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arXiv · 2305.10064

FlashBench: A lightning nowcasting framework based on the hybrid deep learning and physics-based dynamical models

Manmeet Singh·Vaisakh S. B.·Dipjyoti Mudiar·Deewakar Chakraborty·V. Gopalakrishnan·Bhupendra Bahadur Singh·Shikha Singh·Rakesh Ghosh·Rajib Chattopadhyay·Bipin Kumar·S. D. Pawar·S. A. Rao
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Abstract

Lightning strikes are a well-known danger, and are a leading cause of accidental fatality worldwide. Unfortunately, lightning hazards seldom make headlines in international media coverage because of their infrequency and the low number of casualties each incidence. According to readings from the TRMM LIS lightning sensor, thunderstorms are more common in the tropics while being extremely rare in the polar regions. To improve the precision of lightning forecasts, we develop a technique similar to LightNet's, with one key modification. We didn't just base our model off the results of preliminary numerical simulations; we also factored in the observed fields' time-dependent development. The effectiveness of the lightning forecast rose dramatically once this adjustment was made. The model was tested in a case study during a thunderstorm. Using lightning parameterization in the WRF model simulation, we compared the simulated fields. As the first of its type, this research has the potential to set the bar for how regional lightning predictions are conducted in the future because of its data-driven approach. In addition, we have built a cloud-based lightning forecast system based on Google Earth Engine. With this setup, lightning forecasts over West India may be made in real time, giving critically important information for the area.

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Manmeet Singh, Vaisakh S. B., Dipjyoti Mudiar, Deewakar Chakraborty, V. Gopalakrishnan, Bhupendra Bahadur Singh, Shikha Singh, Rakesh Ghosh, Rajib Chattopadhyay, Bipin Kumar, S. D. Pawar, S. A. Rao. 2023-05-17. FlashBench: A lightning nowcasting framework based on the hybrid deep learning and physics-based dynamical models. https://arxiv.org/abs/2305.10064

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