Evaluation and Improvement of FY-4A/AGRI Sea Surface Temperature Data

Abstract

The advanced geosynchronous radiation imager (AGRI) aboard the Chinese Fengyun-4A (FY-4A) satellite can provide operational hourly sea surface temperature (SST) product. However, the temporal and spatial variation of the errors for this product is still unclear. In this article, FY-4A/AGRI SST is evaluated using the in situ SST from 2019-2021, and a cumulative distribution function matching method is adopted to reduce the errors. Statistical results show that the mean bias and root-mean-square error (RMSE) of FY-4A/AGRI SST are −0.37 °C and 0.98 °C, the median and robust standard deviation (RSD) are −0.30 °C and 0.90 °C. The variations in daily and monthly errors are large and there are no prominent seasonal variations during the period analyzed. There are negative biases exceeding −1.0 °C in low-mid latitude regions and larger positive biases in southern high latitude region. There are dependencies of satellite SST minus in situ SST on satellite zenith angle and on SST itself. After the bias correction, the bias and RMSE are reduced to −0.02 °C and 0.72 °C, and the median and RSD are reduced to 0.00 °C and 0.60 °C. On the time scale, the fluctuation ranges of bias and median are smaller. The difference of satellite SST minus in situ SST can reflect the diurnal variation of SST. The biases are generally within ±0.2 °C in full disk. The error dependencies on satellite zenith angle and SST are also greatly reduced.