姚恒恺 | 人工智能海洋技术场景化应用山东省工程研究中心

LocRes–PINN: A Physics–Informed Neural Network with Local Awareness and Residual Learning

Mon, 02 Feb 2026 00:00:00 +0000

Physics informed neural network framework for time-varying wind stress drag coefficient identification in the Ekman model

Wed, 13 Aug 2025 00:00:00 +0000

A Dual-attention Embedded CNN Model for Estimating Mixed Layer Depths in the Bay of Bengal

Wed, 19 Mar 2025 00:00:00 +0000

Variations in ocean mixed layer depth (MLD) show a significant impact on energy balance in the global climate systems and marine ecosystems. So far, the accuracy of modeling MLD, especially in the region with complex ocean dynamics, remains a challenge. This thus calls for an emergency using Artificial intelligence (AI) approach to improve the assessment of the MLD. In this study, we introduce a novel convolutional neural network model based on a dual-attention module (DA-CNN) to estimate the MLD in the Bay of Bengal (BoB) by integrating multi-source remote sensing data and Argo gridded data. Compared with the original CNN model, the DA-CNN model exhibits superior performance with notable improvements in the annual average RMSE and R2 values by 13.0% and 8.4%, respectively, while more accurately capturing the seasonal variations in MLD. Moreover, the results using the DA-CNN model show minimum RMSE and maximum R2 values, in comparison to the calculation by the random forest (RF), neural network (ANN) model, and the Hybrid Coordinate Ocean Model (HYCOM). Accordingly, our findings suggest that the newly developed DA-CNN model provides an effective advantage in studying the MLD and the associated ocean processes.

Estimating Subsurface Thermohaline Structure in the Tropical Western Pacific Using DO-ResNet Model

Thu, 29 Aug 2024 00:00:00 +0000

Estimating Subsurface Thermohaline Structure in the tropical Western Pacific using DO-ResNet model

Tue, 16 Jul 2024 00:00:00 +0000

Research on vertical structure and origin of eddies in Kuroshio-Oyashio Extension region

Mon, 18 Dec 2023 00:00:00 +0000

On the Vertical Structure of Mesoscale Eddies in the Kuroshio-Oyashio Extension

Sat, 16 Dec 2023 00:00:00 +0000

Introducing the New Regional Community Earth System Model, R-CESM

Thu, 10 Aug 2023 00:00:00 +0000

Asymmetric vertical structure of mesoscale eddies in the Kuroshio Extension region

Tue, 01 Dec 2020 00:00:00 +0000

Vertical structure of mesoscale eddy energy in the Kuroshio Extension region

Sun, 11 Feb 2018 21:27:37 +0800

Interannual Variations of Water Mass in Nordic seas

Thu, 18 Jun 2015 00:00:00 +0000

A study on topography Rossby wave simulation

Fri, 22 Aug 2014 00:00:00 +0000

全球0.5度海洋盐度格点数据产品

Fri, 22 Aug 2014 00:00:00 +0000

信息卡片

中文名称: 日本气象厅西北太平洋硝酸盐现场观测数据
英文名称: In-situ nitrate observations from the Japan Meteorological Agency (JMA)
DOI: []
CSTR:
数据集编码:
数据共享方式: 完全共享
授权许可方式:
学科分类: 物理海洋
数据量: 42.5MB

基本信息

时间范围: 2015年01月至2021年03月
水平分辨率: 站位观测（非格点）
空间区域: 西北太平洋
垂直分辨率: 0–1000m深度剖面
经度范围: 120°E – 180°E
时间分辨率: 航次观测
纬度范围: 9°S–34°N
存储格式: excel
要素信息: 硝酸盐浓度
关键词: 硝酸盐; 海洋营养盐; 西北太平洋; 现场观测
资助项目编码:
资助项目:

数据联系信息

数据生产者: Japan Meteorological Agency (JMA)
单位: Japan Meteorological Agency
联系方式: https://www.data.jma.go.jp
中心联系人:
联系方式:

数据使用声明

声明内容: 数据来自日本气象厅长期海洋观测航次，用于训练和验证深度神经网络模型。
引用方式: Wang L., Xu Z., Gong X., Zhang P., Hao Z., You J., Zhao X., Guo X., 2023: Estimation of nitrate concentration and its distribution in the northwestern Pacific Ocean by a deep neural network model, Deep-Sea Research Part I, 195, 104005.
致谢方式: 感谢 Japan Meteorological Agency (JMA) 提供西北太平洋海洋观测数据支持。