论文标题
深度学习的雷暴现象：多危险数据融合模型
Thunderstorm nowcasting with deep learning: a multi-hazard data fusion model
论文作者
论文摘要
在包括急救人员，基础设施管理和航空在内的多个部门需要对雷暴相关危害的预测。为了满足这种需求，我们提出了一个可以适应不同危害类型的深度学习模型。该模型可以利用多个数据源；我们使用来自天气雷达，闪电检测，卫星可见/红外图像，数值天气预测和数字高程模型的数据。我们证明了模型在1 km分辨率网格上概率地预测闪电，冰雹和大降水的能力，时间分辨率为5分钟，交货时间为60分钟。 Shapley值量化了不同数据源的重要性，表明天气雷达产品是所有三种危险类型的最重要预测因子。
Predictions of thunderstorm-related hazards are needed in several sectors, including first responders, infrastructure management and aviation. To address this need, we present a deep learning model that can be adapted to different hazard types. The model can utilize multiple data sources; we use data from weather radar, lightning detection, satellite visible/infrared imagery, numerical weather prediction and digital elevation models. We demonstrate the ability of the model to predict lightning, hail and heavy precipitation probabilistically on a 1 km resolution grid, with a temporal resolution of 5 min and lead times up to 60 min. Shapley values quantify the importance of the different data sources, showing that the weather radar products are the most important predictors for all three hazard types.
