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 MONSOON SEASON (June to September 2025)
Copyright (c) 2026 MAUSAM https://creativecommons.org/licenses/by-nc/4.0
Cyclonic storms & depressions over the North Indian Ocean during 2025
During 2025, in total, 15 intense low pressure systems formed over the North Indian Ocean. These include: two severe cyclonic storms (SHAKHTI & MONTHA), two cyclonic storms (SENYAR & DITWAH), 2 deep depressions. and 9 depressions. Out of these 15 systems, 7 systems formed over the Bay of Bengal (BoB), 3 over the Arabian Sea (AS), one in the Strait of Malacca and 4 overland. Both the severe cyclonic storms (one over the BoB and the other over the AS formed in the post-monsoon season. Monsoon Season witnessed one deep depression over the AS and and 6 depressions with 3 each over the BoB and the land. Two cyclonic storms formed during the post-monsoon season, out of which one formed over BoB and the other one over the Strait of Malacca.
WEATHER IN INDIA Hot Weather Season(March-May) 2025
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.
An LSTM based model for the prediction of INSAT-3DR satellite images for nowcasting
The demand for accurate and early warning systems for severe weather occurrences is increasing due to the growth in both the frequency and intensity of extreme meteorological phenomena. Accurate advance prediction of such catastrophic events remains a significant challenge for weather forecasters, making disaster mitigation difficult. To enhance nowcasting accuracy, multiple data sources including meteorological (radar, satellite, and surface observations) and geographical information (elevation, exposure, vegetation, hydrological characteristics, and anthropogenic features) are increasingly integrated into forecasting frameworks. This study, presents a deep learning–based architecture employing Long Short-Term Memory (LSTM) neural networks for short-term satellite image prediction using the INSAT-3DR data, which plays a crucial role in weather nowcasting applications. Experimental evaluation is carried out using the Thermal Infrared-1 (TIR-1) channel. The LSTM-based framework processes satellite imagery by randomly segmenting images into pixel blocks of 5 × 5, 20 × 20, and 50 × 50 resolutions. Three model variants CNN–LSTM, Bidirectional LSTM, and Vanilla LSTM are implemented to predict future satellite images at lead times of 30 minutes, 1 hour, 1.5 hours, and 2 hours. The best-performing model for the TIR-1 band achieves an average normalized mean absolute error of approximately 1.40%, demonstrating high prediction accuracy and confirming the effectiveness of the proposed approach for INSAT-3DR satellite image nowcasting.
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
Performance profiling and optimization of the NCMRWF’s unified model (NCUM) vn13.0 on the MIHIR cray XC40 HPC facility at NCMRWF
The National Centre for Medium Range Weather Forecasting (NCMRWF) operated the MIHIR High Performance Computing (HPC) Facility, delivering up to 2.8 petaflops of processing capacity to run Numerical Weather Prediction (NWP) models, thereby enabling accurate and timely weather forecasting. This study presents a comprehensive performance profiling and optimization analysis of the NCMRWF Unified Model (NCUM) version 13.0 on the MIHIR Cray XC40 HPC Facility. The model requires computations in the order of peta floating point operations per second (PFLOPS). The NCUM is profiled at two horizontal resolutions n96e (approximately 130 km) taken as baseline and n1280e (approximately 10 km) using the Cray Performance Analysis Tool (CrayPAT) to identify runtime bottlenecks and communication overhead. The optimization experiments focused on halo size reduction, domain decomposition strategies, OpenMP threading, and MPI rank reordering. Results demonstrate that reducing the extended halo size from 10 to 5 grid points improves execution time by approximately 10 seconds and thus reduces communication costs and lowers imbalance in key routines from 15.2 percent to 3.8 percent. A change in domain decomposition from 4 by 8 to 3 by 12 further reduced MPI collective imbalance, while rank reordering improved execution time by up to 12.8 percent. These findings will be valuable in guiding domain and communication optimizations to efficiently scale high resolution NWP models on the Cray XC40 system and future HPC architectures to be deployed at NCMRWF.
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.
Geological structure identification and derivative analysis methodwith lithological inverse modelling in Mount Gede-Pangrango geothermal prospect area using gravity data
The geothermal prospects of the Gede–Pangrango volcano complex in West Java, situated along the Pacific Ring of Fire, exhibit significant potential indicated by surface manifestations such as solfataras, fumaroles, and hot springs. Previous studies have reported the presence of a high-temperature reservoir (290–300°C) within Quaternary volcanic rocks at depths of 2000–2800 meters; however, uncertainties remain regarding the subsurface structures and faults that control the geothermal system. This study employs satellite-derived gravity data (GGMPlus) and elevation data (ERTM) to investigate these features. Edge-detection filters, including the First Horizontal Derivative (FHD) and Second Vertical Derivative (SVD), were applied to delineate subsurface structures associated with geothermal activity. The residual anomaly map reveals three primary zones: high, moderate, and low anomalies. High anomalies are concentrated around Mount Gede and Mount Pangrango, reflecting dense volcanic rocks or shallow intrusions, whereas low anomalies correspond to pyroclastic deposits and hydrothermally altered rocks. FHD and SVD analyses highlight northeast–southwest (NE–SW) trending faults that serve as primary pathways for geothermal fluid migration, confirmed by the alignment of fractures with hot springs and the Kawah Ratu crater. The 2D cross-sectional model indicates a low-density reservoir at depths of 1–2 km controlled by a major fault, underlain by intrusive rocks acting as the heat source. Furthermore, the 3D density modeling demonstrates a connected geothermal system between Mount Gede and Mount Pangrango, characterized by high-permeability zones beneath both volcanoes.
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.
Analysis of the trend of heavy rainfall over Uttarakhand, India
Uttarakhand state receives around 79% of the total annual rainfall during the southwest monsoon season, i.e. from June to September. In this study, precipitation data of the monsoon season from 1983 to 2023 of four departmental observatories of the India Meteorological Department (IMD) in Uttarakhand have been used to analyse the trend of frequency of Heavy, Very Heavy and Extremely Heavy rainfall. Tehri & Mukteshwar observatories represent the hilly region of Uttarakhand, while Dehradun & Pantnagar observatories represent the plains of Uttarakhand. Heavy rainfall climatology reveals that the plains of Uttarakhand receive a higher frequency of heavy rainfall days than the hilly stations. The state experiences higher frequency and lower variability of heavy rainfall days during August & July months, followed by September & June. Mann-Kendall non-parametric trend test has been used to evaluate the existence of monotonic trends. The results show a weak, statistically insignificant increasing tendency in the number of heavy and more rainfall days in Dehradun station, while no trend is observed in other stations. Quantitatively, a non-significant rise in the percentage of extreme rainfall to the total monsoon rainfall is observed over Dehradun, Pantnagar & Mukteshwar stations, while Tehri exhibits a non-significant fall in the extreme rainfall quantity. As per the IMD criteria, the 24-hour accumulated rainfall is categorized into Very Light, Light, Moderate, Heavy, Very Heavy & Extremely Heavy. The trend analysis of individual categories of rainfall shows a significant increasing trend in very light rainfall days over three stations (Dehradun, Mukteshwar & Pantnagar) and a non-significant decreasing trend of dry days over three stations (Dehradun, Mukteshwar & Tehri). The other rainfall categories show no trend.
Impact of temperature and rainfall variability on agriculture and horticulture crops in the North-Western Indian Himalaya
Climate variability, particularly fluctuations in temperature and rainfall, showed significant impacts on the agricultural sector, especially on cropping systems critical to ensuring food security. A comprehensive study assessed the influence of both positive and negative climate variability on agriculture and horticulture in the Sirmour district of Himachal Pradesh. This investigation analyzed the complex relationship between climate variables and crop productivity over 27 years (1995–2021), providing insights crucial for developing climate-resilient farming strategies. The statistical tools and models were employed to evaluate long-term trends in key climatic variables, with a focus on determining the significance and rate of observed changes. Over the study period, a significant decline in both maximum and minimum temperatures during the Kharif (summer) season was observed, with rates of -0.080°C/year and -0.090°C/year, respectively. Trend analysis of Kharif crops indicated a significant increase in the productivity of rice (0.046 t/ha/year) and pulses (0.020 t/ha/year), while maize showed a non-significant increase in productivity. In the horticulture sector, significant declines in both maximum and minimum temperatures were observed during the fruit-setting stage in the temperate zone, suggesting climate-induced alterations across multiple phenological stages. Furthermore, in the sub-tropical region, a significant decrease in maximum temperature was recorded during the fruit-setting stage. These findings indicate that over the past 27 years, climatic changes in the Sirmour district have had a generally positive impact on the productivity of most fruit crops, except guava, which exhibited a decline in yield. This analysis highlighted the critical need for adaptive strategies in the face of evolving climate patterns to sustain and enhance agricultural productivity in the region.
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.
Hydrometeorological dynamics of monsoon floods on the Kaveri River, Southern India
This study aims to investigate the hydrometeorological dynamics of monsoon floods on the Kaveri River, Southern India. It encompasses the analysis of interannual rainfall variability and its correlation with floods, characterization of flood-inducing low-pressure systems (LPS), depth-area-duration (DAD) evaluation, assessment of the relationship between annual rainfall totals and flood frequencies, application of the normalized accumulated departure from mean (NADM) method to identify flood-linked anomalies, and investigation of the interplay between El Niño southern oscillation (ENSO) phenomena and monsoon rainfall patterns governing flood dynamics. The study employs a 121-year rainfall dataset, coupled with a 45-year annual maximum series for two hydrological monitoring stations, enabling long-term statistical analysis of rainfall variability and flood dynamics. The results indicate that the interannual variability in the basin is characterized by a notable increase in the frequency and magnitude of floods, particularly post-1960s. Moreover, substantial flood events coincided with periods of above-average rainfall, underscoring the significant role of rainfall anomalies in modulating flood dynamics. Intense LPS predominantly drive major floods in the basin, especially in the deltaic zone. DAD curve of 23-25 July 1924 event has contributed the highest average depth of rainfall over the basin for 1-day, 2-day and 3-day duration. Analysis of synoptic conditions associated with major floods indicate that most of the floods were associated with positive departure from mean rainfall in the basin. The NADM graphs show epochal behaviour of high and low rainfall and therefore, floods. The frequency of floods is generally high in years with normal or weak ENSO conditions.
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)
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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.
Utilization climate hazards group infrared precipitation with station data (CHIRPS) to determine rainfall thresholds for early warning landslide in Luwu, South Sulawesi, Indonesia
Early warning of landslides using remote sensing has received increasing attention in recent decades. However, in Luwu Raya, South Sulawesi, rainfall thresholds that trigger landslides remain poorly understood due to limited rainfall ground data. This study evaluates the suitability of the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) to determine landslide-triggering thresholds. This lack of understanding poses significant risks to local communities and infrastructure, making it crucial to identify reliable rainfall thresholds for landslide prediction and risk mitigation. Because satellite rainfall estimates have varying biases, both spatially and temporally, validation was performed using observational data from 41 ground-based rainfall observation as reference data for 2019–2024 against CHIRPS. Accuracy was assessed using RMSE, a dichotomous metric, and extreme rainfall indices. Rainfall thresholds were derived by event-based matching between the landslide inventory and CHIRPS rainfall, followed by curve analysis of daily rainfall combined with antecedent accumulations over 3, 5, 10, 15, 20, and 30 days. Thresholds were derived from curve analysis of accumulated rainfall (3–30 days) based on flood and landslide events. Results show that CHIRPS accuracy is lower in mountainous areas (>1000 m) than in lowlands. The most reliable threshold combined daily rainfall with 15-day antecedent rainfall, achieving 67% accuracy, while shorter accumulation (10 days) performed poorly (5%). Central Luwu (500–750 m) showed the highest predictive potential. The identified threshold of only 20 mm/day, categorized as light rainfall in Indonesia, suggests that while CHIRPS offers promise, further refinement is needed before satellite rainfall can be fully applied in operational landslide early warning.
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.
The Assessing drought trends and prediction using GCMs: A case study of South Bihar
Understanding precipitation projections under climate change in semi-arid regions such as South Bihar is crucial for water-resources planning. This study examines the impacts of a high-emissions scenario (RCP 8.5) on drought patterns across the southern districts of Bihar using outputs from 14 downscaled, regionally adjusted global climate models (GCMs). Meteorological drought is characterized with the Standardized Precipitation Index (SPI). Model performance was evaluated by correlating GCM precipitation with India Meteorological Department (IMD) observations for 2006–2020; the best-performing models were HadGEM2-AO, MRI-CGCM3, MIROC5, CESM1-CAM5, and CCSM4. Projections indicate an ~30% increase in the severity of mild-to-moderate droughts, with Gaya, Jamui, and Lakhisarai most affected. The likelihood of rainless spells during 2021–2050 is projected to rise, signalling a growing risk of dry spell driven meteorological drought. While individual drought events cannot be predicted deterministically, the evolving rainfall regime suggests greater alternation between wet and dry conditions. Strong agreement between simulated and observed precipitation (R² 0.63–0.73) supports the suitability of these models for assessing future drought risk in this region.
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.
Passive microwave remote sensing of soil moisture for flood prediction: A case study of Derna, Libya
Monitoring environmental factors such as soil moisture and precipitation on a frequent basis plays a vital role in identifying early signs of flooding, particularly in regions prone to extreme weather. Passive microwave remote sensing stands out in this context due to its ability to collect data regardless of weather conditions, along with its regular daily coverage. In this study, changes in the Polarisation Index (PI) were examined before and after the severe flooding that struck Derna, Libya, on 11 September 2023. The data used were obtained from the AMSR2 sensor operating at X-band (10 GHz) aboard Japan’s GCOM-W1 satellite. Notably, a sharp rise in PI was recorded one day ahead of the flood, aligning with increased soil moisture linked to intense rainfall in the area. Following the event, PI values remained elevated, indicating continued ground saturation. These findings point to the potential of PI as an early warning indicator for heavy rainfall and flood risk. With appropriate selection of observation points, this method could support the development of flood forecasting systems in other high-risk regions around the world.
CELEBRATING 150 YEARS OF IMD: A CHRONICLE OF WEATHER GUARDIANS AND TECHNOLOGICAL MARVELS