U-Net卡尔曼滤波(UNetKF):机器学习辅助数据同化的一个例子

IF 4.4 2区 地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES
Feiyu Lu
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引用次数: 0

摘要

机器学习技术在天气和气候科学领域的普及程度大幅上升。数据同化(DA)结合了观测和数值模型,具有结合机器学习和人工智能(ML/AI)技术的巨大潜力。在本文中,我们使用一种卷积神经网络(CNN) U-Net来改进集成卡尔曼滤波(EnKF)算法的局部误差协方差。采用两层准地转模型,利用EnKF DA实验数据对U-Nets进行训练。然后将训练好的U-Nets成功地应用于U-Net卡尔曼滤波(UNetKF)实验中,以预测具有自适应定位和模型误差协方差的一些状态依赖特征的局部误差协方差。UNetKF与传统的三维变分(3DVar)、集成3DVar (En3DVar)和EnKF方法进行了比较。UNetKF的性能可以匹配或超过3DVar,或En3DVar和EnKF的小到中等集成尺寸。我们还证明,经过训练的U-Nets可以转移到UNetKF实现的更高分辨率模型,这再次与3DVar和EnKF相比具有竞争力,特别是对于小型集成规模。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

U-Net Kalman Filter (UNetKF): An Example of Machine Learning-Assisted Data Assimilation

U-Net Kalman Filter (UNetKF): An Example of Machine Learning-Assisted Data Assimilation

Machine learning techniques have seen a tremendous rise in popularity in weather and climate sciences. Data assimilation (DA), which combines observations and numerical models, has great potential to incorporate machine learning and artificial intelligence (ML/AI) techniques. In this paper, we use U-Net, a type of convolutional neutral network (CNN), to improve the localized error covariances for the Ensemble Kalman Filter (EnKF) algorithm. Using a 2-layer quasi-geostrophic model, U-Nets are trained using data from EnKF DA experiments. The trained U-Nets are then successfully implemented in U-Net Kalman Filter (UNetKF) experiments to predict localized error covariances that possess adaptive localization and some state-dependent features of the model error covariances. UNetKF is compared to traditional 3-dimensional variational (3DVar), ensemble 3DVar (En3DVar) and EnKF methods. The performance of UNetKF can match or exceed that of 3DVar, or En3DVar and EnKF for small to moderate ensemble sizes. We also demonstrate that trained U-Nets can be transferred to a higher-resolution model for UNetKF implementation, which again performs competitively to 3DVar and EnKF, particularly for small ensemble sizes.

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来源期刊
Journal of Advances in Modeling Earth Systems
Journal of Advances in Modeling Earth Systems METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
11.40
自引率
11.80%
发文量
241
审稿时长
>12 weeks
期刊介绍: The Journal of Advances in Modeling Earth Systems (JAMES) is committed to advancing the science of Earth systems modeling by offering high-quality scientific research through online availability and open access licensing. JAMES invites authors and readers from the international Earth systems modeling community. Open access. Articles are available free of charge for everyone with Internet access to view and download. Formal peer review. Supplemental material, such as code samples, images, and visualizations, is published at no additional charge. No additional charge for color figures. Modest page charges to cover production costs. Articles published in high-quality full text PDF, HTML, and XML. Internal and external reference linking, DOI registration, and forward linking via CrossRef.
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