Weather forecasting requires comprehensive analysis of a variety of meteorological data. Recent decades have witnessed the advance of weather observation and simulation technologies, triggering an explosion of meteorological data which are collected from multiple sources (e.g., radar, automatic stations and numerical weather prediction) and usually characterized by a spatiotemporal (ST) structure. As a result, the adequate exploition of these multi‐source ST data emerges as a promising but challenging topic for weather forecasting. To address this issue, we propose a data‐driven forecasting framework (referred to as LightNet+) based on deep neural networks using a lightning scenario. Our framework design enables LightNet+ to make forecasts by mining complementary information distributed across multiple data sources, which may be heterogeneous in spatial (continuous versus discrete) and temporal (observations from the past versus simulation of the future) domains. We evaluate LightNet+ using a real‐world weather dataset in North China. The experimental results demonstrate: (a) LightNet+ produces significantly better forecasts than three established lightning schemes, and (b) the more data sources are fed into LightNet+, the higher forecasting quality it achieves.

Lightning prediction is a complicated and challenging task requiring meteorologists to integrate information from multiple data sources to make decisions. Although some data-driven models have been proposed to make prediction automatically, most of them are based on a single data source or several basically-homogeneous data sources, making them hard to adapt to complex and diverse data in practice. In this work, we propose a heterogeneous spatiotemporal network (HSTN) for lightning prediction, aiming at mining knowledge from several heterogeneous spatiotemporal (ST) data sources. Specifically, HSTN comprises three modules: Gaussian diffusion module, ST encoder and ST decoder. Noting that most of meteorological data can be formatted into either a dense ST tensor or a sparse ST tensor, the ST encoder, with the help of the Gaussian diffusion module, is designed to extract information from both two types of tensors. On the other hand, ST decoder is responsible for merging all information from the other modules and generate the final prediction. By organically combining the three modules, HSTN can handle complex input with heterogeneity in both space and time domains. We conduct experimental evaluations on a real-world lightning dataset. The results demonstrate that HSTN achieves state-of-the-art performance compared with several established baselines.

Lightning as a natural phenomenon poses serious threats to human life, aviation and electrical infrastructures. Lightning prediction plays a vital role in lightning disaster reduction. Existing prediction methods, usually based on numerical weather models, rely on lightning parameterization schemes for forecasting. These methods, however, have two drawbacks. Firstly, simulations of the numerical weather models usually have deviations in space and time domains, which introduces irreparable biases to subsequent parameterization processes. Secondly, the lightning parameterization schemes are designed manually by experts in meteorology, which means these schemes can hardly benefit from abundant historical data. In this work, we propose a data-driven model based on neural networks, referred to as LightNet, for lightning prediction. Unlike the conventional prediction methods which are fully based on numerical weather models, LightNet introduces recent lightning observations in an attempt to calibrate the simulations and assist the prediction. LightNet first extracts spatiotemporal features of the simulations and observations via dual encoders. These features are then combined by a fusion module. Finally, the fused features are fed into a spatiotemporal decoder to make forecasts. We conduct experimental evaluations on a real-world North China lightning dataset, which shows that LightNet achieves a threefold improvement in equitable threat score for six-hour prediction compared with three established forecast methods.

Accurate lightning forecast is significant for disaster prevention and reduction. However, the mainstream lightning forecast methods, which mainly rely on numerical simulations and parameterizations, can hardly cope with the spatiotemporal deviations. Meanwhile, the rapid and complex evolution of lightning regions go beyond the traditional extrapolation-based forecast methods. In this work, we propose a data-driven neural network model for hourly lightning forecast, which exploits both the numerical simulations and the recent historical lightning observations. The two kinds of data complement each other and play different roles at different stages of the forecast. The use of dual-source data greatly increases the amount of information available to improve the forecasting performance. To handle the variability of deviation patterns in numerical simulations, we introduce a channel-wise attention mechanism, which adaptively adjusts the proportion of each simulated parameter to maximize the useful information. The attention mechanism also enables the model to reveal the contribution of each simulated parameter for the forecast. Experimental results on a real-world dataset show that the proposed method outperforms several baseline methods. Ablation studies further demonstrate the effectiveness of our data fusion approach and attention module.