利用深度学习模型在线数据同化重建热带太平洋上层海洋

IF 4.4 2区 地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES
Zilu Meng, Gregory J. Hakim
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引用次数: 0

摘要

基于变压器架构的深度学习(DL)模型在气候模式数据集上进行了训练,并与热带太平洋地区的标准线性反演模型(LIM)进行了比较。在对再分析数据集进行测试时,我们发现深度学习模型比线性反演模型能做出更准确的预测。然后,我们评估了集合卡尔曼滤波器从 24 个海面温度观测数据中重建月平均上层海洋的能力,这 24 个海面温度观测数据是模仿现有的珊瑚代用测量数据设计的,并比较了 DL 模式和 LIM 模式的结果。由于 DL 模型中的信号阻尼,我们采用了一种新颖的膨胀技术,即添加来自后报实验的噪声。结果表明,在观测平均时间为 1 个月到 1 年的情况下,用 DL 模式同化观测数据比 LIM 模式能得到更好的重建结果。重建效果的改善得益于 DL 模式预测能力的增强,它将过去观测数据的记忆映射到了未来的同化时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Reconstructing the Tropical Pacific Upper Ocean Using Online Data Assimilation With a Deep Learning Model

Reconstructing the Tropical Pacific Upper Ocean Using Online Data Assimilation With a Deep Learning Model

A deep learning (DL) model, based on a transformer architecture, is trained on a climate-model data set and compared with a standard linear inverse model (LIM) in the tropical Pacific. We show that the DL model produces more accurate forecasts compared to the LIM when tested on a reanalysis data set. We then assess the ability of an ensemble Kalman filter to reconstruct the monthly averaged upper ocean from a noisy set of 24 sea-surface temperature observations designed to mimic existing coral proxy measurements, and compare results for the DL model and LIM. Due to signal damping in the DL model, we implement a novel inflation technique by adding noise from hindcast experiments. Results show that assimilating observations with the DL model yields better reconstructions than the LIM for observation averaging times ranging from 1 month to 1 year. The improved reconstruction is due to the enhanced predictive capabilities of the DL model, which map the memory of past observations to future assimilation times.

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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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