Assimilating OSTIA SST into regional modeling systems for the Yellow Sea using ensemble methods

JI Xuanliang; KWON Kyung Man; CHOI Byoung-Ju; LIU Guimei; PARK Kwang-Soon; WANG Hui; BYUN Do-Seong; LI Yun; JI Qiyan; ZHU Xueming

doi:10.1007/s13131-017-0978-2

集合同化方法在黄海海域海表面温度同化中的应用

doi: 10.1007/s13131-017-0978-2

1.
国家海洋环境预报中心, 北京, 100081, 中国;国家海洋局海洋灾害预报技术研究重点实验室, 北京, 100081, 中国
2.
群山国立大学海洋室, 群山, 54150, 韩国
3.
群山国立大学海洋室, 群山, 54150, 韩国;全南国立大学海洋室, 广州, 61186, 韩国
4.
韩国海洋科学技术研究所, 安山, 15627, 韩国
5.
韩国水文和海洋机构, 釜山, 49111, 韩国
6.
海洋声学与遥感应用科学技术实验室, 浙江海洋大学, 舟山, 316000, 中国

基金项目: The National Key Research and Development Program of China under contract Nos 2016YFC1401800 and 2016YFC1401605; the Cooperation on the Development of Basic Technologies for the Yellow Sea and East China Sea Operational Oceanographic System (YOOS); the project of Development of Korea Operational Oceanographic System (KOOS), Phase 2 funded by the Ministry of Oceans and Fisheries; the National Natural Science Foundation of China under contract Nos 41076011, 41206023 and 41222038; the National Basic Research Program (973 Program) of China under contract No. 2011CB403606; the Public Science and Technology Research Funds Project of Ocean under contract No. 201205018; the Strategic Priority Research Program of the Chinese Academy of Sciences under contract No. XDA1102010403; Producing map of ocean currents for the neighboring seas of Korea funded by the Ministry of Oceans and Fisheries under contract No. 2033-307-210-13.

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出版历程
- 收稿日期: 2015-10-13
- 修回日期: 2016-03-03

Assimilating OSTIA SST into regional modeling systems for the Yellow Sea using ensemble methods

1.
National Marine Environmental Forecasting Center, State Oceanic Adminstration, Beijing 100081, China;Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, State Oceanic Adminstration, Beijing 100081, China
2.
Department of Oceanography, Kunsan National University, Gunsan 54150, Republic of Korea
3.
Department of Oceanography, Kunsan National University, Gunsan 54150, Republic of Korea;Department of Oceanography, Chonnam National University, Gwangju 61186, Republic of Korea
4.
Korea Institute of Ocean Science and Technology, Ansan 15627, Republic of Korea
5.
Korea Hydrographic and Oceanographic Agency, Busan 49111, Republic of Korea
6.
Marine Acoustics and Remote Sensing Laboratory, Zhejiang Ocean University, Zhoushan 316000, China

摘要

摘要: 基于区域海洋模型系统（ROMS），国家海洋环境预报中心（NMEFC）和群山国立大学（KNU）建立了两套黄海三维温盐流预报系统。为了探讨同化海表面温度数据对预报系统的影响，利用美国国家海洋预报中心（NCOF）提供的2011年9月1日-2012年2月29日业务化海表面温度和海冰再分析数据（OSTIA）中海表面温度数据，NMEFC和KNU分别采用集合最优插值和集合卡曼滤波同化方法对黄海预报系统进行了同化。本文设计了两组实验，不加同化实验的结果表明，NMEFC预报系统刻画次表层海水温度优于KNU，而后者模拟的海表面温度更接近观测数据。造成这种差异的可能原因是模式使用的表面热通量、水平网格分辨率以及大气强迫场数据不同。加入同化的实验结果显示，与最优插值海表面温度（OISST）数据相比，NMEFC预报系统均方根误差（RMSE）从1.78℃减小至1.30℃，KNU预报系统从1.46℃减小至1.30℃。与浮标数据比较的结果显示，同化后的两套预报系统模拟的海表面温度的均方根误差分别减小了41%和31%。两种同化方法对两套预报系统海水垂向温度预报精度也有一定改进，10月上20m层海水的RMSE值小于1.5℃，底层约为3.1℃，而2月份整个海水水体的RMSE值小于1.0℃。该研究结果表明为了提高海水垂向温度的预报精度，将海水剖面观测数据同化至预报系统，以及建立高分辨率预报模型和优化同化方法是亟待解决的。
- 集合最优插值 /
- 集合卡曼滤波 /
- 海表面温度 /
- 黄海 /
- 同化
Abstract: The effects of sea surface temperature (SST) data assimilation in two regional ocean modeling systems were examined for the Yellow Sea (YS). The SST data from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) were assimilated. The National Marine Environmental Forecasting Center (NMEFC) modeling system uses the ensemble optimal interpolation method for ocean data assimilation and the Kunsan National University (KNU) modeling system uses the ensemble Kalman filter. Without data assimilation, the NMEFC modeling system was better in simulating the subsurface temperature while the KNU modeling system was better in simulating SST. The disparity between both modeling systems might be related to differences in calculating the surface heat flux, horizontal grid spacing, and atmospheric forcing data. The data assimilation reduced the root mean square error (RMSE) of the SST from 1.78℃ (1.46℃) to 1.30℃ (1.21℃) for the NMEFC (KNU) modeling system when the simulated temperature was compared to Optimum Interpolation Sea Surface Temperature (OISST) SST dataset. A comparison with the buoy SST data indicated a 41% (31%) decrease in the SST error for the NMEFC (KNU) modeling system by the data assimilation. In both data assimilative systems, the RMSE of the temperature was less than 1.5℃ in the upper 20 m and approximately 3.1℃ in the lower layer in October. In contrast, it was less than 1.0℃ throughout the water column in February. This study suggests that assimilations of the observed temperature profiles are necessary in order to correct the lower layer temperature during the stratified season and an ocean modeling system with small grid spacing and optimal data assimilation method is preferable to ensure accurate predictions of the coastal ocean in the YS.
- ensemble optimal interpolation /
- ensemble Kalman filter /
- SST /
- Yellow Sea /
- assimilation

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集合同化方法在黄海海域海表面温度同化中的应用

doi: 10.1007/s13131-017-0978-2

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Assimilating OSTIA SST into regional modeling systems for the Yellow Sea using ensemble methods

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