Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity
Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity
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DOI：
10.1029/2022ms003596
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发表时间：
2023-10
影响因子：
6.8
通讯作者：
D. Fu;P. Chang;Xue Liu
中科院分区：
文献类型：
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作者：
D. Fu;P. Chang;Xue Liu
热带气旋（TC）的形成需要良好的大尺度环境条件。基于这些联系，人们做出了许多努力，以定量的方式建立季节性TC活动与大规模环境可持续性之间的经验关系，这导致了TC成因指数等概念函数。然而，由于可靠的TC观测和气候系统的复杂性的有限数量，一个简单的分析函数可能不是TC和它们的环境之间的经验关系的准确写照。在这项研究中，我们使用卷积神经网络（CNN）来解开这种复杂的关系。为了规避季节性TC观测记录的有限数量，我们首先在具有真实季节性TC活动和大规模环境条件的高分辨率气候模型模拟套件上实施转移学习技术来训练CNN的集合，然后在1950年至2019年的最先进的再分析上进行训练。经过训练的CNN可以很好地再现历史TC记录，并在业务气候预报提供大规模环境输入时产生重要的季节预测技能。此外，通过输入20世纪世纪再分析产品和耦合模式相互比较项目（CMIP 6）模拟的集合CNN，我们研究了TC的变化及其在过去和未来的气候变化。具体来说，我们的集合CNN预测未来变暖情景中全球平均TC活动的下降趋势，这与我们使用高分辨率气候模型的未来预测一致。
It has been widely recognized that tropical cyclone (TC) genesis requires favorable large‐scale environmental conditions. Based on these linkages, numerous efforts have been made to establish an empirical relationship between seasonal TC activities and large‐scale environmental favorability in a quantitative way, which lead to conceptual functions such as the TC genesis index. However, due to the limited amount of reliable TC observations and complexity of the climate system, a simple analytic function may not be an accurate portrait of the empirical relationship between TCs and their ambiences. In this research, we use convolution neural networks (CNNs) to disentangle this complex relationship. To circumvent the limited amount of seasonal TC observation records, we implement transfer‐learning technique to train ensemble of CNNs first on suites of high‐resolution climate model simulations with realistic seasonal TC activities and large‐scale environmental conditions, and then on a state‐of‐the‐art reanalysis from 1950 to 2019. The trained CNNs can well reproduce the historical TC records and yields significant seasonal prediction skills when the large‐scale environmental inputs are provided by operational climate forecasts. Furthermore, by inputting the ensemble CNNs with 20th century reanalysis products and Phase 6 of the Coupled Model Intercomparison Project (CMIP6) simulations, we investigated TC variability and its changes in the past and future climates. Specifically, our ensemble CNNs project a decreasing trend of global mean TC activity in the future warming scenario, which is consistent with our future projections using high‐resolution climate model.