使用iTransformer模型预测中等尺度海洋表面温度和潜在热流合的长期预测
Xuwei Hu1, Yuan Feng1, Jiahao Liu1
1Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
一个新的iTransformer模型使用海面温度异常 (SSTa) 和潜热流异常 (LHFa) 的合,准确预测中介尺度的空海相互作用. 这一进步为气候变化预测提供了一条新的途径.
科学领域:
- 海洋学 海洋学 海洋学
- 气候科学 气候科学
- 人工智能的人工智能
背景情况:
- 西边界电流 (WBCs) 中间层空海相互作用显著影响中度气候变化.
- 当前的分析和预测方法依赖于高分辨率的数据和模型,这些数据和模型具有限制和长时间的处理时间.
研究的目的:
- 开发和评估一种新的iTransformer模型,用于预测中等尺度海洋表面温度异常 (SSTa) 和潜热流异常 (LHFa) 的合.
- 评估模型的性能与传统方法和气候模型相比.
主要方法:
- 使用了一个新开发的iTransformer模型,集成SSTa和LHFa合系数数据.
- 在过去的冬季的ERA5数据集上训练模型,并预测未来的合系数.
- 优化每个WBC的输入长度,以提高预测准确度.
主要成果:
- 与仅使用合系数相比,将SST数据纳入预测改进了3.0%.
- 该iTransformer模型成功地重现了中等尺度SSTa-LHFa合的线性趋势和平均值.
- 预测显示了令人满意的表现,可与中等尺度解决气候模型相比较.
结论:
- 该iTransformer模型提供了一种新且高效的途径,用于研究中介面空气-海相互作用变化.
- 这种方法为预测未来气候变化的准确性提供了一个有希望的工具.
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