Rapid reconstruction of temperature and salinity fields based on machine learning and the assimilation application

Abstract

Satellite observations play important roles in ocean operational forecasting systems, however, the direct assimilation of satellite observations cannot provide sufficient constraints on the model underwater structure. This study adopted the indirect assimilation method. First, we created a 3D temperature and salinity reconstruction model that took into account the advantage of the nonlinear regression of the generalized regression neural network with the fruit fly optimization (abbreviated as FOAGRNN). Compared with the reanalysis product and the WOA13 climatology data, the synthetic T/S (temperature and salinity) profiles had sufficient accuracy and could better describe the characteristics of mesoscale eddies. Then, the synthetic T/S profiles were assimilated into the Regional Ocean Model System (ROMS) using the Incremental Strong constraint 4D Variational (I4D-Var) data assimilation algorithm. The quantitative and qualitative analysis results indicated that compared with the direct assimilation of satellite observations, the root mean square errors (RMSEs) of temperature and salinity were reduced by 26.0% and 23.1% respectively by assimilating the synthetic T/S profiles. Furthermore, this method significantly improved the simulation effect of the model underwater structure, especially in the 300 m to 500 m water layer. Compared with the National Marine Data Center’s real-time analysis data, the machine learning-based assimilation system demonstrated a significant advantage in the simulation of underwater salinity structure, while showing a similar performance in the simulation of underwater temperature structure.