An ensemble-based data assimilation system for forecasting variability of the Northwestern Pacific ocean

IF 2.2 3区 地球科学 Q2 OCEANOGRAPHY
Yasumasa Miyazawa, Max Yaremchuk, Sergey M. Varlamov, Toru Miyama, Yu-Lin K. Chang, Hakase Hayashida
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Abstract

An adjoint-free four-dimensional variational (a4dVar) data assimilation (DA) is implemented in an operational ocean forecast system based on an eddy-resolving ocean general circulation model for the Northwestern Pacific. Validation of the system against independent observations demonstrates that fitting the model to time-dependent satellite altimetry during a 10-day DA window leads to substantial skill improvements in the succeeding 60-day hindcast. The a4dVar corrects representation of the Kuroshio path variation south of Japan by adjusting the dynamical balance between amplitude/wavelength of the meander and flow advection. A larger ensemble spread tends to reduce the skill in representing the observed sea surface height anomaly, suggesting that it is possible to use the ensemble information for quantifying the forecast error. The ensemble information is also utilized for modification of the background error covariance (BEC), which improves the accuracy of temperature and salinity distributions. The modified BEC yields the skill decline of the Kuroshio path variation during the 60-day hindcast period, and the ensemble sensitivity analysis shows that changes in the dynamical balance caused by the ensemble BEC result in such skill deterioration.

Abstract Image

预报西北太平洋海洋变化的集合数据同化系统
在一个基于西北太平洋涡旋解算海洋大气环流模式的业务化海洋预报系统中,实施了无邻接四维变分(a4dVar)数据同化(DA)。根据独立观测数据对该系统进行的验证表明,在 10 天的数据同化窗口期间,将模型与随时间变化的卫星测高数据进行拟合,可大幅提高后续 60 天的后报精度。a4dVar 通过调整蜿蜒的振幅/波长与气流平流之间的动态平衡,修正了日本以南黑潮路径变化的表示。较大的集合散布往往会降低对观测到的海面高度异常的描述能力,这表明可以利用集合信息来量化预报误差。集合信息还可用于修改背景误差协方差(BEC),从而提高温度和盐度分布的精度。修改后的背景误差协方差得出了黑潮路径变化在 60 天后报期间的技能下降,集合敏感性分析表明,集合背景误差协方差引起的动力平衡变化导致了这种技能下降。
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来源期刊
Ocean Dynamics
Ocean Dynamics 地学-海洋学
CiteScore
5.40
自引率
0.00%
发文量
37
审稿时长
6-12 weeks
期刊介绍: Ocean Dynamics is an international journal that aims to publish high-quality peer-reviewed articles in the following areas of research: Theoretical oceanography (new theoretical concepts that further system understanding with a strong view to applicability for operational or monitoring purposes); Computational oceanography (all aspects of ocean modeling and data analysis); Observational oceanography (new techniques or systematic approaches in measuring oceanic variables, including all aspects of monitoring the state of the ocean); Articles with an interdisciplinary character that encompass research in the fields of biological, chemical and physical oceanography are especially encouraged.
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