QUST-AIOCEAN 省工程研究中心网站

# --------------------------------------研究方向--------------------------------------------
  - block: research-areas                          #【必选】固定值 research-areas（模块类型）
    id: research-areas
    content:                                        #【可选】内容配置
      title: "研究方向"                               #【可选】任意字符串；支持 Markdown/emoji
      subtitle: "Research Focus"                    #【可选】任意字符串；支持 Markdown/emoji
      # text: "简要说明"                                 #【可选】任意字符串；支持 Markdown/emoji

      items:                                        #【必选】条目数组；为空则无内容
        - name: "方向1名称"                            #【必选】任意字符串
          description: "方向描述"                     #【必选】任意字符串
          # image: "areas/ai.jpg"                     #【可选】图片文件名；位于 assets/media/
          # icon: "hero/brain"                        #【可选】任意图标名；由 get_icon 支持
          gradient: "from-primary-400 to-secondary-400" #【可选】任意 Tailwind 类；卡片/形状背景渐变
          status: "active"                          #【可选】状态：active/emerging/planning（影响徽章颜色）
          url: "/research/ai/"                      #【可选】标题链接
          topics: ["AI","NLP","Vision"]             #【可选】字符串数组；最多展示前 3 个
          team_size: "10"              #【可选】任意字符串；显示为 team 统计
          publications: "25 papers"                #【可选】任意字符串；显示为 papers 统计
          funding: "100 万"                            #【可选】任意字符串；显示为 funding 统计
          cta:                                      #【可选】卡片内按钮
            text: "了解更多"                          #【必选】任意字符串
            url: "/research/ai/"                    #【必选】任意链接（站内/站外）
        - name: "方向2名称"                            #【必选】任意字符串
          description: "方向描述"                     #【必选】任意字符串
          # image: "areas/ai.jpg"                     #【可选】图片文件名；位于 assets/media/
          # icon: "hero/brain"                        #【可选】任意图标名；由 get_icon 支持
          gradient: "from-primary-400 to-secondary-400" #【可选】任意 Tailwind 类；卡片/形状背景渐变
          status: "active"                          #【可选】状态：active/emerging/planning（影响徽章颜色）
          url: "/research/ai/"                      #【可选】标题链接
          topics: ["AI","NLP","Vision"]             #【可选】字符串数组；最多展示前 3 个
          team_size: "10"              #【可选】任意字符串；显示为 team 统计
          publications: "25 papers"                #【可选】任意字符串；显示为 papers 统计
          funding: "100 万"                            #【可选】任意字符串；显示为 funding 统计
          cta:                                      #【可选】卡片内按钮
            text: "了解更多"                          #【必选】任意字符串
            url: "/research/ai/"                    #【必选】任意链接（站内/站外）
        - name: "方向3名称"                            #【必选】任意字符串
          description: "方向描述"                     #【必选】任意字符串
          # image: "areas/ai.jpg"                     #【可选】图片文件名；位于 assets/media/
          # icon: "hero/brain"                        #【可选】任意图标名；由 get_icon 支持
          gradient: "from-primary-400 to-secondary-400" #【可选】任意 Tailwind 类；卡片/形状背景渐变
          status: "active"                          #【可选】状态：active/emerging/planning（影响徽章颜色）
          url: "/research/ai/"                      #【可选】标题链接
          topics: ["AI","NLP","Vision"]             #【可选】字符串数组；最多展示前 3 个
          team_size: "10"              #【可选】任意字符串；显示为 team 统计
          publications: "25 papers"                #【可选】任意字符串；显示为 papers 统计
          funding: "100 万"                            #【可选】任意字符串；显示为 funding 统计
          cta:                                      #【可选】卡片内按钮
            text: "了解更多"                          #【必选】任意字符串
            url: "/research/ai/"                    #【必选】任意链接（站内/站外）

      cta:                                          #【可选】模块底部全局按钮
        text: "查看全部方向"                           #【必选】任意字符串
        url: "/research/"                           #【必选】任意链接
        icon: "hero/arrow-right"                    #【可选】任意图标名

    design:                                         #【可选】样式配置
      layout: "cards"                               #【可选】cards/hexagon/timeline；默认 cards

schema: hugoblox/author/v1
slug: hengkai-yao
name:
  display: 姚恒恺
  given: Hengkai
  family: Yao
status: #非必须，可注释
  icon: 🚀 # 可使用emoji表情
role: 讲师
bio: "Dr. Hengkai Yao (姚恒恺) is a lecturer of School of Mathmetica and Physics at the [Qingdao University of Science and Technology](https://www.qust.edu.cn). He got Ph.D of *Physical Oceanograpy* from [Ocean University of China](https://www.ouc.edu.cn). His research interests include mesoscale eddies, ocean modeling and AI oceanography. He is member of the [AI Oceanography group](https://oceanai.ac.cn), which develops big data in ocean, ocean simulation, and ocean prediction. He is also a chief scientist in [Qingdao Oakfull Water Technology Co., Ltd](https://oakfullwater.com/)."
affiliations: #可输入多个，多余可注释或删除
  - name: 青岛科技大学
    url: https://www.qust.edu.cn/
  - name: 青岛澳可富净水科技
    url: https://oakfullwater.com/
links: #可输入多个，多余可注释或删除
  - icon: at-symbol
    url: mailto:hengkai.yao@gmail.com
    label: E-mail Me
  - icon: brands/github
    url: https://github.com/yaohengkai
  - icon: brands/linkedin
    url: https://www.linkedin.com/in/yaohengkai/
  - icon: academicons/google-scholar
    url: https://scholar.google.com/citations?user=ujcYod0AAAAJ
  - icon: academicons/orcid
    url: https://orcid.org/0009-0002-6277-0775
  - icon: brands/instagram
    url: https://www.instagram.com/yaohengkai/
interests: #可输入多个，多余可注释或删除
  - 中尺度涡
  - 人工智能海洋学
  - 新材料
user_groups: #可输入多个，多余可注释或删除
  # - 学术带头人
  - 成员
  # - 学生

---
title: "Estimating Subsurface Thermohaline Structure in the tropical Western Pacific using DO-ResNet model"
authors: # 作者名应按命名为英文拼音（即使是中文刊物）
  - Xianmei Zhou
  - Shanliang Zhu
  - Wentao Jia
  - Hengkai Yao

author_notes: # 可以标注对应位置的作者为‘通讯作者‘或‘共同第一作者‘，中英文都可
  - ""
  - ""
  - ""
  - "Corresponding"

date: "2024-07-16T00:00:00Z" # 这里是本文出版日期

# Publication type.
# Options: article-journal, paper-conference, thesis, book, chapter, report, patent, manuscript
publication_types: ["article-journal"] 

# Publication name and optional abbreviated publication name.
publication: "Atmosphere"
publication_short: "ATM"

abstract: 'Estimating the ocean’s subsurface thermohaline information from satellite measurements is essential for understanding ocean dynamics and El Niño phenomenon. This paper proposes an improved double-output Residual Neural Network (DO-ResNet) model to concurrently estimate the subsurface temperature (ST) and subsurface salinity (SS) in the tropical Western Pacific using multi-source remote sensing data, including sea surface temperature (SST), sea surface salinity (SSS), sea surface height anomaly (SSHA), sea surface wind (SSW), and geographical information (including longitude and latitude). In the model experiment, Argo data were used to train and validate the model, and the root mean square error (RMSE), normalized root mean square error (NRMSE) and coefficient of determination (R²) were employed to evaluate the model''s performance. The results showed that the sea surface parameters selected in this study have a positive effect on the estimation process, and the average RMSE and R² values for estimating ST (SS) by the proposed model are 0.34 "°C " (0.05 psu) and 0.91 (0.95), respectively. Under the data conditions considered in this study, DO-ResNet demonstrates superior performance relative to the extreme gradient boosting model, random forest model, and artificial neural network model. Additionally, this study evaluates the model’s accuracy by comparing its estimations of ST and SS across different depths with Argo data, demonstrating the model''s ability to effectively capture the most spatial features, and by comparing NRMSE across different depths and seasons, the model demonstrates strong adaptability to seasonal variations. In conclusion, this research introduces a novel artificial intelligence technique for estimating ST and SS in the tropical Western Pacific Ocean.'

tags: # 可多个关键词，除了文章自身的关键词外要加上一个中文类别：海洋大数据/海洋数值模式/海洋要素智能预报/场景智能服务/其他
  - AI Oceanography
  - Physical Oceanography
  - 海洋大数据

hugoblox:
  ids:
    doi: "10.3390/atmos15091043" # 必填

links: #可填额外的链接
  # - type: pdf
  #   url: ""
  # - type: code
  #   url: ""
  # - type: dataset
  #   url: ""
  # - type: poster
  #   url: ""
  # - type: project
  #   url: ""
  # - type: slides
  #   url: ""
  # - type: source
  #   url: ""
  # - type: video
  #   url: ""
  # - name: News
  #   url: ""

# Featured image
# To use, add an image named `featured.jpg/png` to your page's folder.
image:
  caption: "Image credit: Xianmei Zhou"
  focal_point: ""
  preview_only: false

# Associated Projects (optional).
#   Associate this publication with one or more of your projects.
#   Simply enter your project's folder or file name without extension.
#   E.g. `internal-project` references `content/project/internal-project/index.md`.
#   Otherwise, set `projects: []`.
projects: #可以归于某个项目
  - 2024_AI_Ocean

# Slides (optional).
#   Associate this publication with Markdown slides.
#   Simply enter your slide deck's filename without extension.
#   E.g. `slides: "example"` references `content/slides/example/index.md`.
#   Otherwise, set `slides: ""`.
slides: ""

---
下面可使用 markdown 格式增加对于文章的详解

@article{2025_Wang_Assessment,
 abstract = {Marine heatwaves (MHWs) are extreme coherent ocean temperature events that can have devastating impacts on marine ecosystems and socio-economies. This study assesses the prediction performance of the First Institute of Oceanography-Climate Prediction System version 2.0 (FIO-CPS v2.0) for MHWs. The results indicate that FIO-CPS v2.0 demonstrates skillful prediction capabilities for global MHWs, particularly in the tropics. The system effectively captures the spatial distribution and temporal evolution of sea surface temperature anomalies associated with MHW events. Evaluation metrics including intensity, duration, and frequency show that the model provides valuable lead times for early warning systems, although challenges remain in high-latitude regions and western boundary currents.},
 author = {Wang, Yuanlin and Song, Yajuan and Bao, Ying and Jang, Chan Joo and Song, Zhenya},
 date = {2025-06},
 doi = {10.1016/j.wace.2025.100757},
 issn = {2212-0947},
 journaltitle = {Weather and Climate Extremes},
 keywords = {Climate model,FIO-CPS v2.0,Marine heatwaves,Prediction,SST},
 langid = {english},
 pages = {100757},
 shortjournal = {Weather Clim. Extremes},
 title = {Assessment of the Marine Heatwaves Prediction Performance of the Short-Term Climate Prediction System FIO-CPS v2.0},
 url = {https://linkinghub.elsevier.com/retrieve/pii/S2212094725000155},
 volume = {48}
}