This is a python package to download and handle binary grided data from Indian Meterological department (IMD).
pip install imdlib
or
conda install -c iamsaswata imdlib
or
pip install git+https://github.com/iamsaswata/imdlib.git
imdlib is available under the MIT license.
If you are using imdlib and would like to cite it in academic publication, we would certainly appreciate it. We recommend to use the zenodo DOI for this purpose:
Nandi, S., Patel, P., and Swain, S. (2022). IMDLIB: A python library for IMD gridded data. Zenodo. https://doi.org/10.5281/zenodo.7205414
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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]