| 题名 | Assimilating In Situ and Remote Sensing Observations in a Highly Variable Estuary–Shelf Model |
| 作者 | |
| 发表日期 | 2021-03
|
| DOI | |
| 发表期刊 | |
| ISSN | 0739-0572
|
| 卷号 | 38期号:3页码:459-479 |
| 摘要 | To improve the forecasting performance in dynamically active coastal waters forced by winds, tides, and
river discharges in a coupled estuary–shelf model off Hong Kong, a multivariable data assimilation (DA) system using the ensemble optimal interpolation method has been developed and implemented. The system assimilates the conductivity– temperature–depth (CTD) profifilers, time series buoy measurement, and remote sensing sea surface temperature (SST) data into a high-resolution estuary–shelf ocean model around Hong Kong. We found that the time window selection associated with the local dynamics and the number of observation samples are two key factors in improving assimilation in the unique estuary–shelf system. DA with a varied assimilation time window that is based on the intratidal variation in the local dynamics can reduce the errors in the estimation of the innovation vector caused by the model–observation mismatch at the analysis time and improve simulation greatly in both the estuary and coastal regions. Statistically, the overall root-meansquare error (RMSE) between the DA forecasts and not-yet-assimilated observations for temperature and salinity has been reduced by 33.0% and 31.9% in the experiment period, respectively. By assimilating higher-resolution remote sensing SST data instead of lower-resolution satellite SST, the RMSE of SST is improved by ;18%. Besides, by assimilating real-time buoy mooring data, the model bias can be continuously corrected both around the buoy location and beyond. The assimilation of the combined buoy, CTD, and SST data can provide an overall improvement of the simulated three-dimensional solution. A dynamics-oriented assimilation scheme is essential for the improvement of model forecasting in the estuary– shelf system under multiple forcings. |
| 关键词 | |
| 相关链接 | [来源记录] |
| 收录类别 | |
| 语种 | 英语
|
| 学校署名 | 其他
|
| WOS记录号 | WOS:000646372600004
|
| 出版者 | |
| EI入藏号 | 20211510205268
|
| EI主题词 | Buoys
; Discharge (fluid mechanics)
; Dynamics
; Estuaries
; Forecasting
; Mean square error
; Oceanography
|
| EI分类号 | Waterways:407.2
; Oceanography, General:471.1
; Mathematical Statistics:922.2
|
| ESI学科分类 | GEOSCIENCES
|
| 来源库 | 人工提交
|
| 引用统计 |
被引频次[WOS]：6
|
| 成果类型 | 期刊论文 |
| 条目标识符 | http://sustech.caswiz.com/handle/2SGJ60CL/222167 |
| 专题 | 工学院_海洋科学与工程系 |
| 作者单位 | 1.Hong Kong University of Science and Technology, Hong Kong 2.Department of Ocean Science, Hong Kong University of Science and Technology, Hong Kong 3.Swedish Meteorological and Hydrological Institute, Norrk€oping, Sweden 4.Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, China 5.Nansen Environmental and Remote Sensing Center, Bergen, Norway 6.Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China |
| 推荐引用方式 GB/T 7714 |
Wenfeng,Lai,Jianping,Gan,Ye,Liu,等. Assimilating In Situ and Remote Sensing Observations in a Highly Variable Estuary–Shelf Model[J]. JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY,2021,38(3):459-479.
|
| APA |
Wenfeng,Lai,Jianping,Gan,Ye,Liu,Zhiqiang,Liu,Jiping,Xie,&Jiang,Zhu.(2021).Assimilating In Situ and Remote Sensing Observations in a Highly Variable Estuary–Shelf Model.JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY,38(3),459-479.
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| MLA |
Wenfeng,Lai,et al."Assimilating In Situ and Remote Sensing Observations in a Highly Variable Estuary–Shelf Model".JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY 38.3(2021):459-479.
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| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | 操作 | |
| 2021Lai WF-作者-Journa（3958KB） | 期刊论文 | 作者接受稿 | 限制开放 | CC BY-NC-SA | ||
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