Fast and accurate prediction of hurricane evolution from genesis onwards is needed to reduce loss of life and enhance community resilience. In this work, a novel model development methodology for predicting storm trajectory is proposed based on two classes of Recurrent Neural Networks (RNNs). The RNN models are trained on input features available in or derived from the HURDAT2 North Atlantic hurricane database maintained by the National Hurricane Center (NHC). The models use probabilities of storms passing through any location, computed from historical data. A detailed analysis of model forecasting error shows that Many-To-One prediction models are less accurate than Many-To-Many models owing to compounded error accumulation, with the exception of $6-hr$ predictions, for which the two types of model perform comparably. Application to 75 or more test storms in the North Atlantic basin showed that, for short-term forecasting up to 12 hours, the Many-to-Many RNN storm trajectory prediction models presented herein are significantly faster than ensemble models used by the NHC, while leading to errors of comparable magnitude.
翻译:为了减少生命损失和提高社区复原力,需要快速准确地预测飓风从起源起的演变情况。在这项工作中,根据两类经常性神经网络(神经网络)提出了预测风暴轨迹的新模式开发方法;对RNN模型进行了关于国家飓风中心(国家飓风中心)维护的HURDAT2北大西洋飓风数据库现有或衍生的投入特点的培训。模型使用从历史数据计算的任何地点的风暴概率。模型预测错误的详细分析表明,由于复杂的错误积累,“多到一个”预测模型不如“多到多个”模型准确,只有6千元的预测除外,两种模型可与之比较。对北大西洋盆地75个或更多的试验风暴的应用表明,在短期预测中,许多到Many RNN的风暴轨迹预测模型比NHC使用的混合模型要快得多,同时导致类似程度的误差。