Paper Info
Title
Sea Surface Temperature Analysis for Fengyun-3C Data Using Oriented Elliptic Correlation Scales
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 degrees C had better performance than did the OI analysis with a RMSE of 0.3911 degrees C, which was closer to the OISST product RMSE of 0.2897 degrees 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.
Year
DOI
Venue
2021
10.3390/s21238067
SENSORS
Keywords
DocType
Volume
oriented elliptic correlation scales, Kalman filtering, sea surface temperature, FY-3C VIRR data
Journal
21
Issue
ISSN
Citations 
23
1424-8220
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Zhihong Liao100.34
Bin Xu200.34
Junxia Gu300.68
Chunxiang Shi431.17