Abstract
Sea surface temperature (SST) is critical for global climate change analysis and research. In this study, we used visible and infrared scanning radiometer (VIRR) sea surface temperature (SST) data from the Fengyun-3C (FY-3C) satellite for SST analysis, and applied the Kalman filtering methods with oriented elliptic correlation scales to construct SST fields. Firstly, the model for the oriented elliptic correlation scale was established for SST analysis. Secondly, observation errors from each type of SST data source were estimated using the optimal matched datasets, and background field errors were calculated using the model of oriented elliptic correlation scale. Finally, the blended SST analysis product was obtained using the Kalman filtering method, then the SST fields using the optimum interpolation (OI) method were chosen for comparison to validate results. The quality analysis for 2016 revealed that the Kalman analysis with a root-mean-square error (RMSE) of 0.3243 °C had better performance than did the OI analysis with a RMSE of 0.3911 °C, which was closer to the OISST product RMSE of 0.2897 °C. The results demonstrated that the Kalman filtering method with dynamic observation error and background error estimation was significantly superior to the OI method in SST analysis for FY-3C SST data.
Highlights
It should be noted that the optimum interpolation (OI) analysis used the same Sea surface temperature (SST) observations and oriented elliptic correlation scale model as the Kalman filtering analysis in the study; the values of the noise-to-signal standard deviation ratios for OI analysis were provided by Liao et al [13], which can be regarded as the error ratios of the observation field to the background field
The observation errors of visible and infrared scanning radiometer (VIRR) SST were obtained from the bias correction process with optimal matched datasets
Values of 0.3911 and 0.3243 ◦ C, respectively; these values were still higher than the 0.2897 ◦ C from the OISST product
Summary
The analysis of multi-source SST data requires us to consider the effective range of the SST correlation areas and the weight assigned to each SST observation. The effective range of the correlation areas determines the number of effective observations, which mainly depends on the correlation scales applied for SST analysis. The correlation of SST increments is often affected by factors, such as surface ocean currents and the heat transfer effect of SST, and these factors should have orientational variability in different sea areas [10,11,12]. The weights of SST observations are determined by objective analysis and error estimation of SST data, and the optimum interpolation (OI) and Kalman filtering methods are commonly used for the objective analysis of SST data [8,15]. Kalman filtering is a classical data assimilation method that is similar to the OI method [22,23], but
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