Urban Modelling: Development of multi-sectorial simulation lab and science-based decision support framework to address urban environment issues

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Urban Modelling: Development of multi-sectorial simulation lab and science-based decision support framework to address urban environment issues

About Project

India is undergoing a major urban population shift, with a projection to be double by 2050. This rapid urban expansion is reshaping the socio-economic landscape but also intensifying environmental vulnerabilities across cities. As urban areas grow, Indian cities are increasingly experiencing extreme weather events and environmental stressors, such as, intense rainfall and urban flooding, heat and cold waves, and severe air pollution episodes. These are no longer isolated incidents; they are becoming frequent and more severe, posing significant risks to public health, infrastructure, and economy. The cascading effects of these environmental challenges are felt across all levels of governance. Policymakers at the national, state, and local levels are increasingly concerned about the long-term implications of these urban stressors.

To effectively manage and mitigate these challenges, it is vital to understand, simulate, and predict urban extreme events in a timely and accurate manner. This calls for a science-based decision support system that can guide operational responses and policy formulation. Urban environmental systems are essentially cross-sectoral interactions between meteorology, hydrology, and air quality. Addressing these complexities requires a fully integrated modeling platform that includes:

  • High-Performance Computing (HPC)
  • Multi-source input data: satellite, ground observations, and real-time data
  • Validation and verification mechanisms to ensure model accuracy
  • Multi-model interoperability for seamless integration across domains
  • Multi-scale ensemble modeling
  • 2D/3D visualization tools

The comprehensive urban modeling system will encompass, Urban parameterization and canopy modeling, Urban Heat Island (UHI) analysis, Boundary layer and atmospheric dynamics, Chemical and morphological data assimilation, Query frameworks powered by big data analytics and AI/ML technologies. These components will enable to forecast extreme events, assess risks, and help city authorities to plan management strategies.

Against this backdrop, the NSM Urban Modelling consortia project (funded by MeitY) is articulated for timely prediction of weather, air quality, and hydrological systems. Urban Modelling project, an integrated urban modelling system and service cyberinfrastructure Urban Environment Science to Society (UES2S) (Figure 1) is developed. This is an online, fully coupled urban ‘meteorology and hydrology, and air quality’ modeling system (Figure 2) which captures the urban representation of micro-scale city environmental conditions.

Figure 1: Homepage of Urban Environment Science to Society (UES2S)
Figure 1: Homepage of Urban Environment Science to Society (UES2S)
Figure 2: HPC Based Integrated Urban Modelling System
Figure 2: HPC Based Integrated Urban Modelling System

The objective of this consortia project is to develop an online fully coupled urban ‘meteorology, hydrology, and air quality’ modeling system to capture the urban representation of micro-scale city environmental conditions. The aim is to improve the skill of urban weather forecasting, atmospheric dispersion and air quality forecasting, and hydrology forecasting useful for exposure assessment, disaster management, daily operations, and policy decisions and create a science-based, HPC-enabled urban data and decision framework with optimized performance and 3D visualization techniques, thereby fulfilling India’s sustainable smart city goals.

The NSM Urban Modelling Project developed the online, fully coupled urban “meteorology, hydrology, and air quality” modelling system called UES2S. The urban illustration of micro-scale city environmental conditions is captured by this approach. Data as a service (Data Hub), modeling platform as a service (Science Gateway), and Decision Support System (DSS) for cross-sector end-user decisions are the three main parts of UES2S. We plan to offer a data-sharing platform and cross-sector data access through Data Hub. On NSM clusters, the Science Gateway (Figures 3 & 4) offers ready-to-use weather, hydrological, and air quality models through automated end-end modeling workflows. The DSS component (Figures 5 and 6) facilitates easy conversion of scientific data into participatory, multi-stakeholder actions. The DSS forecasts reservoir inflows, water levels, and weather, air quality, and hydrology with high resolution. Thus, the DSS is essential to everyday operations, disaster management initiatives, and science-based policymaking.

To solve urban environmental issues, this multi-sector simulation lab and science-based decision framework were created. This interdisciplinary urban testbed and HPC-based automated model execution workflows were created to run meteorological, hydrological, and air quality models in order to predict extreme events. This system’s incredibly user-friendly architecture, researchers and students can execute the models with ease. This would make it easier for research to be smoothly incorporated into operational procedures. The framework enables meteorology, air quality, and hydrology services for a variety of stakeholders by providing an urban modeling system, operational procedures, a data hub, and a DSS.

Science Gateway, a digital platform designed specifically for meteorologists, hydrologists, and air quality modelers that provide easy access to data from specialist tools and collaborative capabilities to improve research and forecasting in these fields. The Weather Research and Forecasting (WRF) workflow (Figure 4) is created in Science Gateway, a cutting-edge mesoscale numerical weather prediction system intended for operational forecasting and atmospheric research applications.

Making decisions during extreme events, such as heat waves, floods, and heavy rains, is aided by a Decision Support System (DSS) (Figure 5). It makes it easier to analyze hydrological indicators like reservoir levels and river flows, air pollution levels like PM2.5, and meteorological events like heat and cold waves, short-duration high-intensity rains, and more.

The reservoir operations module, an essential element of the DSS, presents a series of forecast plots over time as shown in Figure 5. This chart features various parameters like rainfall in the upstream/downstream catchment, the water level of the reservoir, dam discharge, and the inflow to the reservoir for the chosen reservoir by the user. The user, generally a scientist or operational forecaster in weather, environmental science, or disaster management, can utilize this data to make informed decisions and strategize effectively.

In a section of the DSS module (Figure 6), users can choose flood hotspots found in the alerts area and then click on a hotspot marker for a specific location to see water depth relative to human height, enhancing the visualization engaging and intuitive.

Figure 3: Components of Science Gateway
Figure 3: Components of Science Gateway
Figure 4: WRF Workflow
Figure 4: WRF Workflow
Figure 5: Decision Support System
Figure 5: Decision Support System
Figure 6: DSS for Flood – Water depth: 0.52 meter (pictorial view) at Pune catchment
Figure 6: DSS for Flood – Water depth: 0.52 meter (pictorial view) at Pune catchment
  • Calibrated and customized model for Indian cities
  • Prepared high resolution LULC map for pilot cities
  • Integrated Met-Hydro-AQ user friendly web-based Model Execution framework (WRF, WRF-Chem, AERMOD, HEC-RAS, HEC-HMS, SWMM), Data hub and Decision Support System
  • End-to-End automated model forecast validation tool
  • Indigenously developed visualization platform
  • Provided access of developed system to IMD
  • Shared ward level Rainfall, Reservoir Water and urban flood, heatwave, air pollution forecast information with IMD, PMC, PCMC and WRD (Figure 3 & 4)
  • Demonstration of UES2S to Dr. M. Ravichandran, Secretary, MoES, Chairman, PMC and domain experts (Figure 5)
  • 36 Publications in peer-reviewed journals & 13 Conference papers
  • MoU signed on 30th October 2024 with Pimpri-Chinchwad Municipal Corporations to share urban Decision Support System weather, flood information, and air quality (jointly with IITM Pune) at ward level
  • Data sharing: Providing weather and flood forecast to IMD, Pune Municipal Corporation, Pimpri Chinchwad Municipal Corporation (PCMC) and water resources departments, govt of Maharashtra
Figure 7: Typical Daily Rainfall info upto Ward level
Figure 7: Typical Daily Rainfall info upto Ward level
Figure 8: Air quality forecast information
Figure 8: Air quality forecast information
Figure 9: Project review by Dr. M. Ravichandran, Secretary, MoES, Chairman, PMC and domain experts
Figure 9: Project review by Dr. M. Ravichandran, Secretary, MoES, Chairman, PMC and domain experts
MoU
  • MoU signed with Pimpri-Chinchwad Municipal Corporation (PCMC) on 30th October 2024 to share the access and use of Urban Decision Support System
(R to L) Dr Sanjay Wandhekar Centre Head, C-DAC Pune, Mr Shekhar Singh (IAS) Commissioner, PCMC, PCMC officials
Journal
2025
  1. Boyaj, A., Sinha, Palash, Karrevula, N.R., Nadimpalli, R., Vinoj, V., Islam, Sahidul., Khare, M. and Mohanty, U.C., 2025. Assessment of the WRF Model for Real-Time Prediction of Heavy Rainfall Events over the Twin Cities of East Coast of India. Pure and Applied Geophysics, pp.1-19. https://doi.org/10.1007/s00024-025-03734-x.
  2. Boyaj, A., Karrevula, N.R., Swain, M., Sinha, Palash., Nadimpalli, R., Islam, Sahidul., Vinoj, V., Khare, M., Niyogi, D. and Mohanty, U.C., 2025. Assessment of the WRF model configuration optimization in predicting the heavy rainfall over urban city Bhubaneswar, India. Computational Urban Science, 5(1), pp.1-21. https://doi.org/10.1007/s43762-025-00180-2.
  3. Ambulkar, R., Govardhan, G., Gavhale, S., Kalita, G., Pande, C., Jat, R., et al. (2025). Crop residue burning in north-western India: Emission estimation and uncertainty quantification. Journal of Geophysical Research: Atmospheres, 130, e2024JD042198. https://doi.org/10.1029/2024JD042198
  4. Govardhan, G., Ghude, S. D., Kumar, R., Sharma, S., Gunwani, P., Jena, C., Yadav, P., Ingle, S., Debnath, S., Pawar, P., Acharja, P., Jat, R., Kalita, G., Ambulkar, R., Kulkarni, S., Kaginalkar, A., Soni, V. K., Nanjundiah, R. S., and Rajeevan, M.: Decision Support System version 1.0 (DSS v1.0) for air quality management in Delhi, India, Geosci. Model Dev., 17, 2617–2640, https://doi.org/10.5194/gmd-17-2617-2024.
  5. Rajmal Jat, Chinmay Jena, Prafull P. Yadav, Gaurav Govardhan, Gayatry Kalita, Sreyashi Debnath, Preeti Gunwani, Prodip Acharja, PoojaV. Pawar, Pratul Sharma, Santosh H. Kulkarni, Akshay Kulkarni, Akshara Kaginalkar, Dilip M. Chate, Rajesh Kumar, Vijay Kumar Soni, Sachin D. Ghude,Evaluating the sensitivity of fine particulate matter (PM2.5) simulations to chemical mechanism in WRF-Chem over Delhi, Atmospheric Environment,Volume 323,2024,120410,ISSN 1352-2310, https://doi.org/10.1016/j.atmosenv.2024.120410.
  6. Akash S. Vispute, Prodip Acharja, Suresh W. Gosavi, Gaurav Govardhan, Vinayak Ruge, M.N. Patil, T. Dharmaraj, Sachin D. Ghude, Source characteristics of Non-Refractory Particulate Matter (NR-PM1) using High-Resolution Time-of-Flight Aerosol Mass Spectrometric (HR-ToF-AMS) measurements in the urban industrial city in India, Atmospheric Environment,2025,121186,ISSN 1352-2310, https://doi.org/10.1016/j.atmosenv.2025.121186.
  7. Chinmay Jena , et. al.: “ Evaluating the sensitivity of fine particulate matter (PM2.5 ) simulations to chemical mechanism in WRF-Chem over Delhi”., Science of the Total Environment, (2023). https://doi.org/10.1016/j.atmosenv.2024.120410.
  1. Ghude SD, Govardhan G, Kumar R, Yadav PP, Jat R, Debnath S, Rajeevan M (2024) Air Quality warning and Integrated decision support system for emissions(AIRWISE): Enhancing Air Quality management in megacities. Bull Am Meteorol Soc. https://doi.org/10.1175/BAMS-D-23-0181.1
  2. Swain, M., Nadimpalli, R., Das, A.K., Mohanty, U.C. and Niyogi, D., 2023. Urban modification of heavy rainfall: a model case study for Bhubaneswar urban region. Computational Urban Science, 3(1), p.2. https://doi.org/10.1007/s43762-023-00080-3.
  3. Islam, S., Karipot, A., Bhawar, R., Sinha, P., Kedia, S. and Khare, M., 2024. Urban heat island effect in India: a review of current status, impact and mitigation strategies. Discover Cities, 1(1), pp.1-28. https://doi.org/10.1007/s44327-024-00033-3.
  4. Gaikwad S., et al 2024: Harnessing deep learning for forecasting fire-burning locations and unveiling PM2.5 emissions, Modeling Earth Systems and Environment, 10, February 2024, DOI:10.1007/s40808-023-01831-1, 927-941. https://doi.org/10.1007/s40808-023-01831-1.
  5. Govardhan G., et al (2024), Decision Support System version 1.0 (DSS v1.0) for air quality management in Delhi, India, Geoscientific Model Development, 17, April 2024, DOI:10.5194/gmd-17-2617-2024, 2617–2640. https://doi.org/10.5194/gmd-17-2617-2024.
  6. Karrevula, N.R., Nadimpalli, R., Sinha, P., Mohanty, S., Boyaj, A., Swain, M. and Mohanty, U.C., 2024. Performance Evaluation of WRF Model in Simulating Extreme Rainfall Events Over Bhubaneswar Urban Region of East Coast of India. Pure and Applied Geophysics, pp.1-27. https://doi.org/10.1007/s43762-025-00180-2.
  7. Karrevula, N.R., Boyaj, A., Sinha, P., Nadimpalli, R., Mohanty, U.C., Islam, S., Kaginalkar, A. and Vinoj, V., 2024. Role of planetary boundary layer physics in urban-scale WRF model for predicting the heat waves over tropical city Bhubaneswar. Journal of Earth System Science, 133(3), pp.1-26. https://www.researchgate.net/publication/383642297_Role_of_planetary_boundary_layer_physics_in_urban-scale_WRF_model_for_predicting_the_heat_waves_over_tropical_city_Bhubaneswar.
  8. Boyaj, A., Karrevula, N.R., Sinha, P., Patel, P., Mohanty, U.C. and Niyogi, D., 2024. Impact of increasing urbanization on heatwaves in Indian cities. International Journal of Climatology, 44(11), pp.4089-4114. https://doi.org/10.1002/joc.8570.
  9. Boyaj, A., Sinha, P., Mohanty, U.C., Vinoj, V., Ashok, K., Islam, S., Kaginalkar, A. and Khare, M., 2024. Projected frequency of low to high-intensity rainfall events over India using bias-corrected CORDEX models. Atmospheric Research, p.107760. https://www.researchgate.net/publication/385505754_Projected_frequency_of_low_to_high-intensity_rainfall_events_over_India_using_bias-corrected_CORDEX_models.
  1. Mohanty, S., Swain, M., Nadimpalli, R., Osuri, K.K., Mohanty, U.C., Patel, P. and Niyogi, D., 2023. Meteorological conditions of extreme heavy rains over coastal city Mumbai. Journal of Applied Meteorology and Climatology, 62(2), pp.191-208. https://doi.org/10.1175/JAMC-D-21-0223.1
  2. Boyaj, A., Nadimpalli, R., Reddy, D., Sinha, P., Karrevula, N.R., Osuri, K.K., Srivastava, A., Swain, M., Mohanty, U.C., Islam, S. and Kaginalkar, A., 2023. Role of radiation and canopy model in predicting heat waves using WRF over the city of Bhubaneswar, Odisha. Meteorology and Atmospheric Physics, 135(6), p.60. https://doi.org/10.1007/s00703-023-00994-x.
  3. Swain, M., Nadimpalli, R.R., Mohanty, U.C., Guhathakurta, P., Gupta, A., Kaginalkar, A., Chen, F. and Niyogi, D., 2023. Delay in timing and spatial reorganization of rainfall due to urbanization-analysis over India’s smart city Bhubaneswar. Computational Urban Science, 3(1), p.8. https://doi.org/10.1007/s43762-023-00081-2.
  4. Gunwani P., et. al.: “Sensitivity of WRF/Chem to different Meteorological Initial Conditions and PBL parameterization schemes”, Atmospheric Environment (2023). https://doi.org/10.1007/s10661-023-10987-3.
  5.  Madhusmita Swain, et al., 2023: Delay in timing and spatial reorganization of rainfall due to urbanization- analysis over India’s smart city Bhubaneswar. Comput. Urban Sci. 3, 8. https://doi.org/10.1007/s43762-023-00081-2.
  6. Shyama Mohanty,  Madhusmita Swain, Raghu Nadimpalli, K. K. Osuri, U. C. Mohanty, Pratiman Patel, and  Dev Niyogi, 2023: Meteorological Conditions of Extreme Heavy Rains over Coastal City Mumbai. Journal of Applied Meteorology and Climatology, 2023,Vol.62-2, 191–208. https://doi.org/10.1175/JAMC-D-21-0223.1.
  7. V. K. Valappil, Sumita Kedia, A. K Dwivedi, S. S Pokale, Sahidul Islam, Manoj K Khare, Assessing the performance of WRF ARW model in simulating heavy rainfall events over the Pune region: in support of operational applications, Meteorology and Atmospheric Physics, 2023. https://doi.org/10.1007/s00024-025-03734-x.
  8. Madhusmita Swain, R. Nadimpalli, U C Mohanty, P Guhathakurta, A Gupta, A Kaginalkar, F Chen, and D Niyogi. Delay in timing and spatial reorganization of rainfall due to urbanization- Analysis for pre-monsoon conditions in Bhubaneswar, India. Computational Urban Science vol 3, Article number: 8 (2023). https://doi.org/10.1007/s43762-023-00081-2.
  9. Gayatry Kalita, et. al.: “Forecasting of an unusual dust event over Western India by the Air Quality Early Warning System”, Atmospheric Environment (2023). https://doi.org/10.1016/j.atmosenv.2023.120013.
  10. Madhusmita Swain, R. Nadimpalli, U C Mohanty, and D Niyogi. Urban modification of heavy rainfall: a model case study for Bhubaneswar urban region. Computational Urban Science volume 3, Article number: 2 (2023). https://doi.org/10.1007/s43762-023-00080-3
  1. Risma Joseph, P. P. Mujumdar, Rajarshi Das Bhowmik, (2022). Reconstruction of Urban Rainfall Measurements to Estimate the Spatiotemporal Variability of Extreme Rainfall. Water, 14(23), 3900. Doi: https://doi.org/10.3390/w14233900, (2022)
  2. Gaurav Govardhan, et. al.: “Satellite retrieved stubble-burning activities in north-western India in 2021: Contribution to air pollution in Delhi“, Heliyon (2022). https://orcid.org/0000-0003-0213-6289.
  3. Sreyashi Debnath, et. al.: “Implications of implementing promulgated and prospective emission regulations on air quality and health in India during 2030” AAQR (2022). https://doi.org/10.4209/aaqr.220112.
  4. Davis S, Pentakota L, Saptarishy N and Mujumdar PP : A Flood Forecasting Framework Coupling a High-Resolution WRF Ensemble With an Urban Hydrologic Model. Front. Earth Sci. 10:883842. Doi: 10.3389/feart.2022.883842, (2022). https://doi.org/10.3389/feart.2022.883842.
  5. Sengupta A., G. Govardhan, S. Debnath, C. Jena, A.N. Parde, P. Lonkar, P. Gunwani, Santosh H Kulkarni, S. Nivdange, R Kumar, and S.D. Ghude, “Probing into the wintertime meteorology and particulate matter (PM2.5 and PM10) forecast over Delhi”, Atmospheric Pollution Research, (2022). https://doi.org/10.1016/j.apr.2022.101426.
  6. S. Nivdange, C. Jena, P. Pawar-Jadhav, G. Govardhan, Santosh H. Kulkarni, P. Lonkar, A. Vispute, N. Dhangar, A. Parde, P. Acharja, V. Kumar, P. Yadav and N. R Karmalkar: Nationwide CoViD-19 lockdown impact on air quality in India, Mausam, 73, 1, 115-128, (2022). https://doi.org/10.54302/mausam.v73i1.1475.
  1. Nadimpalli, Raghu, Shyama Mohanty, Nishant Pathak, Krishna K. Osuri, U. C. Mohanty, and Somoshree Chatterjee. “Understanding the characteristics of rapid intensity changes of Tropical Cyclones over North Indian Ocean.” SN Applied Sciences 3, no. 1 (2021): 1-12. https://doi.org/10.1007/s42452-020-03995-2.
  2. Mohanty, Shyama, Raghu Nadimpalli, U. C. Mohanty, M. Mohapatra, A. Sharma, Ananda K. Das, and S. Sil. “Quasi-operational forecast guidance of extremely severe cyclonic storm Fani over the Bay of Bengal using high-resolution mesoscale models.” Meteorology and Atmospheric Physics 133, no. 2 (2021): 331-348. https://doi.org/10.1007/s00703-020-00751-4.
  3. Kaginalkar A. et al., Integrated urban environmental system of systems for weather ready cities in India, https://doi.org/10.1175/BAMS-D-20-0279.1, Bulletin of the American Meteorological Society, (2021).
  4. Pawar, P. V., S. D. Ghude, C. Jena, Móring, A., Sutton, M. A., Kulkarni Santosh H., Lal, D. M., Surendran, D., Van Damme, M., Clarisse, L., Coheur, P.-F., Liu, X., Xu, W., Jiang, J., and Adhya, T. K.: Analysis of atmospheric ammonia over South and East Asia based on the MOZART-4 model and its comparison with satellite and surface observations, Atmos. Chem. Phys,. https://doi.org/10.5194/acp-2020-639, (2021)
  5. C. Jena, S. D. Ghude, R. Kumar, S. Debnath, G. Govardhan, V. K. Soni, Santosh H. Kulkarni, G. Beig, R. S. Nanjundiah and M. Rajeevan: Performance of high resolution (400 m) PM2.5 forecast over Delhi. Nature Sci Rep, 11, 4104,  (2021). https://doi.org/10.1038/s41598-021-83467-8.
  6. Sumita Kedia, Sudheer Bhakare, Arun Dwivedi, Sahidul Islam, Akshara Kaginalkar: Estimates of change in surface meteorology and urban heat island over northwest India: Impact of urbanization, Urban Climate, Volume 36, (2021). https://doi.org/10.1016/j.uclim.2021.100782.
  1. S. D. Ghude, R. Kumar, C. Jena, S. Debnath, R. G. Kulkarni, S. Alessandrini, M. Biswas, Santosh H. Kulkrani, P. Pithani, S. Kelkar, V. Sajjan, D.M. Chate, V.K. Soni, S. Singh, R. S. Nanjundiah and M. Rajeevan: Evaluation of PM2.5 forecast using chemical data assimilation in the WRF-chem model: a new initiative under the Ministry of Earth Sciences (MoES) air quality early warning system (AQEWS) for Delhi, Current Science (2020). https://opensky.ucar.edu/system/files/2024-08/articles_23419.pdf.
  2. Kulkarni Santosh H., S. D. Ghude, C. Jena, R. K. Karumuri, B. Sinha, V. Sinha, R. Kumar, V. K. Soni, and M. Khare: How Much Does Large-Scale Crop Residue Burning Affect the Air Quality in Delhi? Environmental Science & Technology, 54 (8), 4790-4799 (2020). https://dx.doi.org/10.1021/acs.est.0c00329?ref=pdf.
  3. Kumar R., Ghude, S. D, M. Biswas, C. Jena, S. Alessandrini, S. Debnath, Santosh Kulkarni, Simone Sperati, Vijay K. Soni, R. S. Nanjundiah, and M. Rajeevan: Enhancing accuracy of air quality and temperature forecasts during paddy crop-residue burning season in Delhi via chemical data assimilation, JGR (Atmosphere), (2020). https://doi.org/10.1029/2020JD033019.
  4. Jena, C., Ghude, S. D., Kulkarni, R., Debnath, S., Kumar, R., Soni, V. K., Acharja, P., Kulkarni  Santosh H., Khare, M., Kaginalkar, A. J., Chate, D. M., Ali, K., Nanjundiah, R. S., and Rajeevan, M. N.: Evaluating the sensitivity of fine particulate matter (PM2.5) simulations to chemical mechanism in Delhi, Atmos. Chem. Phys. Discuss.,  (2020). https://doi.org/10.5194/acp-2020-673.
  5. S. D. Ghude, R. K. Karumuri, C. Jena, R. Kulkarni, G.G. Pfister, V. S. Sajjan, P. Pithani, S. Debnath, R. Kumar, B. Upendra, Santosh H. Kulkarni, D.M. Lal, R.J. Vander A, A. S. Mahajan: What is driving the diurnal variation in tropospheric NO2 columns over a cluster of high emission thermal power plants in India?, Atmospheric Environment: X 5, 100058,  (2020). https://doi.org/10.1016/j.aeaoa.2019.100058.
  1. U C Mohanty, Narayana Reddy Karrevula, Alugula Boyaj, Madhu Smita Swain, Raghu Nadimpalli, Palash Sinha, Sahidul Islam, Manoj Khare (2025). Urban-Scale Weather Modelling System for Prediction of Heavy Rainfall Events during Summer Monsoon Season over India. The Eight International Workshop on Monsoons (IWM8) under the World Weather Research Programme (WWRP) of the World Meteorological Organization (WMO), held at Indian Institute of Tropical Meteorology (IITM) Pune during 17-20 March 2025.
  2. Palash Sinha, Sahidul Islam, Ketaki Belange, Sumita Kedia, T.S. Saikrishna and Manoj Khare: Heatwave Predictions and Decision Support System for Advisory. Presented at TROPMET-2024, 10-12-Dec-2024 at NIT Rourkela, Odisha.
  3. Arun K. Dwivedi, Sumita Kedia, Sagar Pokale, Palash Sinha, Akshara Kaginalkar, Manoj K. Khare, U.C. Mohanty, Sahidul Islam, Assessment of WRF Model in Predicting Heavy Rainfall Events over Complex Topographical Urban City Pune, submitted to International Conference on Urban Climate (ICUC-11), during 28 Aug-1 Sept 2023. https://www.researchgate.net/deref/https%3A%2F%2Fdoi.org%2F10.1007%2Fs00024-025-03734x?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InB1YmxpY2F0aW9uIiwicGFnZSI6InB1YmxpY2F0aW9uIn19.
  4. Sumita Kedia, A. K. Dwivedi, S. Pokale, Sahidul Islam, A. Kaginalkar, P. Sinha, S. Ghavale, R. Nadimpalli, U. C. Mohanty, D. Niyogi, M. Khare, Impact of land use information on heavy rainfall event forecast using an urban scale model, accepted and presented during AMS annual meeting 2023.
  5. Dev Niyogi, Pallavi Gavali, Mohamed Niyaz J, Srujan Gavhale, Arun Dwivedi, Sumita K, Sagar Pokale, Gouri Kadam, Sahidul Islam, Akshara Kaginalkar, Pradeep Mujumdar. Abinav Wadhawa, Likhitha P.: Coupled Meteorology and Hydrology Modelling to Forecast Flood Extreme Events: Case Study of Pune, India. AGU Fall meeting 2022 meet, at Chicago 12 -16 Dec 2022, Advances in modeling hydrological extremes and engineering practices.
  6. Gouri Kadam, M. Niyaz, P. G. Gavali, S. Gavhale, L. Pentakota, S. Kedia, S. Islam, A. K. Dwivedi, S. Pokale, A. Kaginalkar, P. Mujumdar, M. Khare, and D. Niyogi:  Multi-Model Hydrology for Urban Flood Early Warning for Pune, India.  103rd American Meteorological Society Annual Meeting, Denver, USA. (37th Conference on Hydrology).
  7. Pallavi Gavali, Srujan Gavhale, Mohamed Niyaz, Sahidul Islam, Sumita Kedia, Sagar Pokale, Arun Dwivedi, Gouri Kadam, Akshara Kaginalkar, Manoj Khare, and Abhinav Wadhwa. Integrated Reservoir Operations using coupled Hydro-Met Multi-Model system for flood forecasting and mitigations for Pune, India, submitted to EGU general Assembly 2023, 23-28 April 2023. https://doi.org/10.5194/egusphere-egu23-15342.
  8. Lead talk: Dr. Sumita Kedia and Dr. Yogesh Kumar Singh on “C-DAC’s Innovative Technological Development for Societal Applications to Adress Weather/Climate Hazards “, International Conference on “Sustainable Agricultural Development with Climate Smart Systems” (SADCSS-2024), S’O’A (Deemed to be University) Bhubaneswar, India during July 18-20, 2024.  Organized by Centre for Climate Smart Agriculture and Faculty of Agricultural Sciences.
  9. Sahidul Islam, Anandakumar Karipot, Rohini Bhawar, Sumita Kedia, Palash Sinha, Ketaki Belange, T.S. Saikrishna and Manoj Khare, 2024: A high-resolution heat wave forecasting system over urban region in India, International Conference on “Sustainable Agricultural Development with Climate Smart Systems” (SADCSS-2024), S’O’A (Deemed to be University) Bhubaneswar, India during July 18-20, 2024.  Organized by Centre for Climate Smart Agriculture and Faculty of Agricultural Sciences.
  10. Manoj Khare, Palash Sinha, Manish Kale, Sumita Kedia, 2024: GIS and Remote Sensing for Smart Agriculture, International Conference on “Sustainable Agricultural Development with Climate Smart Systems” (SADCSS-2024), S’O’A (Deemed to be University) Bhubaneswar, India during July 18-20, 2024.  Organized by Centre for Climate Smart Agriculture and Faculty of Agricultural Sciences
  11. Palash Sinha et al, Assessing WRF model performance in simulating heatwave over India, International Conference on “Sustainable Agricultural Development with Climate Smart Systems” (SADCSS-2024), S’O’A (Deemed to be University) Bhubaneswar, India during July 18-20, 2024.  Organized by Centre for Climate Smart Agriculture and Faculty of Agricultural Sciences.
  12. Key note lecture by Manoj Khare on Key Note Speakers on Climate change, climate variability and adaptation strategies, International Conference on “Sustainable Agricultural Development with Climate Smart Systems” (SADCSS-2024), S’O’A (Deemed to be University) Bhubaneswar, India during July 18-20, 2024.  Organized by Centre for Climate Smart Agriculture and Faculty of Agricultural Sciences.
  13. Nagaraju Gaddam, Abhinav Wadhwa, Likhitha P, Pradeep P Mujumdar, “WRF- SWMM Coupled Model Performance Assessment with LCZ Classifications”, AGU Fall Meeting 2022, held in Chicago in 2022.
  14. Likhitha P, Abhinav Wadhwa, Shubha Avinash, Nagaraju Gaddam, “Low Impact Development (LID) as Flood Control Alternatives for Rapidly Changing Urban Landscape”, AGU Fall Meeting 2022, held in Chicago in 2022.
  1. Palash Sinha, Sahidul Islam, Ketaki Belange, Sumita Kedia, T.S. Saikrishna and Manoj Khare: Assessing WRF Model Performance in Simulating Heatwave over India. Submitted (Revised version). https://doi.org/10.1038/s41598-024-52541-2.
  2. Islam, Sahidul, Palash Sinha, Rajiv Kumar Srivastava, and Manoj Khare. “Heat Waves and Heat Stress in the Changing Climate: A Data-Driven Evaluation.” In Mitigation and Adaptation Strategies Against Climate Change in Natural Systems, pp. 267-287. Cham: Springer Nature Switzerland, 2025.
  3. Sinha, Palash, Manish Modani, Sahidul Islam, Manoj Khare, and Rajiv Kumar Srivastava. “Evolution of Weather and Climate Prediction Systems.” In Mitigation and Adaptation Strategies Against Climate Change in Natural Systems, pp. 243-265. Cham: Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-030-05405-2.
  4. Tiwari, Sarita, Palash Sinha, Manoj Khare, Rajiv Kumar Srivastava, and A. K. Biswal. “Groundwater: A Critical Resource in a Changing Climate.” In Mitigation and Adaptation Strategies Against Climate Change in Natural Systems, pp. 221-239. Cham: Springer Nature Switzerland, 2025.
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Consortia Partners

Leading Institute: Centre for Development of Advanced Computing (C-DAC), Pune
Indian Institute of Tropical Meteorology (IITM), Pune
Indian Institute of Sciences (IISc), Bengaluru
Indian Institute of Technology Bhubaneshwar (IIT BBS)
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Associate Partners

  • Automotive Research Association of India (ARAI), Pune
  • National Centre for Medium Range Weather Forecasting (NCMRWF)
  • India Meteorological Department (IMD)
  • Karnataka State Natural Disaster Monitoring Centre (KSNDMC)
  • India Meteorological Department (IMD)
  • Pune Municipal Corporation (PMC), Disaster Management Department
  • Pune Municipal Corporation (PMC) Environment Department
  • Pimpri Chinchwad Municipal Corporation (PCMC)
  • Karnataka State Natural Disaster Monitoring Centre (KSNDMC)
  • Central Pollution Control Board (CPCB)
  • Maharashtra Pollution Control Board (MPCB)
  • Scientists / Researchers
  • Research students at Post Graduate and Ph.D. level

Dr. Manoj Khare (PI), manojk@cdac.in

+91-20-25503233/25503244