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

Author(s):  
Jingyi Liu ◽  
Wuhong Guo ◽  
Baolong Cui ◽  
Kun Liu ◽  
Huiqin Hu

<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>

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kun Liu ◽  
Wuhong Guo ◽  
Lianglong Da ◽  
Jingyi Liu ◽  
Huiqin Hu ◽  
...  

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.


2020 ◽  
Author(s):  
Kun Liu ◽  
Jingyi Liu ◽  
Huiqin Hu ◽  
Wuhong Guo ◽  
Baolong Cui

<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>


2020 ◽  
Author(s):  
Lin Jiang ◽  
Wansuo Duan

<p>Previous studies show that the kinetic energy of mesoscale eddies (MEs) accounts for more than 80% of the global ocean energy. The theoretical study and numerical simulation of MEs will enable us to better understand the dynamics of ocean circulation. Weiss and Grooms (2017) found that assimilating uniform observations taken over MEs is much better than assimilating a subset of observations on a regular grid for improving prediction skill of SSH associated with ocean state. In the present study, we use a conditional nonlinear optimal perturbation (CNOP) approach to investigate the sensitivity of the ocean state sea surface height (SSH) predictions on MEs with a two-layer quasi-geostrophic model and show the optimal assimilating scheme. In the study, the CNOPs of SSH predictions are first computed. It is found that, if one regards the regions covered by the grid points with large values of CNOPs as sensitive area of SSH predictions, the sensitive areas are mainly located on MEs. Furthermore, the stronger the MEs, the more the MEs grid points covered by the sensitive area. Especially, these grid points associated with sensitive areas are not uniformly distributed over the MEs. It is obvious that the predictions of SSH are quite sensitive to the initialization of MEs (especially that of the particular region of large values of CNOPs for strong MEs, rather than of the uniformly distributed grid points over MEs). Therefore, an appropriate initialization of MEs is much helpful for improving the prediction accuracy of SSH. And the CNOPs of SSH prediction here may provide useful information on how to improve initialization of MEs.</p>


2022 ◽  
Author(s):  
Bin Mu ◽  
Yuehan Cui ◽  
Shijin Yuan ◽  
Bo Qin

Abstract. The global impact of an El Niño-Southern Oscillation (ENSO) event can differ greatly depending on whether it is an Eastern-Pacific-type (EP-type) event or a Central-Pacific-type (CP-type) event. Reliable predictions of the two types of ENSO are therefore of critical importance. Here we construct a deep neural network with multichannel structure for ENSO (named ENSO-MC) to simulate the spatial evolution of sea surface temperature (SST) anomalies for the two types of events. We select SST, heat content, and wind stress (i.e., three key ingredients of Bjerknes feedback) to represent coupled ocean-atmosphere dynamics that underpins ENSO, achieving skillful forecasts for the spatial patterns of SST anomalies out to one year ahead. Furthermore, it is of great significance to analyze the precursors of EP-type or CP-type events and identify targeted observation sensitive area for the understanding and prediction of ENSO. Precursors analysis is to determine what type of initial perturbations will develop into EP-type or CP-type events. Sensitive area identification is to determine the regions where initial states tend to have greatest impacts on evolution of ENSO. We use saliency map method to investigate the subsurface precursors and identify the sensitive areas of ENSO. The results show that there are pronounced signals in the equatorial subsurface before EP events, while the precursory signals of CP events are located in the North Pacific. It indicates that the subtropical precursors seem to favor the generation of the CP-type El Niño and the EP-type El Niño is more related to the tropical thermocline dynamics. And the saliency maps show that the sensitive areas of the surface and the subsurface are located in the equatorial central Pacific and the equatorial western Pacific, respectively. The sensitivity experiments imply that additional observations in the identified sensitive areas can improve forecasting skills. Our results of precursors and sensitive areas are consistent with the previous theories of ENSO, demonstrating the potential usage and advantages of the ENSO-MC model in improving the simulation, understanding and observations of two ENSO types.


2013 ◽  
Vol 18 (6) ◽  
pp. 1335-1342
Author(s):  
Zhenbo LU ◽  
Bingqing XU ◽  
Fan LI ◽  
Mingyi SONG ◽  
Huanjun ZHANG ◽  
...  

2011 ◽  
Vol 31 (5) ◽  
pp. 73-78
Author(s):  
Huijun LI ◽  
Xunhua ZHANG ◽  
Shuyin NIU ◽  
Kaining YU ◽  
Aiqun SUN ◽  
...  

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