastern India after the late seventies. The increase in surface temperature over northeast Europe is a manifestation of Arctic amplification, warm temperature advection from the North Atlantic, increased solar insolation, and drying of the region, which has led to increased subsidence and tropospheric pressure. The mid-tropospheric subsidence and surface warming over northeast Europe have made it an active centre-of-action for the emulation/propagation of the Rossby wave towards the Eurasian region, having a trough east over the Caspian Sea, followed by massive ridges over east Asia. The penetration of this trough towards the Indian landmass favours deep convection. The recent decades of warming of the tropical Indian Ocean have produced low pressure over the tropical western Indian Ocean and Somalia, which increases the cross-equatorial flow towards the Arabian Sea and decreases towards northern India. This increases moisture flow/convergence towards western India and decreases moisture towards northern India. The interaction of the moisture-embedded cross-equatorial flow with the upper-tropospheric deeply penetrated trough through the Indian landmass breaks out to heavy rainfall over western India, which causes a shift of the monsoon westward.
Performance evaluation of WRF model in simulating a thundershower event of March 2025 in Bhubaneswar, Odisha, India
This study evaluates the performance of Weather Research and Forecasting (WRF) model in simulating a thundershower event that occurred in Bhubaneswar, Odisha valid 18 UTC on 22 March to 00 UTC on 23 March 2025. Simulation was conducted using WRF single moment six-class (WSM6) microphysics, Yonsei University Planetary boundary layer parameterizations to study the storm structure, precipitation, and dynamics. Model simulated outputs are compared with observations including Global Precipitation Measurement (GPM) rainfall data. Results indicate that the model effectively captures the spatial distribution and temporal evolution of the thundershower, although with certain biases in rainfall intensity and timing. The analysis of horizontal divergence and relative vorticity features underscore the necessity for improved microphysical representations in numerical weather prediction models to enhance forecasting accuracy for convective events associated with thundershowers/thunderstorms in tropical and subtropical regions.
Comparative verification of GDAS and NCUM analyses using In-situ Radiosonde observations
This study quantitatively evaluated the performance of high-resolution global analyses generated by the National Centre for Medium Range Weather Forecasting (NCMRWF) Global Data Assimilation System (GDAS) and the Unified Model (NCUM). The growing density of observational networks has contributed to improvements in numerical model performance, but there is a need for rigorous verification of analysis and forecast products to validate and enhance the competency of Numerical Weather Prediction (NWP) systems. A comparative study between GDAS and NCUM analyses was commenced during the 2024 summer monsoon over 12 distinct regions globally based on Commission for Basic Systems (CBS) guidelines. Various verification skill scores were calculated daily, weekly and monthly timescales from two analyses against the radiosonde (RS/RW) observations. Regional variations in model performance are observed, with higher errors in geopotential height and temperature noted for the South Pole (SP), Tropics (TR), Regional Specialized Meteorological Centre (RSMC), and India (IN) regions. Wind components exhibit more significant errors over the SP region. However, the GDAS exhibits lower errors in Pearson correlation and anomaly correlation coefficients. These findings facilitate the inter-comparison of model performance at operational centers worldwide, informing future improvements in NWP systems.
Climate change with special reference to rainfall variability: A Case study of Upper Jambhira basin, Odisha, India
ABSTRACT. Rainfall is a vital source of water for the rural population of the upper Jambira basin. 50% of the cultivable land is rain fed, and fluctuation in rainfall will adversely affect their livelihood. The present research study observes the characteristic variability of rainfall in the upper Jambhira basin of north Odisha. This analysis is based on the daily rainfall data collected for a period of 33 years (1988-2020) from the office of the Special Relief Commissioner (SRC), Govt. of Odisha. The rainfall data has been analyzed to know the annual, monsoonal and decadal trend analysis of rainfall variability, rainfall intensity, extreme and excess rainfall, wet and dry spell, monsoon onset and withdrawal. The analysis shows, the basin experiences an increasing trend of annual and monsoonal rainfall with inter annual and inter decadal variations. Though the basin experiences monsoon surplus rainfall, but annual surplus is very negligible with water scarcity in many years. This study suggests for the conservation of monsoon rainfall for utilization in the scarcity period. It will help in mitigation of the impact of climate change and occurrence of extreme rainfall events in the study area.
Future changes in extreme precipitation and warm days over megacities of India and the semi-arid Tirupati district
We study future changes in extreme precipitation and warm days over selected megacities of India and the semi-arid Tirupati district. This work synthesises projected changes in climate extremes using indices from the Expert Team on Climate Change Detection and Indices (ETCCDI). It utilises gridded rainfall and temperature data from IMD for the historical period and future projections from the NEX-GDDP-CMIP6 dataset under moderate (SSP2-4.5) and high (SSP5-8.5) warming scenarios for 2026-2100. Projections indicate a consistent increase in both mean annual precipitation and the number of heavy rainfall days across the analysed cities, with larger increases under the high-emissions scenario. Changes in consecutive wet days exhibit spatial heterogeneity, displaying complex patterns without a uniform trend across all cities. A consistent and accelerating warming trend is projected across all cities, leading to a significant increase in warm-day frequency throughout the century, particularly under the SSP5-8.5 scenario. For the Tirupati district, annual precipitation is projected to increase under both scenarios, with the southwest monsoon season showing particularly notable enhancement. Substantial warming is also projected across all seasons for Tirupati, with the pre-monsoon season expected to experience the most severe warming. These findings emphasise the critical need for tailored, region-specific adaptation strategies and highlight how future emissions pathways will significantly influence the severity of climate impacts.
A Gaussian and Gamma mixture model approach to Rainfall analysis in drought-prone regions of Karnataka
This research article presents an in-depth statistical analysis of drought-prone regions in Karnataka, focusing on Bagalkote, Chitradurga, Koppala and Raichur. By using historical data and utilizing Gaussian and Gamma mixture distribution models, this study aims to model drought patterns effectively across these regions. Descriptive statistics and exploratory data analysis were conducted to understand regional drought characteristics, followed by the application of Gaussian and Gamma mixture models to capture the variability and intensity of drought occurrences. Parameters within the models were estimated using the maximum likelihood estimation technique to ensure accuracy and robustness in fitting the distribution to the data. The results highlight the comparative performance of Gaussian and Gamma models, demonstrating the mixture model’s to represent drought intensity and frequency across the regions studied. This study offers valuable insights into drought dynamics in Karnataka, contributing to improved drought risk assessment and informing resource management strategies in affected regions.
Century-scale rainfall variation over Madhya Maharashtra, India
IPCC (2007) report says that by the middle of the 21st Century, available water will reduce by 10-30% causing serious scarcity of fresh water. Madhya Maharashtra meteorological sub-division is one of the significant agricultural regions of the country. In the recent years this region has witnessed extremities in rainfall, and demanding an investigation of rainfall variability. The present study analyses long-term rainfall trends for 120 years (1901-2020) using parametric and nonparametric tests. Madhya Maharashtra has an increasing trend in annual rainfall since 1926. In monsoon all the districts registered rising trend, with three of them being statistically significant. In winter, all the districts except Sangli show decreasing trends which were statistically significant for four districts. Significant change points were found in all the temporal scales (monthly, seasonal and annual). Most importantly the Sequential Mann-Kendall test of the monsoon, as well as the annual rainfall of the districts depict that, except Dhule, Nandurbar, Pune and Kolhapur all the other districts witnessed at least one change point during the last decade. Many of these districts registered their latest change point in last 5 years also. So, a question arises from this observation is that, what will be the scenario of rainfall in these districts in the coming decades and how much will that contribute in changing the overall trend in rainfall over Madhya Maharashtra sub-division as a whole. Future climate prediction till 2100 estimates a mild possibility of an increase in the rainfall extremes in the study area.
Rainfall, Trend analysis, Sequential Mann-Kendall test, Mann-Kendall test, Linear Regression.
ROLE OF COMMON ALERT PROTOCOL-BASED WARNING DISSEMINATION SYSTEM AND EFFECTIVE DISSEMINATION METHODOLOGY
Initial assessment of IPW data from SOI-CORS stations over Uttar Pradesh
This study explores the impact of assimilating Integrated Precipitable Water (IPW) data from the Survey of India (SOI) – Continuously Operating Reference Stations (CORS) into weather forecasts using the Weather Research and Forecasting (WRF) model with Gridpoint Statistical Interpolation (GSI) as the assimilation scheme. IPW data from SOI-CORS stations located in Uttar Pradesh and neighbouring regions. The research was conducted over June and July 2024, with a high-resolution WRF model domain (3 km grid spacing) focussing on the rainfall events during the period. Data assimilation was performed four times daily at 0000, 0600, 1200, and 1800 UTC, with forecasts extending up to 72 hours. The study found that assimilating IPW data significantly improved both the analysis and the forecasts of various meteorological parameters. Comparison with radiosonde showed a 2% reduction in Root Mean Square Error (RMSE) of wind components above 600hPa and surface moisture (q) error reduction by 7%, indicating enhanced initial conditions. Significant RMSE reductions were observed for wind forecasts at 200 hPa (7.71% on Day 1) and 850 hPa (4.78% on Day 1). Temperature forecasts at 200 hPa exhibited consistent improvements across all forecast days, ranging from 3.05% to 4.70%. Geopotential height forecasts showed the most substantial improvements, with RMSE reductions of 15.33% at 200 hPa (Day 1), and sustained improvements at 850 hPa (3.28%–5.47%) over multiple days. The results highlight the positive effects of integrating high-resolution IPW data into weather models, especially in terms of improving the accuracy of upper-level atmospheric parameters and overall model performance.
Adaptive neuro-fuzzy inference system for drought modeling
. Drought exerts a significant impact on both the environment and agricultural sectors, particularly in farming. Addressing the impact of drough is essential for all stackholders who depends on natural water sources. Adaptive Neuro-Fuzzy Inference System (ANFIS), one of the hybrid artificial neural networks, is primarily used in this study to model the drought. The study utilizes the monthly precipitation data spanning for last 39 years for the Coimbatore district. Initial steps involve the estimating of Standardized Precipitation Index (SPI) values at a 3-month scale using monthly precipitation values, considering the impact of North-East Monsoon over the district. Several ANFIS forecasting models are developed using the mean precipitation value of North-East Monsoon season and the computed values of SPI. The evaluation of these models incorporates several error metrics like Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R2), allowing for a comprehensive comparison between the projected ANFIS model and observed values. The model which exhibits the lowest RMSE and MAE, coupled with a high R2, are considered as robust fit to the data.
Weather in India MONSOON SEASON (June to September 2024)
Assessing aerosol interactions and re-distribution during very severe cyclonic storm Vardah (2016) in the Bay of Bengal
Utilizing reanalysis and satellite observations, the present study investigates the interactions and redistribution of aerosols during a Very Severe Cyclonic Storm (VSCS) Vardah (6th to 13th December, 2016) in the Bay of Bengal (BoB). The detailed analysis focuses on the effects of aerosols on the tropical cyclone (TC) induced precipitation, including an examination of aerosol loading, changes in their distribution during the passage of the cyclone. As cyclone Vardah matured from a Severe Cyclonic Storm (SCS) to VSCS, a gradual reduction in the Precipitation Rate (PR) was observed, accompanied by an increasing trend of Lower Tropospheric Stability (LTS). Winds from the aerosol-rich north-eastern Himalayan region that were directed toward the cyclone resulted in a significant influx of aerosols into the cyclone. Even more aerosol loading was recorded over the central and western BoB during the SCS and VSCS stages of TC Vardah, respectively. This could be due to the strong drag of winds from the north-eastern Himalayan region towards the cyclone as it approached the coastal region. Investigation of the spatial distribution of aerosols and precipitation rate during all three stages (i.e., Cyclonic Storm (CS), SCS, and VSCS) revealed that the presence of aerosols played a significant role in suppressing precipitation before cyclone Vardah made landfall. Additionally, spatio-temporal anomalies of AOD showed a sharp contrast, with anthropogenic aerosols depleted near the storm core due to wet scavenging, while natural aerosols such as sea salt were enhanced along the storm track, highlighting the cyclone’s dual role as both a cleanser and redistributor of aerosols. Further, our analysis revealed that TC Vardah deposited a significant amount of aerosols over Chennai, bringing it from the ocean. These results make an important contribution to understanding the redistribution of aerosols and their impact on precipitation induced by cyclones over the BoB.
Cyclonic storms & depressions over the North Indian Ocean during 2024