Blending of satellite SST products using ensemble Bayesian model averaging (EBMA)

Abstract

ABSTRACTSea surface temperature (SST) is an important parameter in understanding atmosphere–ocean circulation processes and monitoring global climate change. In addition to in situ observations of SST, a series of satellite-borne instruments provide global coverage of SST through infrared and microwave remote sensing. This study was the first application of the ensemble Bayesian model averaging (EBMA) method to the blending of satellite SST products to minimize inherent uncertainties and improve the validation statistics. Monthly SST products from moderate resolution imaging spectroadiometer, Advanced Very High Resolution Radiometer and Advanced Microwave Scanning Radiometer-EOS were used as ensemble members. The mean bias and root-mean-square error (RMSE) of the EBMA method were better than those of the individual members or generic methods such as ensemble mean and median. This is because the weighting scheme adjusted by the expectation–maximization algorithm was based on the suitability of each member derived from training procedures. The errors of EBMA in our experiment had almost no spatial and temporal autocorrelation with regard to the latitude and month, which implies that the EBMA method can serve as a viable option for blending of satellite SST, although more experiments are necessary to determine its feasibility in more detail.