Identification of the sensitive area for targeted observation to improve vertical thermal structure prediction in summer in the Yellow Sea

The sensitive area of targeted observations for short-term (7 d) prediction of vertical thermal structure (VTS) in summer in the Yellow Sea was investigated. We applied the Conditional Nonlinear Optimal Perturbation (CNOP) method and an adjoint-free algorithm with the Regional Ocean Modeling System (ROMS). We used vertical integration of CNOP-type temperature errors to locate the sensitive areas, where reduction of initial errors is expected to yield the greatest improvement in VTS prediction for the selected verification area. The identified sensitive areas were northeast-southwest orientated northeast to the verification area, which were possibly related to the southwestward background currents. Then, we performed a series of sensitivity experiments to evaluate the effectiveness of the identified sensitive areas. Results show that initial errors in the identified sensitive areas had the greatest negative effect on VTS prediction in the verification area compared to errors in other areas (e.g., the verification area and areas to its east and northeast). Moreover, removal of initial errors through deploying simulated observations in the identified sensitive areas led to more refined prediction than correction of initial conditions in the verification area itself. Our results suggest that implementation of targeted observation in the CNOP-based sensitive areas is an effective method to improve short-term prediction of VTS in summer in the Yellow Sea.

Automated summary

The study focused on the sensitive areas for short-term prediction of vertical thermal structure in the Yellow Sea in summer, using the CNOP method and ROMS algorithm. Identified sensitive areas were related to background currents and deploying observations in these areas led to more refined predictions than correcting initial conditions in the verification area. Targeted observations in CNOP-based sensitive areas proved to be an effective method for improving short-term VTS prediction in summer in the Yellow Sea.