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[2508.07428] Lightning Prediction under Uncertainty: DeepLight with Hazy Loss
Summary
The paper presents DeepLight, a novel deep learning architecture designed for predicting lightning occurrences by addressing the limitations of existing models, particularly in handling uncertainty and utilizing multi-source meteorological data.
Why It Matters
Accurate lightning prediction is crucial for safety and economic protection, especially as climate change increases the frequency of severe weather events. DeepLight's innovative approach enhances predictive capabilities, potentially reducing risks associated with lightning strikes.
Key Takeaways
- DeepLight improves lightning prediction accuracy by 18%-30% over existing models.
- Utilizes multi-source meteorological data to enhance spatial correlation capture.
- Introduces a novel Hazy Loss function to manage spatio-temporal uncertainties.
Computer Science > Machine Learning arXiv:2508.07428 (cs) [Submitted on 10 Aug 2025 (v1), last revised 14 Feb 2026 (this version, v2)] Title:Lightning Prediction under Uncertainty: DeepLight with Hazy Loss Authors:Md Sultanul Arifin, Abu Nowshed Sakib, Yeasir Rayhan, Tanzima Hashem View a PDF of the paper titled Lightning Prediction under Uncertainty: DeepLight with Hazy Loss, by Md Sultanul Arifin and 3 other authors View PDF HTML (experimental) Abstract:Lightning, a common feature of severe meteorological conditions, poses significant risks, from direct human injuries to substantial economic losses. These risks are further exacerbated by climate change. Early and accurate prediction of lightning would enable preventive measures to safeguard people, protect property, and minimize economic losses. In this paper, we present DeepLight, a novel deep learning architecture for predicting lightning occurrences. Existing prediction models face several critical limitations: i) they often struggle to capture the dynamic spatial context and the inherent randomness of lightning events, including whether lightning occurs and its variability in location and timing even under similar meteorological conditions; ii) they underutilize key observational data, such as radar reflectivity and cloud properties; and iii) they rely heavily on Numerical Weather Prediction (NWP) systems, which are both computationally expensive and highly sensitive to parameter settings. To overcome these challenge...
