Abstract

Targeted 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.

Highlights

  • In August 2019, an exploratory field experiment is conducted on the northwest continental slope of the Yellow Sea (YS; Fig. 1), aiming at improving the thermal structure predictions by oceanic targeted observations based on identified sensitive areas

  • The simulated monthly averaged (August) temperature along the 35°N section is extracted and compared with previous observations obtained from the Atlas of Ocean Data in the China S­ eas[38]

  • The regionally averaged temperature profile root-mean-square errors (RMSEs) in the target region at the verification time between the nature run EXP0 and other experiments are used to evaluate the effectiveness of the Conditional Nonlinear Optimal Perturbation (CNOP)-based sensitive area

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Summary

Introduction

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. In August 2019, an exploratory field experiment is conducted on the northwest continental slope of the Yellow Sea (YS; Fig. 1), aiming at improving the thermal structure predictions by oceanic targeted observations based on identified sensitive areas.

Results
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