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.
All articles published by MAUSAM are licensed under the Creative Commons Attribution 4.0 International License. This permits anyone. Anyone is free: To Share - to copy, distribute and transmit the work To Remix - to adapt the work. Under the following conditions: Share - copy and redistribute the material in any medium or format Adapt - remix, transform, and build upon the material for any purpose, even commercially.
Development of synoptic analogue model for quantitative precipitation forecast in the sub-basins of river Krishna
Average areal precipitation (aap) data during south west monsoon season (2012 to 2022) in 10 sub-basins of Krishna river basin were computed and synoptic systems inducing rainfall in the sub-basins were collected. Five synoptic systems-depression/deep depression, low/well marked low (WML) pressure area, Upper air cyclonic circulations (UAC), off-shore trough (OST)/OST with embedded cyclonic circulations, east-west shear zone are considered in the study. Rainfall (AAP) occurrence in the range of 11-25 mm, 26-50 mm, 51-100 mm and > 100 mm due to these systems are considered. OST/OST with embedded cyclonic circulation leads to highest frequency of rainfall in all the sub-basins. Occurrence heavy rainfall (AAP from 26-50 mm) culminating flood in the river basins is mainly due to Off-shore trough/OST with embedded cyclonic circulation at different locations around the river basin. Frequency of 26-50 mm range is more than 51-100 mm in different sub-basins. Shear zone and upper air cyclonic circulations are also contributed significantly to heavy rainfall. Off-shore troughs, upper air cyclonic circulations and East-West shear zone resulted >51-100 mm rainfall in Ghataprabha, Bannehalla, Upper Krishna, Upper Tungabadra and Lower Bhima. Off-shore trough resulted >100mm rainfall in Upper Krishna and Upper Tungabhadra. Upper air cyclonic circulations resulted >100 mm rainfall in Upper Tungabhadra sub-basin. Heavy rainfall >100 mm in maximum number of occasions are due to off-shore trough from Gujarat to Kerala and Karnataka. Number of days for which these synoptic systems contributed rainfall under each range was computed. The rainfall range with highest frequency for the particular system is considered as synoptic analogue model. Validation of synoptic analogue model indicates better performance of the models in different sub-basins and it can be used to improve the accuracy of operational Quantitative precipitation forecast (QPF) issued for flood forecasting.
Copyright (c) 2026 MAUSAM https://creativecommons.org/licenses/by-nc/4.0
Weather in India WINTER SEASON (January-February 2025)
Evaluating the performance of the Flash Flood Guidance System: A Case study from Himachal Pradesh, India
Flash floods are among the most challenging hydro-meteorological hazards to forecast due to their rapid onset, short hydrological response times, and limited warning, particularly in mountainous and densely populated regions. These challenges are being amplified by climate change through increases in short-duration intense rainfall. India is highly vulnerable to such extremes, especially across the Western Himalayan region, where complex terrain and strong monsoon interactions further exacerbate flood risk. To strengthen flash flood preparedness, the India Meteorological Department, in collaboration with the World Meteorological Organization and the Hydrologic Research Center, operationalized the South Asia Flash Flood Guidance System (SAsiaFFGS) in October 2020. The system integrates real-time hydrometeorological observations and numerical weather prediction outputs to generate Flash Flood Risk (FFR) at daily scales and Imminent and Persistent Flash Flood Threats (IFFT, PFFT) within short-term nowcasting windows at the watershed level. This study evaluates the performance of SAsiaFFGS during the extreme rainfall and flash flood event of July 2023 over Himachal Pradesh, a representative Western Himalayan state characterized by steep terrain and short hydrological response times. System generated risk and threat indices are assessed against observed rainfall, soil moisture evolution, and satellite-derived runoff. Results indicate that SAsiaFFGS successfully captured the spatial and temporal evolution of flash flood conditions, providing actionable lead time for operational early warning.
Warning and Dissemination system (Technological Application)
Application of Weather and Climate Services
Extreme Precipitation; Flood Forecasting; Hydrometeorology; Flash Flood; Himalayan region; India
Integrating new GMPE and Seismic b-value for comprehensive hazard analysis in Central, West, and South Sulawesi, Indonesia
Detailed seismic hazard analysis is a critical component in identifying regions with elevated earthquake risk. Consequently, the estimation of seismotectonic parameters (b-value) and the development of ground motion prediction equations (GMPEs) that capture the local characteristics of an area are essential for assessing peak ground acceleration (PGA) in earthquake hazard analysis. The GMPE model is typically constructed using a least squares inversion linear regression approach, while the b-value is estimated through the maximum likelihood method. The findings of this study indicate that the PGA values in the research area range from 0.77 to 8.36 gal, and the b-values fall within the interval of 0.11 to 1.86. The areas with high PGA values are observed in the vicinity of active faults, which can facilitate the occurrence of large earthquakes, as well as regions with weaker geological conditions. Furthermore, the low b-value results suggest the potential for stress accumulation in certain areas, which may lead to the occurrence of moderate to large magnitude earthquakes.
Reconstruction of Paleo-glacier equilibrium line altitudes during the last glacial maximum in the Nubra-Shyok valley, Ladakh
Reconstructing paleoclimatic conditions in the westerlies-dominated Karakoram is essential for understanding the response of high-altitude arid regions to global climate variability. This study presents a reconstruction of the Last Glacial Maximum (LGM) extents of 15 glaciers in the Nubra–Shyok valley, eastern Karakoram, using preserved glacial geomorphology in combination with the GlaRe model. The valley, situated within the influence zone of the mid-latitude Westerlies, provides an exceptional setting to assess westerlies-driven glacial behavior. The reconstructions indicate that the main Siachen trunk glacier (NS1) covered an area of approximately 692 km², with modeled ice thicknesses ranging between 200 and 400 m, reaching a maximum of about 370 m. Equilibrium Line Altitudes (ELAs) were estimated using a weighted average of four established geomorphometric approaches (AABR, AAR, MELM, and THAR). The results indicate a marked ELA depression of ~ 700 m for the Siachen glacier during the LGM. Based on a locally derived lapse rate, this ELA depression corresponds to the lowering of summer temperature by 1.5–2.0 °C, suggesting that enhanced westerlies-induced precipitation moderated the regional expression of global LGM cooling. A distinct spatial ELA pattern is also evident: east-facing glaciers show significantly higher values than west-facing glaciers. This asymmetry reflects the combined effects of westerlies-controlled moisture distribution and differential solar insolation influencing ablation processes. These findings provide robust quantitative constraints on the extent of LGM glaciation and the prevailing paleoclimatic conditions in the valley, highlighting the primary influence of mid-latitude westerlies in modulating glacier dynamics and temperature regimes across this high-elevation region of the Himalaya.
Wind pattern analysis at Bandaranaike international airport (2019-2023): temporal variability and dominant wind directions
This study focuses on the analysis of wind patterns at Bandaranaike International Airport, which is a key factor in flight safety and efficiency. Wind speed and direction have a significant impact on flight operations. Especially during take-off and landing. The objective of this research is to provide a comprehensive understanding of wind behavior over a five-year period (2019–2023) using observational data from airports. These findings provide valuable insights to improve aviation safety. Increase efficiency in using the runway and improving operational planning at Bandaranaike International Airport. The study also emphasizes the importance of accurate weather forecasts to reduce risks associated with adverse weather conditions in aviation. The consequences show that the northeast monsoon dominates within the early and past due months of the year, whilst the southwest monsoon prevails from May to September. Wind speeds are typically among 3–7 knots, with the very best prevalence at some point of the northeast monsoon. The southwest monsoon brings stronger winds predominantly from the southwest, whilst the northeast monsoon introduces enormously calmer winds from the northeast. The findings provide valuable information for aviation operations, including insights for runway utilization, flight safety, and operational planning. By establishing a detailed wind profile, this study enhances local meteorological knowledge, contributing to the development of predictive models and supporting the accuracy of short- to medium-term wind forecasts. This analysis ultimately aids in improving airport management and operational efficiency under varying wind conditions.
Rainfall trends and their influence on crop production in Tamil Nadu, India - A 33-year analysis
The purpose of this study was to investigate the effects of annual and seasonal rainfall on crop productivity in Tamil Nadu. From the Tamil Nadu Season and Crop Report for the years 1990 to 2023, the long-term data of annual rainfall, seasonal rainfall, crop production, yield, crop sowing, and harvesting report was obtained. Mann-Kendall and Theil-Sen trend analysis results for seasonal and yearly rainfall over 33 years revealed positive trend patterns, except for NE monsoon. The investigation reveals that the distribution and quantity of rainfall are higher in NE monsoon than in other seasons in Tamil Nadu. Tamil Nadu receives 50 % of rainfall in NE monsoon. The study reveals that the rainfall increases with latitude in NE and SW monsoon. The SW monsoon brings more rainfall than the NE monsoon over Coimbatore and the Nilgiris. The annual rainfall is higher than 500 mm in all years from 1990 to 2023. Tamil Nadu has experienced 5, 22, and 6 excess, normal, and deficit rainfall years during 1990-2023. The annual rainfall is more on land than on the coast. Nevertheless, during the Northeast monsoon, coastal areas receive more rainfall than land areas. Crop production is positively associated with the rainfall than that at groundwater. The functional relations have been established for crop production with rainfall and yield. A significant correlation exists between them with high R-squared, varying mostly from 0.7-0.96, except for cumbu, red gram, Bengal gram, and gingelly. There is a strong relationship between the amount of rainfall and the crop-planting season. Planting these crops at that particular season can thereby increase crop productivity and yield on rain-fed agricultural land. These study findings may enhance the understanding of rainfall's influence on crop production more effectively and increase crop yield through the utilization of water resources in Tamil Nadu.
Regional teleconnections of ENSO, IOD events, and rainfall trends in Central India
The study investigates teleconnections of ENSO (El Niño Southern Oscillation), IOD (Indian Ocean Dipole) events to rainfall trends. Spatiotemporal rainfall analysis for 50 years (1971–2020) is carried out. Trend analysis for 50 (1971–2020) and 30 (1991–2020) years using four Mann-Kendall (MK) tests, based on MK statistics (Z value), is carried out for six districts of Nagpur division of central India. Theil-Sen's slope estimator is used to compute trend magnitudes. Out of four tests, the Yue and Wang Modified MK test provides the most significant result in Z values. Trend analysis of 50 years shows that June had no trend, July and September had an increasing trend, while August had a decreasing trend in some districts. The impact of ENSO (El Niño and La Niña) and IOD (positive and negative IOD) events on ISMR (Indian Summer Monsoon Rainfall) has been investigated using spatiotemporal variation, rainfall deficiency, and correlation analysis. The response of vegetation to ENSO and IOD events was used for validation purposes. Results indicate that El Niño events are associated with drier conditions in Low NDVI (Normalized Difference Vegetation Index) areas, whereas positive IOD events are associated with wetter conditions (High NDVI area) in Nagpur division. La Niña and negative events show both drier and wetter conditions (Low and high NDVI). The IOD events show a significant correlation with ISMR compared to ENSO events in the study area.
POST MONSOON SEASON (October-December 2024)
Copyright (c) 2025 MAUSAM https://creativecommons.org/licenses/by-nc/4.0
Analysis of Summer monsoon rainfall: Sustainability of long-term modelling by machine learning methods
Indian monsoon is a distinct meteorological phenomenon owing to its importance in the socio-economical context in the Indian subcontinent. Analysis of available Indian and regional data sets spanning over hundred years gives critical insights into various aspects of rainfall. Two major characteristics namely the trend and break points have been analyzed for Indian summer monsoon rainfall (ISMR) and Kerala summer monsoon rainfall (KSMR). Kerala is selected because of it being the point of onset of monsoon in India. The analysis of trend is investigated with a null hypothesis of there being no trend at the 95% confidence level for both the ISMR and KSMR. Prediction of seasonal rain is vital for overall planning and governance. Machine learning models are statistical models that are proven tools for accurate prediction of meteorological phenomena. However, it is yet to be investigated if the predictions by such models stand the test of time. In the present study we have developed machine learning models to investigate if the predictions reflect the long-term trend and if the predicted seasonal time series of KSMR reflect the same parametric values in the intervals of observed break points. The positions of break points in the observed and predicted time series are analyzed to get a fair estimate of model’s capability to detect the break points independent of training on specific break points. Autocorrelation analysis of the seasonal monsoon is done to find the best predictors. The results are encouraging and demonstrate that trends, break points and the parametric values may be sustained for several decades within statistical acceptability.
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.
Flood risk reduction in a river basin under climate change
A flood and its occurrence is a complex interaction between environmental, socio- economic and physical factors. climate change is an imminent witness of extreme events occurring more frequently and floods have emerged as one of the pressing challenges in our society. The current research study strives to simulate extreme streamflow on a daily time scale by adopting hydrologic and hydraulic modeling in order to assess the flood risk in a changing climate. This research was applied to the lower Pennaiyar and Gadilam sub-basins located in the state North eastern side of Tamil Nadu state, India. The efficiency of the HEC-HMS model was precisely examined for each flood events during calibration (2015, 2017, 2018, 2020, and 2023) and validation (2015, 2018, and 2020) stages using the coefficient of determination R2. The R2 and NSE values for the three major flood event 2015, 2018 and 2020 are found to be 0.88, 0.83, 0.86 and 0.85, 0.8, 0.82 respectively. The R2 and NSE values in the results reveal that the calibrated model performs well in the validation. The EC-EARTH 3 climate model of CMIP6 under the SSP2-4.5 and SSP5-8.5 scenarios is employed for future flood simulations. The EC-EARTH 3 GCM data was utilized to generate hydrographs pertaining to different return periods. The hydraulic modeling through the HEC-RAS 2D model enabled the development of flood inundation maps for future SSP scenarios under 25, 50, 100 year return periods. An integrated approach of combining hydrologic and hydraulic modeling with risk reduction strategies using the HEC-RAS terrain modification tool was adopted in this study. The flood estimates are used as the basis for the planning and implementation of suitable flood control measures and flood water conservation structures. The results revealed in this research provide valuable insight for decision-makers.
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.
Rainfall distribution patterns in South Sulawesi during the El Niño and La Niña periods 1995-2020
South Sulawesi, with its diverse geography and climate, experiences fluctuations in rainfall that can have a significant impact on the agricultural sector and people's lives. The main factor affecting rainfall patterns in this region is the El Niño Southern Oscillation (ENSO) climate phenomenon, namely La Niña and El Niño. This study aims to analyze the distribution pattern of rainfall in South Sulawesi during the period 1995-2020 with a focus on the occurrence of El Niño and La Niña phenomena. The determination of distribution patterns and rainfall anomalies was analyzed descriptively. Based on the results of the analysis shows a striking variation between the western, central and eastern regions. In December-February, rainfall in the western region is high, while the eastern and central regions are low. Peak rainfall occurs in January. In March-May, the eastern region has increased rainfall while the western and central regions have decreased. In June-August there is a decrease in rainfall in all regions, indicating a dry season. In October-November rainfall begins to increase. The influence of La Niña is seen in September, with varying impacts. During weak La Niña, the western region partly experiences increased rainfall, while the western region is stable and the eastern and central regions are stable. During moderate La Niña, the western, central and eastern regions are stable. When La Niña is strong, it has a significant effect on the entire South Sulawesi region. In July, the effect of weak El Niño on rainfall is strong in all regions, while during moderate and strong El Niño, there is no significant impact.
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.
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.
A Comparative study of the effects of the Indian ocean dipole and the El Niño in 2019 and 2023 on agricultural drought in Karawang Regency, Indonesia
This study examines the effects of El Niño and the Indian Ocean Dipole (IOD) in 2019 and 2023 on drought conditions in rice fields in Karawang Regency. Both IOD and El Niño influence agricultural drought indirectly through meteorological drought. The IOD in 2019 was classified as extreme, while the El Niño in 2023 was considered strong. The Oceanic Niño Index (ONI) and Dipole Mode Index served as indicators for El Niño and IOD. Meteorological drought was evaluated using the Standardized Precipitation Index (SPI), while agricultural drought was measured with the Normalized Difference Drought Index (NDDI). The results revealed that the most severe meteorological droughts in both years occurred in August and September, with the 2023 drought being more widespread due to elevated IOD and ONI indices. There was a two-month delay between these droughts and the related IOD and El Niño events. Agricultural drought showed significant variation between 2019 and 2023, with 2019 experiencing an earlier and more extensive drought in the northern and central regions, while 2023 saw a later and more intense drought in specific locations. This agricultural drought lagged behind the meteorological drought by about two (three) months in 2019 (2023), likely due to differences in soil water retention, peaking in October of both years after a decline in July of 2019 (August in 2023) influenced by reservoir-fed irrigation.
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.