基于存储器的参数化与可微分解器:应用到洛伦兹'96的应用
Mohamed Aziz Bhouri1, Pierre Gentine1
1Department of Earth and Environmental Engineering, Columbia University, New York, New York 10027, USA.
Chaos (Woodbury, N.Y.)
|July 6, 2023
概括
新的基于记忆的神经网络通过更好地代表小规模过程来改善天气和气候模型. 这种方法提高了预测准确性和稳定性,克服了当前机器学习参数化的局限性.
科学领域:
- 大气科学 大气科学
- 气候建模气候模型
- 机器学习 机器学习
背景情况:
- 物理参数表示气候模型中未解决的子网过程.
- 机器学习参数化显示出希望,但与过程随机性斗争.
研究的目的:
- 开发一种基于记忆的神经网络参数化的新方法,以解决随机性和提高预测准确性.
- 提高气候模型中关闭的稳定性和非即时响应.
主要方法:
- 实现基于记忆的神经网络,具有可微分解答器.
- 将新的参数化应用于Lorenz '96模型,具有粗略的时间分辨率.
主要成果:
- 基于内存的参数化证明了在长时间内有技巧的预测.
- 与即时参数化相比,实现了更好的预测准确性和稳定性.
结论:
- 基于内存的参数化为气候建模中的关闭问题提供了一个有希望的解决方案.
- 这种方法可以减少与随机过程相关的不确定性.
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