Tropical cyclone (TC) motion has an important impact on both human lives and infrastructure. Predicting TC intensity is crucial, especially within the 24 h warning time. TC intensity change prediction can be regarded as a problem of both regression and classification. Statistical forecasting methods based on empirical relationships and traditional numerical prediction methods based on dynamical equations still have difficulty in accurately predicting TC intensity. In this study, a prediction algorithm for TC intensity changes based on deep learning is proposed by exploring the joint spatial features of three-dimensional (3D) environmental conditions that contain the basic variables of the atmosphere and ocean. These features can also be interpreted as fused characteristics of the distributions and interactions of these 3D environmental variables. We adopt a 3D convolutional neural network (3D-CNN) for learning the implicit correlations between the spatial distribution features and TC intensity changes. Image processing technology is also used to enhance the data from a small number of TC samples to generate the training set. Considering the instantaneous 3D status of a TC, we extract deep hybrid features from TC image patterns to predict 24 h intensity changes. Compared to previous studies, the experimental results show that the mean absolute error (MAE) of TC intensity change predictions and the accuracy of the classification as either intensifying or weakening are both significantly improved. The results of combining features of high and low spatial layers confirm that considering the distributions and interactions of 3D environmental variables is conducive to predicting TC intensity changes, thus providing insight into the process of TC evolution.

中文翻译：

---

基于周边环境条件的热带气旋强度变化预测与深度学习

热带气旋 (TC) 运动对人类生活和基础设施都有重要影响。预测 TC 强度至关重要，尤其是在 24 小时预警时间内。TC 强度变化预测可以看作是一个回归和分类的问题。基于经验关系的统计预报方法和基于动力学方程的传统数值预报方法在准确预报台风强度方面仍存在困难。本研究通过探索包含大气和海洋基本变量的三维（3D）环境条件的联合空间特征，提出了一种基于深度学习的台风强度变化预测算法。这些特征也可以解释为这些 3D 环境变量的分布和相互作用的融合特征。我们采用 3D 卷积神经网络 (3D-CNN) 来学习空间分布特征与 TC 强度变化之间的隐式相关性。图像处理技术也用于对少量TC样本的数据进行增强以生成训练集。考虑到 TC 的瞬时 3D 状态，我们从 TC 图像模式中提取深度混合特征来预测 24 小时强度变化。与以往的研究相比，实验结果表明，TC强度变化预测的平均绝对误差（MAE）和分类为增强或减弱的准确性均显着提高。