Mj\"olnir: A Deep Learning Parametrization Framework for Global Lightning Flash Density
Abstract
Recent advances in AI-based weather forecasting models, such as FourCastNet, Pangu-Weather, and GraphCast, have demonstrated the remarkable ability of deep learning to emulate complex atmospheric dynamics. Building on this momentum, we propose Mj\"olnir, a novel deep learning-based framework for global lightning flash density parameterization. Trained on ERA5 atmospheric predictors and World Wide Lightning Location Network (WWLLN) observations at a daily temporal resolution and 1 degree spatial resolution, Mj\"olnir captures the nonlinear mapping between large-scale environmental conditions and lightning activity. The model architecture is based on the InceptionNeXt backbone with SENet, and a multi-task learning strategy to simultaneously predict lightning occurrence and magnitude. Extensive evaluations yield that Mollnir accurately reproduces the global distribution, seasonal variability, and regional characteristics of lightning activity, achieving a global Pearson correlation coefficient of 0.96 for annual mean fields. These results suggest that Mj\"olnir serves not only as an effective data-driven global lightning parameterization but also as a promising AI-based scheme for next-generation Earth system models (AI-ESMs).
Cite
@article{arxiv.2504.19822,
title = {Mj\"olnir: A Deep Learning Parametrization Framework for Global Lightning Flash Density},
author = {Minjong Cheon},
journal= {arXiv preprint arXiv:2504.19822},
year = {2025}
}
Comments
After an internal review, we found that the current version does not meet our intended academic standards due to incomplete descriptions and insufficient detail in key sections. No revised manuscript can be prepared in the near future. To ensure academic quality, we withdraw this version and plan to resubmit when the work is substantially improved