An ensemble machine learning model for tropical cyclone (TC) track forecasts over the Western North Pacific was developed and evaluated in this study. First, we investigated predictors including TC climatology and persistence factors which were extracted from TC best-track dataset and storm’s surrounding atmospheric conditions which were extracted from ERA-Interim reanalysis. Then, we built a Gradient Boosting Decision Tree (GBDT) nonlinear model for TC track forecasts, in which 30-year data was used. Finally, using tenfold cross-validation method, the GBDT model was compared with a frequently used technique: climatology and persistence (CLIPER) model. The experimental results show that the GBDT model performs well in three forecast times (24 h, 48 h, and 72 h) with relatively small forecast error of 138, 264, and 363.5 km, respectively. The model obtains excellent TC moving direction aspects. However, the model is still insufficient to produce aspects of storm acceleration and deceleration, with mean moving velocity sensitivities all less than 60%. Nevertheless, the model obtains much more robust and accurate TC tracks relative to CLIPER model, where the forecast skills are 17.5%, 26.3%, and 32.1% at three forecast times, respectively. The presented study demonstrates that the GBDT model could provide reliable evidence and guidance for operational TC track forecasts.

在这项研究中，开发并评估了西北太平洋西部热带气旋（TC）航迹预报的整体机器学习模型。首先，我们调查了从TC最佳跟踪数据集中提取的TC气候和持久性因子以及从ERA-Interim重新分析中提取的风暴周围大气状况的预测因子。然后，我们为TC航迹预测建立了梯度提升决策树（GBDT）非线性模型，其中使用了30年的数据。最后，使用十倍交叉验证方法，将GBDT模型与一种常用技术进行了比较：气候学和持久性（CLIPER）模型。实验结果表明，GBDT模型在三个预测时间（24 h，48 h和72 h）中表现良好，分别具有138、264和363.5 km的相对较小的预测误差。该模型获得了出色的TC运动方向方面。但是，该模型仍然不足以产生风暴加速和减速方面的信息，平均移动速度敏感性均小于60％。然而，相对于CLIPER模型，该模型获得的鲁棒性和准确性更高的TC跟踪，在三个预测时间，其预测技能分别为17.5％，26.3％和32.1％。提出的研究表明，GBDT模型可以为运行的TC轨迹预报提供可靠的证据和指导。其中三个预测时间的预测技能分别为17.5％，26.3％和32.1％。提出的研究表明，GBDT模型可以为运行的TC轨迹预报提供可靠的证据和指导。其中三个预测时间的预测技能分别为17.5％，26.3％和32.1％。提出的研究表明，GBDT模型可以为运行的TC轨迹预报提供可靠的证据和指导。