Article
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A Survey of Deep Learning-Based Lightning Prediction
Version 1
: Received: 15 September 2023 / Approved: 18 September 2023 / Online: 19 September 2023 (13:32:06 CEST)
A peer-reviewed article of this Preprint also exists.
Wang, X.; Hu, K.; Wu, Y.; Zhou, W. A Survey of Deep Learning-Based Lightning Prediction. Atmosphere 2023, 14, 1698. Wang, X.; Hu, K.; Wu, Y.; Zhou, W. A Survey of Deep Learning-Based Lightning Prediction. Atmosphere 2023, 14, 1698.
Abstract
The escalation of climate change and the increasing frequency of extreme weather events have amplified the importance of precise and timely lightning prediction. This predictive capability is pivotal for the preservation of life, protection of property, and maintenance of crucial infrastructure safety. Recently, the rapid advancement and successful application of data-driven deep learning across diverse sectors, particularly in computer vision and spatio-temporal data analysis, have opened up innovative avenues for enhancing both the accuracy and efficiency of lightning prediction. This article presents a comprehensive review of the broad spectrum of existing lightning prediction methodologies. Starting from traditional numerical forecasting techniques, we traverse the path to the most recent breakthroughs in deep learning research. We encapsulate these diverse methods, shedding light on their progression and summarizing their capabilities, while also predicting their future development trajectories. This exploration is designed to enhance our understanding of these methodologies, allowing us to better utilize their strengths, navigate their limitations, and potentially integrate these techniques to create novel and powerful lightning prediction tools. Through such endeavors, our aim is to bolster our preparedness against the growing unpredictability of our climate and ensure a proactive stance towards lightning prediction.
Keywords
lightning prediction; deep learning; spatio-temporal features; convolutional neural networks; long short-term memory networks
Subject
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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