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Lightning Prediction under Uncertainty: DeepLight with Hazy Loss

Md Sultanul Arifin, Abu Nowshed Sakib, Yeasir Rayhan, Tanzima Hashem

TL;DR

DeepLight leverages multi-source meteorological data, including radar reflectivity, cloud properties, and historical lightning occurrences through a dual-encoder architecture, and addresses the spatio-temporal uncertainty of lightning by penalizing deviations based on proximity to true events, enabling the model to better learn patterns amidst randomness.

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 challenges, DeepLight leverages multi-source meteorological data, including radar reflectivity, cloud properties, and historical lightning occurrences through a dual-encoder architecture. By employing multi-branch convolution techniques, it dynamically captures spatial correlations across varying extents. Furthermore, its novel Hazy Loss function explicitly addresses the spatio-temporal uncertainty of lightning by penalizing deviations based on proximity to true events, enabling the model to better learn patterns amidst randomness. Extensive experiments show that DeepLight improves the Equitable Threat Score (ETS) by 18\%--30\% over state-of-the-art methods, establishing it as a robust solution for lightning prediction.

Lightning Prediction under Uncertainty: DeepLight with Hazy Loss

TL;DR

DeepLight leverages multi-source meteorological data, including radar reflectivity, cloud properties, and historical lightning occurrences through a dual-encoder architecture, and addresses the spatio-temporal uncertainty of lightning by penalizing deviations based on proximity to true events, enabling the model to better learn patterns amidst randomness.

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 challenges, DeepLight leverages multi-source meteorological data, including radar reflectivity, cloud properties, and historical lightning occurrences through a dual-encoder architecture. By employing multi-branch convolution techniques, it dynamically captures spatial correlations across varying extents. Furthermore, its novel Hazy Loss function explicitly addresses the spatio-temporal uncertainty of lightning by penalizing deviations based on proximity to true events, enabling the model to better learn patterns amidst randomness. Extensive experiments show that DeepLight improves the Equitable Threat Score (ETS) by 18\%--30\% over state-of-the-art methods, establishing it as a robust solution for lightning prediction.

Paper Structure

This paper contains 32 sections, 5 equations, 7 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Related Works
  4. Lightning Prediction Models
  5. Correlation Modeling
  6. Methodologies
  7. Features
  8. Spatiotemporal Prediction Models
  9. Materials and Methods
  10. Multi-Branched Approach
  11. Convolutional Stem (CStem)
  12. Multi Branch Convolutional LSTM (MB-ConvLSTM)
  13. Fusion Module
  14. UpScaler
  15. Hazy Loss
  16. ...and 17 more sections

Figures (7)

  • Figure 1: Temporal and spatial correlations among lightning and other meteorological parameters. (Note: COD = Cloud Optical Depth; CTP = Cloud Top Pressure; CTH = Cloud Top Height from AWG Cloud Height Algorithm. Details of these parameters are discussed in Section \ref{['sec:prob']}.)
  • Figure 2: Network Architecture of DeepLight. (Note: MBCLSTM = Multi Branch Convolutional LSTM; CStem = Convolutional Stem)
  • Figure 3: Multi-Branched approaches used in DeepLight
  • Figure 4: Ground truth grid $L$ (left) and its Gaussian-blurred version $L_{\text{blur}}$ (right) at a particular timestep. White cells indicate lightning occurence and black cells indicate no lightning. Red dot marks a position close to an actual lightning flash, while the orange dot represents a distant location.
  • Figure 5: Validation ETS as a function of training epoch during progressive blurring-intensity analysis for Hazy Loss. Peak ETS is observed at epoch 4.
  • ...and 2 more figures