用深层神经网络预测的海洋.
Yingzhe Cui1,2, Ruohan Wu3, Xiang Zhang1,2
1Frontiers Science Center for Deep Ocean Multispheres and Earth System and Key Laboratory of Ocean dynamics/Academy of Future Ocean, Ocean University of China, Qingdao, China.
Nature communications
|March 6, 2025
概括
温海是一种新的人工智能系统,通过将物理与深度学习相结合,增强了全球海洋预测. 这种数据驱动的方法提高了中大尺度预测的准确性和效率.
科学领域:
- 海洋学 海洋学 海洋学
- 人工智能的人工智能
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 中级旋主导着上层海洋的变化,对传统的基于物理的数值模型构成挑战.
- 现有的人工智能方法在天气预报方面表现有希望,但由于不同的动态,在海洋学应用中面临障碍.
研究的目的:
- 利用深度神经网络 (DNN) 开发一个基于数据的,解决的全球海洋预测系统 (GOFS).
- 在基于AI的预测中,改进空海相互作用的表现,并保持海洋中层层的变化.
主要方法:
- 训练深度神经网络 (DNN) 用于温海全球海洋预测系统 (GOFS).
- 将动量,热量和淡水流量的大量公式纳入DNN.
- 设计DNN架构以利用海洋动力学并保持中等尺度的变化.
主要成果:
- 温海在最先进的数值和基于人工智能的GOFS上表现出卓越的性能.
- 对于温度和盐度概况,海面温度,海平面异常和近地电流,实现了准确的预测.
- 预测技能保持在从1天到至少10天的交付时间内.
结论:
- 专业指导的深度学习为推进全球海洋预测提供了一个有希望的方法.
- 温海代表了朝着更准确和更有效的海洋状态预测迈出的重要一步.
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