Use of Uncertainty Inflation in OSTIA to Account for Correlated Errors in Satellite-Retrieved Sea Surface Temperature Data

Abstract

Sea surface temperature (SST) analysis systems such as the Operational Sea Surface Temperature and Ice Analysis (OSTIA) use statistical methods to combine observations together with a first guess field to create spatially complete maps of SST. These commonly assume that observation errors are uncorrelated, yet some errors (such as due to retrieval issues) can be correlated. Information about errors is used by the analysis system to determine the weighting to apply to the observations, hence this incorrect assumption could degrade the analysis. A common technique to mitigate for this is to inflate the observation uncertainties. Using information on observation error correlations provided with data produced by the European Space Agency (ESA) SST Climate Change Initiative (CCI) project, idealised tests were carried out to determine how this inflation technique can best be applied. These showed that applying inflation in situations where the observation errors are correlated over similar or larger distances to the errors in the background can cause unpredictable and sometimes negative results. However, in situations where the observation error correlation length scale is relatively small, inflation should improve the analysis. These findings were adapted to the OSTIA system and various configurations were tested. It was found that the inflation methods did not affect statistics of differences between the analyses and independent Argo reference data. However, the SST gradients were affected, particularly if some observation uncertainties were inflated but others were not. The results from both the idealised tests and the application to the real system therefore highlight that it is challenging to implement the inflation method in the case of an SST analysis system and show the need for assimilation schemes that can make full use of observation error correlation information.