Lightning Nowcasting Using Solely Lightning Data

Author:

Mansouri Ehsan1ORCID,Mostajabi Amirhosein1ORCID,Tong Chong2,Rubinstein Marcos3ORCID,Rachidi Farhad1ORCID

Affiliation:

1. Electromagnetic Compatibility Laboratory, Swiss Federal Institute of Technology (EPFL), 1015 Lausanne, Switzerland

2. State Grid JiangSu Electric Power Co., Ltd. Suzhou Branch, Suzhou 215000, China

3. Institute for Information and Communication Technologies, University of Applied Sciences of Western Switzerland (HES-SO), 1400 Yverdon-les-Bains, Switzerland

Abstract

Lightning is directly or indirectly responsible for significant human casualties and property damage worldwide. A timely prediction of its occurrence can enable authorities and the public to take necessary precautionary actions resulting in diminishing the potential hazards caused by lightning. In this paper, based on the assumption that atmospheric phenomena behave in a continuous manner, we present a model based on residual U-nets where the network architecture leverages this inductive bias by combining information passing directly from the input to the output with the necessary required changes to the former, predicted by a neural network. Our model is trained solely on lightning data from geostationary weather satellites and can be used to predict the occurrence of future lightning. Our model has the advantage of not relying on numerical weather models, which are inherently slow due to their sequential nature, enabling it to be used for near-future prediction (nowcasting). Moreover, our model has similar performance compared to other machine learning based lightning predictors in the literature while using significantly less amount of data for training, limited to lightning data. Our model, which is trained for four different lead times of 15, 30, 45, and 60 min, outperforms the traditional persistence baseline by 4%, 12%, and 22% for lead times of 30, 45, and 60 min, respectively, and has comparable accuracy for 15 min lead time.

Funder

JiangSu Electric Power Co., Ltd. Suzhou Branch, Suzhou, China

Swiss National Science Foundation

Publisher

MDPI AG

Subject

Atmospheric Science,Environmental Science (miscellaneous)

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