论文标题
飓风预测：一种新颖的多模式学习框架
Hurricane Forecasting: A Novel Multimodal Machine Learning Framework
论文作者
论文摘要
本文描述了一个新型的机器学习（ML）框架，用于热带气旋强度和轨道预测，结合了多种ML技术并利用了多种数据源。我们的多模式框架（称为Hurricast）有效地将时空数据与统计数据结合在一起，通过提取具有深度学习的编码器架构体系结构并通过梯度增强的树进行预测。我们在2016 - 2019年在北大西洋和东太平洋盆地进行了24小时的提前时间和强度预测评估我们的模型，并表明它们在秒内计算时实现了当前运行预测模型的可比平均绝对误差和技能。此外，将飓风纳入运营预测的共识模型可以改善国家飓风中心的官方预测，从而通过现有方法强调了互补物业。总而言之，我们的工作表明，利用机器学习技术结合不同的数据源可以带来热带气旋预测的新机会。
This paper describes a novel machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple ML techniques and utilizing diverse data sources. Our multimodal framework, called Hurricast, efficiently combines spatial-temporal data with statistical data by extracting features with deep-learning encoder-decoder architectures and predicting with gradient-boosted trees. We evaluate our models in the North Atlantic and Eastern Pacific basins on 2016-2019 for 24-hour lead time track and intensity forecasts and show they achieve comparable mean absolute error and skill to current operational forecast models while computing in seconds. Furthermore, the inclusion of Hurricast into an operational forecast consensus model could improve over the National Hurricane Center's official forecast, thus highlighting the complementary properties with existing approaches. In summary, our work demonstrates that utilizing machine learning techniques to combine different data sources can lead to new opportunities in tropical cyclone forecasting.
