Abstract
Based on the large volume of observational data obtained from Argo and several satellites, an increasing number of datasets are being developed and applied to oceanographic research. However, there are still problems such as sparse subsurface observations, insufficient parameters, and weak pertinence. This study provides a basic framework for high-resolution data fusion that focuses on the multi-source observations in the Western North Pacific. Multi-source observations from satellites, Argo floats, and historical in situ profiles are fused using a statistical model and a gradient-dependent optimal interpolation method. A daily gridded dataset with a 0.25° horizontal resolution is developed, which includes temperature, salinity, and currents. The results show that the correlation coefficients between the observations and the inverted profiles of temperature and salinity are about 0.99 and 0.94, respectively, with mean root mean square errors of about 1.27 °C and 0.13, respectively. In the Northwest Pacific Ocean, the most suitable parameter settings are a search radius of 1.5° in longitude and latitude, correlation scale constant of 0.25°, and relative observation error of 2. Consequently, the average RMSEs of the fused temperature and salinity fields are 0.43°C and 0.056, respectively. Compared with other reanalysis datasets, the product constructed in this study retains more high-frequency ocean signals, and its temperature error relative to XBT observations is also the smallest. Furthermore, the dataset effectively depicts the characteristics of marine dynamic processes such as the Kuroshio paths and mesoscale eddies.