Seminars / Lectures
New Reports by IITM Scientists
1. Technical Report on Design and Development of the IITM’s Disdrometer Network Data Portal
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2. Comprehensive Assessment of Tropical Cyclones over the Indian Ocean
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IITM e-Newsletter
IITM Newsletter, Volume 8, Issue 2, April 2026
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El Niño weekly update
Bharat Forecast System
REAL TIME FORECAST
High resolution air quality forecast at 400 meter over Delhi
Air quality forecast at 10 km over India
IITM Publication Highlights
Assessing the contrasting effects of moist and dry heatwaves on tropospheric ozone and surface energy budget over Delhi
This study investigates how moist and dry heatwaves differently influence the tropospheric ozone concentrations and surface energy budget over Delhi during the pre-monsoon season using observations, reanalysis data and WRF-Chem simulations. The study uncovers that moist heatwaves produced the highest surface ozone levels, reaching nearly 92 ppbv, compared with dry heatwaves and non-heatwave periods. Elevated ozone during moist heatwaves was associated with weak winds, high relative humidity (>60%), increased carbon monoxide, moderate NOx availability, enhanced dust loading, and a shallow planetary boundary layer that restricted pollutant dispersion. The overnight ozone accumulation and its subsequent mixing with fresh daytime emissions dramatically amplifying surface ozone during Moist Heatwaves causing the residual layer effect. While experiencing a humid heatwave in Delhi it could be more dangerous for the public than dry heatwaves because high humidity traps air pollutants like ozone near the ground, suffocating heat make it feel hotter than it actually is, but the stagnant air also traps harmful ozone pollution near the ground, making every breath a health risk; surprisingly, these moist heatwaves are more threatening to people's health than the hotter but drier heatwaves. This study highlights that heatwave warnings should consider both temperature and humidity, as people may experience greater discomfort and health impacts even when temperatures are not at their highest.
Patnaik K., Prasad P., Aravindhavel A., Prabhakaran Thara, Urban Climate, 67: 102873, June 2026, DOI:10.1016/j.uclim.2026.102873, 1-15
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Bridging data scarcity in groundwater quality studies: A systematic evaluation of statistical and deep learning-based generators
This study addresses the critical challenge of data scarcity in groundwater quality assessments by systematically evaluating six synthetic data generation techniques —namely Bootstrap sampling, Gaussian noise perturbation, Monte Carlo simulation, SMOGN, CTGAN, and TVAE—using an 80-sample hydrochemical dataset from the Vaigai River Basin, Tamil Nadu, South India. Bootstrap sampling emerged as the most effective method, accurately reproducing the original hydrochemical distributions, maintaining multivariate relationships, and yielding the highest predictive performance for Total Dissolved Solids (TDS) when used with Random Forest models (R² = 0.999, RMSE = 41.5 mg/L, and MAE = 7.1 mg/L). The findings revealed that traditional statistical approaches significantly outperformed advanced deep learning models in preserving the characteristics of groundwater datasets. The deep generative models CTGAN and TVAE generated unrealistic distributions and demonstrated poor predictive utility under small-sample conditions. This study shows that when groundwater quality data are limited, simple methods can still provide reliable predictions about water safety and pollution risks. By improving the accuracy of groundwater assessments, these approaches can help authorities make better decisions to protect drinking water resources and ensure sustainable water management for communities.
Aju C.D., Singh B.B., Achu A.L., Ingale M., Goswami M.M., Raicy M.C., Elango L., Physics and Chemistry of the Earth, Parts A/B/C, 143: 104327, June 2026, DOI:10.1016/j.pce.2026.104327, 1-16.
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Evaluating the Biases in Indian summer Monsoon Rainfall Simulation Using the Global Storm Resolving Models: An Assessment of the DYAMOND Models
The first comprehensive evaluation of the biases in Indian Summer Monsoon Rainfall (ISMR) simulations from eight Global Storm-Resolving Models (GSRMs) in the DYAMOND project (10 August–10 September 2016), including ARPNH, GEOS, FV3, NICAM, MPAS, SAM, UM, and IFS, running at 1.5–5 km resolution—for their skill in simulating Indian Summer Monsoon (ISM) rainfall, benchmarked against GPM-IMERG precipitation, CERES outgoing longwave radiation (OLR), and ERA5/NCEP water vapor data. The study finds that moderate-to-heavy rainfall events are simulated more realistically than light rainfall events, which are often overestimated in dry-biased models. Wet-biased models tend to overproduce heavy and very heavy rainfall categories. Outgoing Longwave Radiation (OLR) and Integrated Water Vapour (IWV) analyses reveal that excessive deep convection and higher moisture availability contribute to wet biases, whereas weaker convection and lower moisture lead to dry biases. It shows that despite kilometre-scale resolution and explicit representation of deep convection, significant rainfall biases remain across models. Even advanced high-resolution global models, which simulate storms in fine detail, still have notable biases in predicting India's summer monsoon rainfall. This study helps in improving the accuracy of monsoon rainfall forecasts, which are crucial for agriculture, water resource management, disaster preparedness, and daily life across India.
Lekshmi S., Bhowmik M., Hazra A., Jain D., Pillai P., Rao Suryachandra A., Earth and Space Science, 13: e2025EA004708, June 2026, DOI:10.1029/2025EA004708, 1-20
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Advancing MoES Air Quality Monitoring and Forecasting in Indian Cities: Insights from SAFAR and MAPAN, toward MAPAN-II, with Focus on the Himalayas and Oceanic Outflows
India's SAFAR and MAPAN air quality programs, launched by the Ministry of Earth Sciences starting with the 2010 Commonwealth Games in Delhi and later expanded to Pune, Mumbai, and Ahmedabad, complemented by a 21-location MAPAN network that operated nationally from 2010 to 2019. It has transformed air quality monitoring and forecasting in India by providing real-time observations, high-resolution forecasts, and scientific support for policymaking. Long-term data (2011–2023) analyses indicate a significant decline in particulate matter concentrations and an improvement in air quality across major metropolitan cities such as Delhi, Mumbai, and Ahmedabad, reflecting the success of mitigation measures and regulatory interventions. The study demonstrates that sustained monitoring and forecasting have supported evidence-based policy interventions, contributing to a significant decline in PM₂.₅ concentrations in Delhi (3.03 ± 0.39 μg m⁻³ yr⁻¹) and an increase in the number of "good" and "satisfactory" AQI days. In contrast, it highlights the growing challenge of increasing atmospheric CO₂ concentrations across Indian metro regions, reflecting growing fossil-fuel emissions. Importantly, the expansion of MAPAN into the Himalayan and oceanic regions under MAPAN-II addresses critical observational gaps in ecologically sensitive areas. This initiative will strengthen knowledge of pollution transport, climate interactions, glacier impacts, and atmospheric chemistry. The success of these programs shows that continuous air quality monitoring and forecasting systems, such as SAFAR and MAPAN, are helping to improve air quality in several Indian cities by supporting effective pollution-control policies.
Anand V., Ghude S.D., Panicker A.S., Govardha G., Jat R., Maji S., Rathod A., Shinde R., Kori P., Soni V.K., Rao M.N., Mukherjee A., Khamgaonkar K., Pakkattil A., Varghese A., Yadav P.P., Roy C., Wagh S., Kadam V., Bulletin of the American Meteorological Society, 107, May 2026, DOI:10.1175/BAMS-D-24-0220.1, E1033-E1052
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Why does the signal-to-noise paradox exist in seasonal climate predictability?
"How predictable is the Indian summer monsoon rainfall months in advance? A new study suggests that the answer may be more complex than previously thought. It is found that the widely used “perfect model” approach for estimating the potential predictability limit (PPL), the maximum achievable forecast skill, can sometimes produce paradoxical results, with actual forecast skill exceeding the estimated theoretical limit. The study reveals that this mismatch arises not only from uncertainties in initial conditions but also from model imperfections and the influence of sub-seasonal weather fluctuations that collectively shape seasonal monsoon anomalies. By highlighting the important role of these shorter-timescale variations, the research offers fresh insights into the limits of seasonal climate predictability. The study introduces a simple yet powerful diagnostic framework to estimate the true upper limit of seasonal forecast skill. By providing a more realistic measure of climate predictability, this advance could help improve long-range monsoon forecasts, supporting better planning for agriculture, water resources, disaster risk reduction, and the livelihoods of millions across the monsoon region."
Yashas S., Saha Subodh K., Pokhrel S., Konwar M., Utkarsh V, Geoscientific Model Development, 19, June 2026, DOI:10.5194/gmd-19-4817-2026, 4817-4833
Read MoreNew Publications
Performance of WRF double-moment microphysics schemes in simulating pre-monsoon rainfall across diverse coastal climatic regions of India
Umakanth N., Biswasharma R., Parde A.N., Yadav P.P., Naveena N., Niyogi D., Lal D.M., Pawar S.D., Atmospheric Research, 336, June 2026, DOI:10.1016/j.atmosres.2026.108860, 1-25
Physico-Chemical Properties of Aerosols During Fog-Haze Spell in Delhi: Observational Insights From WiFEX Field Campaign
Vispute A.S., Dhangar N.G., Lonkar P., Parde A.N., Wagh S., Naik M.S., Govardhan G., Gosavi S.W., Ghude S.D., Journal of Geophysical Research: Atmospheres, 131: e2025JD045488, June 2026, DOI:10.1029/2025JD045488, 1-18
Representation of Convectively Coupled Equatorial Waves (CCEWs) During Boreal Summer in IITM-IMD GFS T1534 Model Reforecast
Himabindu H., Mukhopadhyay P., Deshpande M., Tirkey S., Sarkar S., Ganai M., Phani M.K.R., Pure and Applied Geophysics, June 2026, DOI:10.1007/s00024-026-04018-8
Rising exposure and risk from seasonal hot and cold extremes in a warming world
Konda G., Lee JY., Chowdary J.S., Karwat A., Gond S., Franzke C.L.E., Climate Dynamics, 64: 291, June 2026, DOI:10.1007/s00382-026-08256-3, 1-16
IITM Events
SkyCast Launch Propels India into the Global League of Advanced Aviation Weather Nations
(29 May 2026)
India’s first indigenous advanced aviation weather monitoring system, SkyCast, has been launched at Indira Gandhi International (IGI) Airport in New Delhi. Developed by the Ministry of Earth Sciences with key scientific contributions from IITM Pune under the WiFEX programme, SkyCast provides real-time monitoring of fog, wind, turbulence, and visibility.
Workshop on Indian Ocean-Monsoon Climate: Annual to Decadal Variability, Mechanisms, and Predictability
(19-22 May 2026)
The workshop aimed to enhance understanding of annual to decadal climate variability, focusing on the Indian Ocean and the Indian summer monsoon. Interdisciplinary collaboration and the identification of key research gaps are intended to be fostered among participants. Practical recommendations for advancing climate prediction frameworks are also expected to be developed.
‘Climate Startup’ IITM Incubation Centre
(15 May 2026)
The IITM, Pune, inaugurated its Incubation Center for Startups in Weather and Climate, during the national meet ‘Weather and Climate Innovation Meet for Startups and Entrepreneurs (WISE-2026)’. This event marked a significant step towards private-sector integration in India's meteorological services.
Urban Testbed and Aerosol Observatory in Chennai
(06 May 2026)
IITM has established A state-of-the-art Urban Testbed and Aerosol Observatory under the 'Mission Mausam' initiative at the SRM Institute of Science and Technology (SRMIST) in Ramapuram, Chennai. MoU between IITM and SRMIST has been signed.