Targeted observations based on sensitive areas identified by CNOP to improve the thermal structure predictions in the summer Yellow Sea: operation in the field

2020 ◽  
Author(s):  
Kun Liu ◽  
Wuhong Guo ◽  
Lianglong Da ◽  
Jingyi Liu ◽  
Huiqin Hu ◽  
...  
Keyword(s):  
Yellow Sea ◽  
Thermal Structure ◽  
Sensitive Areas ◽  
Targeted Observations

scholarly journals Improving the thermal structure predictions in the Yellow Sea by conducting targeted observations in the CNOP-identified sensitive areas

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kun Liu ◽  
Wuhong Guo ◽  
Lianglong Da ◽  
Jingyi Liu ◽  
Huiqin Hu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Yellow Sea ◽  
Thermal Structure ◽  
The Yellow Sea ◽  
Perturbation Approach ◽  
Optimal Perturbation ◽  
Sensitive Areas ◽  
Targeted Observations ◽  
Targeted Observation ◽  
The Impact

AbstractTargeted observation is an appealing procedure for improving model predictions. However, studies on oceanic targeted observations have been largely based on modeling efforts, and there is a need for field validating operations. Here, we report the results of a field targeted observation that is designed based on the sensitive areas identified by the Conditional Nonlinear Optimal Perturbation approach to improve the 7th day thermal structure prediction in the Yellow Sea. By introducing the technique of cycle data assimilation and the new concept of time-varying sensitive areas, an observing strategy is designed and validated by a set of Observing System Simulation Experiments. Then, the impact of targeted observations was investigated by a choreographed field campaign in the summer of 2019. The results of the in-field Observing System Experiments show that, compared to conventional local data assimilation, conducting targeted observations in the sensitive areas can yield more benefit at the verification time. Furthermore, dynamic analysis demonstrates that the refinement of vertical thermal structures is mainly caused by the changes in the upstream horizontal temperature advection driven by the Yellow Sea Cold Water Mass circulation. This study highlights the effectiveness of targeted observations on reducing the forecast uncertainty in the ocean.

Targeted observations based on identified sensitive areas by CNOP to improve the thermal structures prediction in the summer Yellow Sea: preparatory work for the campaign in the field

2020 ◽  
Author(s):  
Jingyi Liu ◽  
Wuhong Guo ◽  
Baolong Cui ◽  
Kun Liu ◽  
Huiqin Hu
Keyword(s):  
Yellow Sea ◽  
The Yellow Sea ◽  
Sensitive Area ◽  
Optimal Perturbation ◽  
Field Campaign ◽  
Sensitive Areas ◽  
Initial Errors ◽  
Thermal Structures ◽  
Targeted Observation ◽  
Preparatory Work

<p>Targeted observation is an appealing procedure to improve oceanic model predictions by taking additional assimilation of collected measurements. However, studies on targeted observation in the oceanic field have been largely based on modeling efforts, and there is a need for field validating observations. Here, we report the preparatory work of a field campaign, which is designed based on the identified sensitive area by the Conditional Nonlinear Optimal Perturbation (CNOP) approach, to improve the short-range summer thermal structures prediction in the Yellow Sea (YS). We firstly simulated the hindcasting (2016-2018) temperature structures in the summertime, and found that the locations of the sensitive areas are generally consistent in space for each hindcast year. Then, we introduced the technique of multiple-assimilation and the definition of time-varying sensitive area, and designed observing strategies for the YS summer campaign. Observing System Simulation Experiments (OSSEs) were conducted prior to address the plan on field campaign in the Yellow Sea in August 2019. Results show that, reducing the initial errors in the sensitive area can lead to more improvement on thermal structures prediction than that in other area.</p>

Statistic characteristics of thermal structure in the southern Yellow Sea in summer

2004 ◽  
Vol 22 (3) ◽  
pp. 237-243 ◽  
Author(s):  
Bai Hong ◽  
Hu Dunxin ◽  
Chen Yongli ◽  
Wang Qingye
Keyword(s):  
Yellow Sea ◽  
Thermal Structure ◽  
Southern Yellow Sea ◽  
The Southern Yellow Sea

Identifying the sensitive area in targeted observation for improving the vertical thermal structure prediction in the summer Yellow Sea

2020 ◽  
Author(s):  
Kun Liu ◽  
Jingyi Liu ◽  
Huiqin Hu ◽  
Wuhong Guo ◽  
Baolong Cui
Keyword(s):  
Yellow Sea ◽  
Structure Prediction ◽  
Thermal Structure ◽  
Integration Scheme ◽  
Sensitive Area ◽  
Short Term ◽  
Initial Errors ◽  
Term Prediction ◽  
Targeted Observation ◽  
Short Term Prediction

<p>The sensitive area of targeted observation for the short-term prediction of the vertical thermal structure in the summer Yellow Sea is investigated by utilizing the Conditional Nonlinear Optimal Perturbation (CNOP) method and a adjoint-free algorithm with the Regional Ocean Modeling System. We use a vertical integration scheme of temperature to locate the sensitive area, in which reducing the initial errors are expected to yield great improvements in vertical thermal structure prediction of the verification area. We perform a series of sensitivity experiments to evaluate the effectiveness of the identified sensitive area. Our results show that, initially adding random perturbations in the sensitive area have the greatest negative effects on the prediction than in other areas (eg. the verification area, regions east and northeast of the verification area). Moreover, Observing System Simulation Experiments (OSSEs) indicate that, eliminating the initial errors in the sensitive area can lead to a more refined prediction than in other selected areas (including the verification area itself). Our study suggests that implementing targeted observation is a feasible way to improve the short-term prediction of the vertical thermal structure in the summer Yellow Sea.</p>

Seasonal evolution of circulation and thermal structure in the Yellow Sea

2005 ◽  
Vol 23 (3) ◽  
pp. 269-274 ◽  
Author(s):  
Pang Chongguang ◽  
Bai Xuezhi ◽  
Bai Hong ◽  
Wang Fan
Keyword(s):  
Yellow Sea ◽  
Thermal Structure ◽  
The Yellow Sea ◽  
Seasonal Evolution
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