Abstract
Weather nowcasting is a problem pursued by scientists for a long time. Accurate short-term forecasting is helpful for detecting weather patterns leading to extreme weather events. Adding the dimension of nowcasting to extreme weather prediction increases the ability of models to look for preliminary patterns ahead in time. In this paper, we propose a two-stage deep learning pipeline that fuses the usability of nowcasting to the high value of extreme events prediction. Our experiments are performed on INSAT-3D satellite data from MOSDAC, SAC-ISRO. We show that our pipeline is modular, and many events can be predicted in the second phase based on the availability of the relevant data from the first phase. Testing for extreme events like the Chennai floods of 2015 and Mumbai floods of 2017 validates the efficacy of our approach.
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Acknowledgements
This work was supported by the Satellite Meteorology and OceAography Research and Training (SMART) program at Space Application Centre, ISRO. We are thankful to SAC-ISRO for providing delightful opportunity and also giving us the relevant data and facilitating environment.
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Oza, V., Thesia, Y., Rasalia, D., Thakkar, P., Dube, N., Garg, S. (2020). Extreme Weather Prediction Using 2-Phase Deep Learning Pipeline. In: Nain, N., Vipparthi, S., Raman, B. (eds) Computer Vision and Image Processing. CVIP 2019. Communications in Computer and Information Science, vol 1148. Springer, Singapore. https://doi.org/10.1007/978-981-15-4018-9_25
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DOI: https://doi.org/10.1007/978-981-15-4018-9_25
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