imdlib

This is a python package to download and handle binary grided data from Indian Meterological department (IMD).

Installation

pip install imdlib

or

conda install -c iamsaswata imdlib

or

pip install git+https://github.com/iamsaswata/imdlib.git

Documentation

Tutorial
Tutorial

Video Tutorial

License

imdlib is available under the MIT license.

Citation

If you are using imdlib and would like to cite it in academic publication, we would certainly appreciate it. We recommend to use one of these two DOIs for this purpose:

Nandi, S., Patel, P., and Swain, S. (2024). IMDLIB: An open-source library for retrieval, processing and spatiotemporal exploratory assessments of gridded meteorological observation datasets over India. Environmental Modelling and Software, 71 (105869), [DOI]

Nandi, S., Patel, P., and Swain, S. (2022). IMDLIB: A python library for IMD gridded data. Zenodo. [DOI]

Publications using IMDLIB

Bahita, T. A., Swain, S., Jha, P. K., Palmate, S. S., & Pandey, A. (2025). Numerical modelling of pollutant dispersion affecting water quality of Upper Ganga Canal (Roorkee City, India). International Journal of Environmental Science and Technology, 22(6), 4433-4444. [DOI]

Dubey, N., Hari, V., Bastos, A., & Ghosh, S. (2025). Vegetation productivity in India is modulated by climate teleconnections from the Pacific Ocean. Journal of Geophysical Research: Biogeosciences, 130(9), e2024JG008682. [DOI]

Kumre, S. K., Swain, S., Amrit, K., Mishra, S. K., & Pandey, A. (2025). Linking curve number with environmental flows: a novel approach. Environmental Science and Pollution Research, 32(16), 10314-10327. [DOI]

Sharma, J., Kumar, A., & Singh, O. (2025). Unraveling the historical and future changes in rainfall concentration over the Narmada River basin. Physical Geography, 1-38. [DOI]

Tsela, R., Maladaki, S., & Kolios, S. (2025). An integrated, automated and modular approach for real-time weather monitoring of surface meteorological variables and short-range forecasting using machine learning. Environmental Modelling & Software, 183, 106203. [DOI]

Sarda, R., & Kumar, P. (2025). Monitoring the dual-season hydrological dynamics of the Pong reservoir in Himachal Pradesh, India. Frontiers in Remote Sensing, 6, 1682140. [DOI]

Krishna, A. B., Kaur, S., Sandhu, S. S., & Kaur, H. (2025). Multi-decadal annual and seasonal temperature variability from 1951 to 2095 over Indian Punjab—A non-parametric statistical approach. Bulletin of Atmospheric Science and Technology, 6(1), 26. [DOI]

Anand, A., Kalyan, K. L., & Bhardwaj, A. (2025). Change Detection of Indian Himalayan Landslide Using Terrestrial Laser Scanner. Indian Geotechnical Journal, 1-14. [DOI]

Zewdu, D., Krishnan, C. M., Raj, P. P., Barati, M. K., Makadi, Y. C., Arlikatti, S., ... & McAleavy, T. (2025). Assessment of Livelihood Vulnerability to Climate Change: A Multidimensional Case Study from Dongarampur Region, India. Earth Systems and Environment, 1-35. [DOI]

Islam, S., Thanveer, J., Yunus, A.P. Beetan, Y., Umrikar, B., Arya, D. S., & Siva Subramanian, S. (2025). Impact of antecedent rainfall and soil saturation on widespread debris flows in the northern Western Ghats during the 2021 extreme rainfall. Bulletin of Engineering Geology and the Environment 84, 360. [DOI]

Karthikeyan, A., Karthik, V., & Chandrasekaran, S. (2025). Flowering out of sync: Climate change alters the reproductive phenology of Terminalia paniculata in the Western Ghats of India. Plants, People, Planet. [DOI]

Ghosh, A., Chakraborty, P. (2025). Mapping Landslide Potential and Assessing Susceptibility in the Darjiling Himalaya Using GIS and Bivariate Statistics. The Himalaya Dilemma. [DOI]

Manivasagam, V. S., Kanagaraj, V. R., Marimuthu, N., Shaanjai, K. S., & Manalil, S. (2025). Exploring the Dynamics of Extreme Rainfall in the Cauvery River Basin, Southern India: Spatio-Temporal Insights and Adaptive Strategies. Natural Hazards Research. [DOI]

Krishna, A. B., Kaur, S., & Sandhu, S. S. (2025). Evaluating the robustness of IMD gridded data of temperature and rainfall with/without statistical bias correction techniques. Arabian Journal of Geosciences, 18(9), 159. [DOI]

Dilama Shamsudeen, S., Sankaran, A., Sajith, A., Stanzin, T., Dev, D., & Abdul Samad, M. S. (2025). A Non-Stationary Framework for Landslide Hazard Assessment Under the Extreme Rainfall Condition. Earth Systems and Environment, 9(1), 337-355. [DOI]

Sailaja, B., Gayatri, S., Rathod, S. et al. (2024). Spatial temperature prediction—a machine learning and GIS perspective. Theoretical and Applied Climatology, 155, 9619–9642. [DOI]

Kulkarni, S., & Agarwal, A. (2024). Quantifying the association between Arctic Sea ice extent and Indian precipitation. International Journal of Climatology, 44(2), 470-484. [DOI]

Sharma, I., Swain, S., Mishra, S. K., & Pandey, A. (2024). Investigating climate and land use change impacts on design flood estimation over Indian tropical catchments. Tropical Ecology, 65(3), 483-507. [DOI]

Srinidhi, A., Smolenaars, W., Werners, S. E., Hegde, S., Rajapure, G., Meuwissen, M. P., & Ludwig, F. (2024). Critical climate-stress moments for semi-arid farming systems in India. Regional Environmental Change, 24(3), 122. [DOI]

Mekonnen, E. N., Fetene, A., & Gebremariam, E. (2024). Grid-based climate variability analysis of Addis Ababa, Ethiopia. Heliyon, 10(6). [DOI]

Kundu, M., Zafor, A., & Maiti, R. (2024). Assessing the nature of potential groundwater zones through machine learning (ML) algorithm in tropical plateau region, West Bengal, India. Acta Geophysica, 72(1), 433-448. [DOI]

Dhal, L., & Kansal, M. L. (2024). An ecohydrological approach to assess water provisioning and supporting ecosystem services in the Budhabalanga River Basin, India. Environmental Monitoring and Assessment, 196(8), 688. [DOI]

Neog, D. R., Singha, G., Dev, S., & Prince, E. H. (2024). Artificial intelligence and its application in disaster risk reduction in the agriculture sector. Disaster risk reduction and rural resilience: with a focus on agriculture, water, gender and Technology. [DOI]

Mohanty, P. K., Pradhan, S., & Samal, R. N. (2024). Vegetation dynamics and its response to climate change at Bhitarkanika mangrove forest, Odisha, east coast of India. Vegetation Dynamics and Crop Stress, (pp. 149-164). Academic Press. [DOI]

Arya, V., & Rao, M. S. (2024). Groundwater recharge potential index and artificial groundwater recharge in the alluvial soils of the middle Ganga basin. Discover Applied Sciences, 6(7), 367. [DOI]

Gupta, A., Sawant, C. P., Kumar, M., Singh, R. K., & Rao, K. V. R. (2024). Assessment of rainfall erosivity for Bundelkhand region of central India using long-term rainfall data. Mausam, 75(2), 415-432. [DOI]

Swain, S., Mishra, P.K., Nandi, S., Pradhan, B., Sahoo, S., & Al-Ansari, A. (2024). A simplistic approach for monitoring meteorological drought over arid regions: a case study of Rajasthan, India. Applied Water Science, 14, 36. [DOI]

Jaiswal, S., Balietti, A., & Schäffer, D. (2023). Environmental Protection and Labor Market Composition. University of Heidelberg, Department of Economics [DOI]

Pandey, H.K., Singh, V.K., Singh, R.P. et al. (2023). Soil Loss Estimation Using RUSLE in Hard Rock Terrain: a Case Study of Bundelkhand, India. Water Conserv Sci Eng 8, 55. [DOI]

Vage, S., Gupta, T., Roy, S. (2023). Impact Analysis of Climate Change on Floods in an Indian Region Using Machine Learning. In: ICANN 2023, 14261. [DOI]

Garg, N., Negi, S., Nagar, R., Rao, S., & KR, S. (2023). Multivariate multi-step LSTM model for flood runoff prediction: a case study on the Godavari River Basin in India. Journal of Water and Climate Change, [DOI]

Bora, S., & Hazarika, A. (2023). Rainfall time series forecasting using ARIMA model. In 2023 ATCON-1, (pp. 1-5). IEEE, [DOI]

Panja, A., Garai, S., Zade, S., Veldandi, A., Sahani, S., & Maiti, S. (2023). Climate Data Extraction for Social Science Research: A Step by Step Process. Social Science Dimensions of Climate Resilient Agriculture, [ISBN] (ISBN: 978-81-964762-1-2)

Chakra, S., Ganguly, A., Oza, H., Padhya, V., Pandey, A., & Deshpande, R. D. (2023). Multidecadal summer monsoon rainfall trend reversals in South Peninsular India: a new approach to examining long-term rainfall dataset. Journal of Hydrology, [DOI].

Sardar, P., and Samadder, S. R. (2023).  Long-term ecological vulnerability assessment of indian sundarban region under present and future climatic conditions under CMIP6 model. Ecological Informatics. [DOI]

Roy, P. K., Ghosh, A., Basak, S. K., Mohinuddin, S., & Roy M. B. (2023).  Analysing the Role of AHP Model to Identify Flood Hazard Zonation in a Coastal Island, India. Journal of the Indian Society of Remote Sensing Article, 1-15. [DOI]

Kundu, M., Zafor, A., & Maiti, R. (2023). Assessing the nature of potential groundwater zones through machine learning (ML) algorithm in tropical plateau region, West Bengal, India. Acta Geophysica, 1-16. [DOI]

Venkatesh, S., Kirubakaran, T., Ayaz, R. M., Umar, S. M., & Parimalarenganayaki, S. (2023). Non-parametric Approaches to Identify Rainfall Pattern in Semi-Arid Regions: Ranipet, Vellore, and Tirupathur Districts, Tamil Nadu, India. In River Dynamics and Flood Hazards (pp. 507-525). Springer, Singapore. [DOI]

Swain, S., Mishra, S. K., Pandey, A., & Dayal, D. (2022). Assessment of drought trends and variabilities over the agriculture-dominated Marathwada Region, India. Environmental Monitoring and Assessment, 194(12), 1-18.
[DOI]

Swain, S., Mishra, S. K., Pandey, A., Dayal, D., & Srivastava, P. K. (2022). Appraisal of historical trends in maximum and minimum temperature using multiple non-parametric techniques over the agriculture-dominated Narmada Basin, India. Environmental Monitoring and Assessment, 194(12), 1-23. [DOI]