OMuLeT: Online Multi-Lead Time Location Prediction for Hurricane Trajectory Forecasting
OMuLeT: Online Multi-Lead Time Location Prediction for Hurricane Trajectory Forecasting
复制标题
OMuLeT：用于飓风轨迹预报的在线多引线时间位置预测
DOI：
10.1609/aaai.v34i01.5444
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发表时间：
2020
影响因子：
1.4
通讯作者：
L. Luo
中科院分区：
文献类型：
--
作者：
Ding Wang;Boyang Liu;P. Tan;L. Luo
飓风是强大的热带气旋，持续风速从至少74英里每小时（1级风暴）到超过157英里每小时（5级风暴）。准确预测风暴路径对于飓风防备和减轻风暴影响至关重要。在本文中，我们铸造的飓风轨迹预测任务作为一个在线的多提前时间位置预测问题，并提出了一个框架，称为OMuLeT，以提高路径预测相结合的6小时和12小时的预测产生的动力学（物理）飓风模型的合奏。OMuLeT采用在线学习与重启策略，随着新的观测数据变得可用，增量地更新集合模型组合的权重。它还可以处理不同的动力学模型，用于预测不同飓风的轨迹。使用大西洋和东太平洋飓风数据的实验结果表明，OMuLeT在48小时提前期预测方面明显优于各种基线方法，包括美国国家飓风中心（NHC）的官方预测，超过10%。
Hurricanes are powerful tropical cyclones with sustained wind speeds ranging from at least 74 mph (for category 1 storms) to more than 157 mph (for category 5 storms). Accurate prediction of the storm tracks is essential for hurricane preparedness and mitigation of storm impacts. In this paper, we cast the hurricane trajectory forecasting task as an online multi-lead time location prediction problem and present a framework called OMuLeT to improve path prediction by combining the 6-hourly and 12-hourly forecasts generated from an ensemble of dynamical (physical) hurricane models. OMuLeT employs an online learning with restart strategy to incrementally update the weights of the ensemble model combination as new observation data become available. It can also handle the varying dynamical models available for predicting the trajectories of different hurricanes. Experimental results using the Atlantic and Eastern Pacific hurricane data showed that OMuLeT significantly outperforms various baseline methods, including the official forecasts produced by the U.S. National Hurricane Center (NHC), by more than 10% in terms of its 48-hour lead time forecasts.