The Single Sensor Error Statistics (SSESs) represent the primary value-added component of GHRSST L2P products. Their use in L4 products should lead to improved SST analyses. Work is needed to establish the most useful methods of incorporating the SSES bias and/or standard deviation into L4 analysis schemes.
Establish the usefulness of the SSES bias and standard deviation
Assessment of the usefulness of the SSES to various L4 schemes first requires the establishment of a set of metrics. Comparison with Argo data is well-established as a measure of SST accuracy but further metrics are needed to assess, for example, reduction in magnitude and structure of stochastic bias correction fields, observation minus background, and internal consistency. Different data providers use different methods to calculate SSES and these methods will be evaluated. The output of this activity will be a set of recommendations on the features of various SSES.
How best to use the current SSES
The variation in usefulness of SSES between different analysis schemes is being investigated with a view to understanding why SSES have different impacts in different analysis schemes. This activity will form the basis of a set of recommendations to L4 producers on how best to employ SSES.
Forum on future evolution of SSES
Future evolution of SSES involves questions such as how L2 providers might calculate correlated errors, how L4 producers might use correlated errors, and how to remove dependence on drifting buoys and obtain properly representative uncertainties that are appropriate for assimilation into L4.
Last Report to The Science Team (GHRSST-XXI, 2020)
Task Team Members
Co-Chairs: Andy Harris, Simon Good
Members: Gary Corlett, Alexey Kaplan, Charlie Barron, Chongyuan Mao, Christopher Merchant, Chunxue Yang, Dorina Surcel Colan, Helen Beggs, Jacob Høyer, Lei Guan, Bruce McKenzie, Mike Chin, Nick Rayner, Prasanjit Dash, Stepane Saux Picart, Werenfrid Wimmer, Peter Minnett, Eileen Maturi