An Efficient T-S Assimilation Strategy Based on the Developed Argo-Extending Algorithm
An Efficient T-S Assimilation Strategy Based on the Developed Argo-Extending Algorithm
复制标题
基于Argo扩展算法的高效T-S同化策略
DOI：
10.1155/2017/6847343
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发表时间：
2017-10
影响因子：
2.9
通讯作者：
Ma Qiang
中科院分区：
文献类型：
--
作者：
Zhou Chaojie;Ding Xiaohua;Zhang Jie;Yang Jungang;Ma Qiang
数据同化是一种有效的海洋状态估计技术，它引入了现场测量的优点。考虑到观测的不足，较少温度和盐度(T-S)廓线的同化效果并不理想。为了改善这种情况，提出了一种基于ARGO温度分布的扩展算法，并应用该算法来表示更多的重建信息。同时，当重建的信息被同化到海洋模式中时，结果的精度将得到显著的提高。为了验证该模型的有效性，基于区域海洋模式系统(ROMS)和四维变分方法(4DVAR)进行了一个包括4个算例的试验。与EN4资料集的比较表明，经过ARGO同化的个例和重建的温度廓线都得到了提高；重建的温度廓线的加入确实提高了精度；扩展算法过程中引入的海温的影响可以忽略不计；重建的温度廓线的净增强与ARGO T-S的观测结果相当。最后，验证了该算法对数据同化的积极影响。
Data assimilation is an efficient technique in the estimation of ocean state, by introducing the benefit of in situ measurements. Considering the insufficiency of the observations, the performance of assimilation with few temperature and salinity (T-S) profiles is not satisfied. To modify the situation, an extending algorithm based on the Argo temperature profile is developed and applied to present more reconstructed information. Meanwhile, when the reconstructed information is assimilated into the ocean model, the accuracy of the outcomes would obtain a notable enhancement. To validate it, an experiment including four cases is conducted based on Regional Ocean Modeling System (ROMS) and 4-dimensional variational method (4DVAR). The comparison with the EN4 dataset shows that the cases assimilated the Argo and the reconstructed temperature profiles are both promoted; the addition of reconstructed temperature profiles does enhance the accuracy; the impact of SST introduced in the extending algorithm process is negligible; the net enhancement of reconstructed temperature profiles is comparable with Argo T-S observations. Finally, the positive impact of the developed algorithm on data assimilation is validated.
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影响因子：
--
作者：
Boyin Huang;Y. Xue;D. Behringer
通讯作者：
Boyin Huang;Y. Xue;D. Behringer
影响因子：
4.1
作者：
A. Moore;H. Arango;G. Broquet;Chris Edwards;M. Veneziani;B. Powell;Dave Foley;J. Doyle;Dan Costa;P. Robinson
通讯作者：
A. Moore;H. Arango;G. Broquet;Chris Edwards;M. Veneziani;B. Powell;Dave Foley;J. Doyle;Dan Costa;P. Robinson
影响因子：
2.2
作者：
Hai-Tien Lee;R. Ellingson
通讯作者：
Hai-Tien Lee;R. Ellingson
影响因子：
4.9
作者：
Reynolds, Richard W.;Smith, Thomas M.;Schlax, Michael G.
通讯作者：
Schlax, Michael G.
影响因子：
3.2
作者：
Shchepetkin, AF;McWilliams, JC
通讯作者：
McWilliams, JC