Identification of possible improvement in Low and Mid latitude Sea Surface Temperature Retrievals
In the context of the OSI SAF Visiting Scientist Program, Prof Christopher Merchant from the University of Reading, UK, worked on the identification of possible improvement in Low and Mid latitude Sea Surface Temperature Retrievals.
Priorities for sea surface temperature (SST) algorithm development relative to OSI SAF state-of-the-art were reviewed. In conclusion, the improvement of optimal estimation SST (OE SST) is seen as offering the most important potential gains. Three areas are most relevant: better estimation of the error covariance matrices for OE SST; extension of the retrieval state vector to include desert dust; and bias aware techniques to improve bias correction. The prerequisites for the first topic are in largely place, and thus a small study on that topic is proposed for this year, 2018. The prerequisites for the second will require more time and preparation, and thus could be addressed during 2019. The third area is most logically addressed after upgrades to the OE SST scheme, and is thus an concept for future work.