Multi-Task Lightning Nowcasting with Spatio-Temporal Focal Perception and Synergistic Weighted Loss

TANG Zhihao; HAN Yuanpeng; ZHANG Hui; SONG Lin; ZHANG Qilin; LIU Yi

doi:10.11999/JEIT260234

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TANG Zhihao, HAN Yuanpeng, ZHANG Hui, SONG Lin, ZHANG Qilin, LIU Yi. Multi-Task Lightning Nowcasting with Spatio-Temporal Focal Perception and Synergistic Weighted Loss[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260234

Citation:

TANG Zhihao, HAN Yuanpeng, ZHANG Hui, SONG Lin, ZHANG Qilin, LIU Yi. Multi-Task Lightning Nowcasting with Spatio-Temporal Focal Perception and Synergistic Weighted Loss[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260234

Citation:

TANG Zhihao, HAN Yuanpeng, ZHANG Hui, SONG Lin, ZHANG Qilin, LIU Yi. Multi-Task Lightning Nowcasting with Spatio-Temporal Focal Perception and Synergistic Weighted Loss[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260234

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Multi-Task Lightning Nowcasting with Spatio-Temporal Focal Perception and Synergistic Weighted Loss

doi: 10.11999/JEIT260234 cstr: 32379.14.JEIT260234

1.
Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, B-DAT, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
State Key Laboratory of Climate System Prediction and Risk Management/ Key Laboratory of Meteorological Disaster, Ministry of Education/ Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China
3.
Qingdao Ecological and Agricultural Meteorological Center, Qingdao Meteorological Bureau, Qingdao 266003, China

Accepted Date: 2026-06-15
Rev Recd Date: 2026-06-15

Available Online: 2026-06-19

Abstract

Abstract

Objective Lightning nowcasting is vital for early warning systems and the protection of critical infrastructure such as aviation, power grids, and transportation. Traditional numerical weather prediction models suffer from parameterization dependencies and high computational costs, making them inefficient for rapid-update nowcasting. Existing deep learning methods, despite progress, inadequately handle extreme data sparsity, suffer from serial computation bottlenecks in recurrent architectures, and primarily focus on binary occurrence prediction rather than the synergistic optimization of frequency estimation and regional localization. Moreover, conventional loss functions are easily dominated by vast non-lightning areas, causing biased predictions toward zero or generating excessive false alarms. To address these limitations, this paper proposes STF-Net, a multi-task lightning nowcasting model that jointly predicts lightning frequency and occurrence regions through three key innovations: a Lightning Adaptive Attention Module (LAAM) for explicit spatio-temporal dependency modeling, a Spatio-Temporally Weighted Hybrid Loss function to tackle data sparsity and imbalance, and a spatio-temporal dual-branch Generative Adversarial Network (GAN) to enhance prediction fidelity and temporal coherence. Methods STF-Net is built upon the SimVP video prediction architecture, adopting an encoder-translator-decoder paradigm. The Lightning Adaptive Attention Module (LAAM) employs a three-dimensional decoupled attention mechanism along height, width, and channel dimensions, enabling adaptive focus on convectively sensitive regions while maintaining computational efficiency. The Spatio-Temporally Weighted Hybrid Loss combines Temporally-Weighted Mean Squared Error (TW-MSE) for frequency regression accuracy and Dual-Weighted Cross-Entropy Loss (DWCE) for precise regional identification, incorporating time-increasing weights to enhance medium-to-long-term forecast robustness (Fig. 5). The DWCE loss innovatively integrates static class weights with dynamic grid weights, effectively balancing global class proportions and local lightning frequency heterogeneity.A spatio-temporal dual-branch GAN, comprising a spatial PatchGAN discriminator and a temporal 3D convolutional discriminator, is introduced to improve the textural fidelity and temporal coherence of the predicted lightning frequency fields. The model processes 6 consecutive historical lightning frequency frames (256×256 resolution, 10-minute intervals) to predict the next 6 frames, corresponding to a 1-hour forecast window. Experiments are conducted on a high-resolution Very Low Frequency Lightning Location Network (VLF-LLN) dataset containing 11,748 images covering diverse seasonal and weather conditions, split 7:3 for training and testing. Results and Discussions Comprehensive evaluation metrics are employed, including frequency regression accuracy (MSE, MAE computed only on lightning-occurring pixels), image quality fidelity (PSNR, SSIM), and regional detection skills (POD, FAR, CSI). STF-Net achieves a Critical Success Index (CSI) of 0.663 within the 1-hour forecast window, a 14.5% improvement over the SimVP baseline (0.579), and reduces the False Alarm Rate (FAR) from 0.351 to 0.216, a relative reduction of 38.5% (Table 1). Ablation studies systematically validate each component: adding GAN improves CSI to 0.624 and reduces MSE to 0.109; further incorporating LAAM increases CSI to 0.629 with the highest POD of 0.894; the complete STF-Net with Hybrid Loss achieves optimal performance with CSI of 0.663 and MSE of 0.105 (Table 1, Fig. 5). Critically, the synergistic prediction of frequency and region is evidenced by the simultaneous improvement in both regression (MSE/MAE) and detection (CSI/FAR) metrics. Time-step analysis reveals that LAAM significantly mitigates long-term performance degradation, with STF-Net maintaining the highest CSI compared to SimVP+GAN and SimVP (Fig. 6). Comparative experiments against ConvLSTM and PredRNN demonstrate STF-Net's superiority across all lead times: it consistently achieves higher CSI and lower FAR, with the advantage becoming more pronounced as the forecast horizon extends (Fig. 7). The consistent advantage in PSNR and SSIM further underscores the spatio-temporal GAN's role in producing coherent, detail-rich predictions.Visualization results show that STF-Net generates structurally clear, continuous lightning activity regions centered on high-frequency areas, accurately tracking dynamic evolution patterns including movement, merging, and splitting while producing minimal noise in non-lightning regions, demonstrating effective collaborative prediction of both frequency magnitude and spatial distribution . Conclusions This paper presents STF-Net, a novel deep learning model that achieves synergistic lightning frequency and regional prediction through three core contributions: (1) the Lightning Adaptive Attention Module (LAAM) explicitly models long-range spatio-temporal dependencies and focuses on critical convective zones; (2) the Spatio-Temporally Weighted Hybrid Loss effectively addresses extreme data sparsity and class imbalance, simultaneously optimizing frequency regression accuracy and regional localization precision while suppressing false alarms; and (3) the spatio-temporal dual-branch GAN enhances the spatial structural consistency of predictions and temporal coherence of predictions. Experimental results demonstrate that STF-Net significantly outperforms baseline and state-of-the-art models, achieving superior CSI of 0.663, reduced FAR of 0.216, and optimal MSE/MAE values within a 1-hour forecast window. The model effectively mitigates long-term performance degradation, accurately captures lightning region evolution trends, and generates physically plausible predictions with minimal background noise. This research provides an efficient, end-to-end solution for operational lightning nowcasting systems and offers new insights for model design in sparse meteorological spatio-temporal sequence prediction.
- Lightning location forecasting,
- Lightning frequency forecasting,
- Spatio-temporal weighted loss function,
- Attention mechanism,
- Disaster early warning

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References(24)

References

[1]	MISHRA M, ACHARYYA T, SANTOS C A G, et al. Mapping main risk areas of lightning fatalities between 2000 and 2020 over Odisha state (India): A diagnostic approach to reduce lightning fatalities using statistical and spatiotemporal analyses[J]. International Journal of Disaster Risk Reduction, 2022, 79: 103145. doi: 10.1016/j.ijdrr.2022.103145.
[2]	SONG Yang, XU Cangsu, LI Xiaolu, et al. Lightning-induced wildfires: An overview[J]. Fire, 2024, 7(3): 79. doi: 10.3390/fire7030079.
[3]	王昊亮, 刘玉宝, 赵天良, 等. 基于数值天气模式及其模式输出的闪电预报研究进展[J]. 地球科学进展, 2017, 32(1): 44–55. doi: 10.11867/j.issn.1001-8166.2017.01.0044. WANG Haoliang, LIU Yubao, ZHAO Tianliang, et al. Progress in lightning forecast by using numerical weather models and model outputs[J]. Advances in Earth Science, 2017, 32(1): 44–55. doi: 10.11867/j.issn.1001-8166.2017.01.0044.
[4]	PARAMANIK M M R, RABBANI K M G, IMRAN A, et al. Prediction of lightning activity over Bangladesh using diagnostic and explicit lightning parameterizations of WRF model[J]. Natural Hazards, 2024, 120(5): 4399–4422. doi: 10.1007/s11069-023-06355-6.
[5]	FEDERICO S, TORCASIO R C, LAGASIO M, et al. A year-long total lightning forecast over Italy with a dynamic lightning scheme and WRF[J]. Remote Sensing, 2022, 14(14): 3244. doi: 10.3390/rs14143244.
[6]	BATTAGLIOLI F, GROENEMEIJER P, TSONEVSKY I, et al. Forecasting large hail and lightning using additive logistic regression models and the ECMWF reforecasts[J]. Natural Hazards and Earth System Sciences, 2023, 23(12): 3651–3669. doi: 10.5194/nhess-23-3651-2023.
[7]	谈玲, 康瑞星, 夏景明, 等. 融合多源异构气象数据的光伏功率预测模型[J]. 电子与信息学报, 2024, 46(2): 503–517. doi: 10.11999/JEIT230731. TAN Ling, KANG Ruixing, XIA Jingming, et al. A photovoltaic power prediction model integrating multi-source heterogeneous meteorological data[J]. Journal of Electronics & Information Technology, 2024, 46(2): 503–517. doi: 10.11999/JEIT230731.
[8]	孙强, 赵珂. 嵌入多阶泰勒微分知识的多尺度注意力循环网络深度时空序列预测方法[J]. 电子与信息学报, 2024, 46(6): 2605–2618. doi: 10.11999/JEIT231108. SUN Qiang and ZHAO Ke. Multi-scale attention recurrent network with multi-order Taylor differential knowledge for deep spatiotemporal sequence prediction[J]. Journal of Electronics & Information Technology, 2024, 46(6): 2605–2618. doi: 10.11999/JEIT231108.
[9]	. SHI Xingjian, CHEN Zhourong, WANG Hao, et al. Convolutional LSTM network: A machine learning approach for precipitation nowcasting[C]. Proceedings of the 29th International Conference on Neural Information Processing Systems, Montreal, Canada, 2015: 802–810.
[10]	CINTINEO J L, PAVOLONIS M J, and SIEGLAFF J M. ProbSevere LightningCast: A deep-learning model for satellite-based lightning nowcasting[J]. Weather and Forecasting, 2022, 37(7): 1239–1257. doi: 10.1175/WAF-D-22-0019.1.
[11]	. WANG Yunbo, LONG Mingsheng, WANG Jianmin, et al. PredRNN: Recurrent neural networks for predictive learning using spatiotemporal LSTMs[C]. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, USA, 2017: 879–888.
[12]	黄兴友, 马玉蓉, 胡苏蔓. 基于深度学习的天气雷达回波序列外推及效果分析[J]. 气象学报, 2021, 79(5): 817–827. doi: 10.11676/qxxb2021.041. HUANG Xingyou, MA Yurong, and HU Suman. Extrapolation and effect analysis of weather radar echo sequence based on deep learning[J]. Acta Meteorologica Sinica, 2021, 79(5): 817–827. doi: 10.11676/qxxb2021.041.
[13]	LI Fengquan, LI Jian, GU Shanqiang, et al. Nowcasting of cloud-to-ground lightning location and frequency based on a deep learning technique[J]. Atmospheric and Oceanic Science Letters, 2025, 19(3): 100607. doi: 10.1016/j.aosl.2025.100607.
[14]	任照环, 林锐, 周浩, 等. 基于深度学习模型的雷电落区预报[J]. 热带气象学报, 2024, 40(5): 758–766. doi: 10.16032/j.issn.1004-4965.2024.068. REN Zhaohuan, LIN Rui, ZHOU Hao, et al. Lightning strike location prediction using a deep learning model[J]. Journal of Tropical Meteorology, 2024, 40(5): 758–766. doi: 10.16032/j.issn.1004-4965.2024.068.
[15]	ARIFIN S, SAKIB N, RAYHAN Y, et al. Lightning prediction under uncertainty: Deeplight with hazy loss[J]. Expert Systems with Applications, 2026, 314: 131586. doi: 10.1016/j.eswa.2026.131586.
[16]	. KUMAR R, SHARMA T, VAGHELA V, et al. PrecipFormer: Efficient transformer for precipitation downscaling[C]. Proceedings of the 2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW), Tucson, USA, 2025: 452–460. doi: 10.1109/WACVW65960.2025.00056.
[17]	职为梅, 常智, 卢俊华, 等. 面向不平衡图像数据的对抗自编码器过采样算法[J]. 电子与信息学报, 2024, 46(11): 4208–4218. doi: 10.11999/JEIT240330. ZHI Weimei, CHANG Zhi, LU Junhua, et al. Adversarial autoencoders oversampling algorithm for imbalanced image data[J]. Journal of Electronics & Information Technology, 2024, 46(11): 4208–4218. doi: 10.11999/JEIT240330.
[18]	CAVAIOLA M, CASSOLA F, SACCHETTI D, et al. Hybrid AI-enhanced lightning flash prediction in the medium-range forecast horizon[J]. Nature Communications, 2024, 15(1): 1188. doi: 10.1038/s41467-024-44697-2.
[19]	. GAO Zhangyang, TAN Cheng, WU Lirong, et al. SimVP: Simpler yet better video prediction[C]. Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, 2022: 3160–3170. doi: 10.1109/CVPR52688.2022.00317.
[20]	TIAN Jie, HAN Wei, SUN Haofei, et al. A dual-weighted loss function for lightning nowcasting[J]. Atmospheric and Oceanic Science Letters, 2026, 19(3): 100778. doi: 10.1016/j.aosl.2026.100778.
[21]	LI Jie, DAI Bingzhe, ZHOU Jiahao, et al. Preliminary application of long-range lightning location network with equivalent propagation velocity in China[J]. Remote Sensing, 2022, 14(3): 560. doi: 10.3390/rs14030560.
[22]	SRIVASTAVA A, LIU Dongxia, XU Chen, et al. Lightning nowcasting with an algorithm of thunderstorm tracking based on lightning location data over the Beijing area[J]. Advances in Atmospheric Sciences, 2022, 39(1): 178–188. doi: 10.1007/s00376-021-0398-2.
[23]	周康辉, 郑永光, 蓝渝. 基于闪电数据的雷暴识别、追踪与外推方法[J]. 应用气象学报, 2016, 27(2): 173–181. doi: 10.11898/1001-7313.20160205. ZHOU Kanghui, ZHENG Yongguang, and LAN Yu. Flash cell identification, tracking and nowcasting with lightning data[J]. Journal of Applied Meteorological Science, 2016, 27(2): 173–181. doi: 10.11898/1001-7313.20160205.
[24]	LIU Yi, WANG Jiale, SONG Yang, et al. Lightning nowcasting based on high-density area and extrapolation utilizing long-range lightning location data[J]. Atmospheric Research, 2025, 321: 108070. doi: 10.1016/j.atmosres.2025.108070.