System Requirements at CCI Programme Level

The Climate Change Initiative programme as a whole has defined System Requirements and Data Standards in two documents. I, Owen, SST CCI project manager (Hugh) and the SST CCI system engineering team (Ralf and Martin) reviewed those documents against our plans last week. This was partly in response to a query from ESA, and we thought it worthwhile to look at the issues in detail for our own benefit from the point of view of making sure the system we are building is as good as possible.

The brief conclusions are:

1. SST CCI is fully in line with the data standards, since the data standards are compatible with GHRSST standards
2. The SST CCI project will internally meet 50% of the CCI programme system requirements (SRs) in Phase 2
3. We think 25% of the SRs can only be done through programme-level investment and activity (some of which is foreseen, e.g., data portal work will be commissioned by ESA)
4. We think 12% of the SRs are relevant only in the context of sustained operations after Phase 2, and will require additional work on the SST CCI system at that time
5. We question, disagree with or don't understand the remaining requirements

It was useful to us to have identified through this exercise the SRs that fall into category 4.

Petrenko et al (2014)

"Evaluation and selection of SST regression algorithms for JPSS VIIRS" by Petrenko et al has been published in the Journal of Geophysical Research: DOI: 10.1002/2013JD020637. It compares validation statistics for a number of coefficient-based algorithms for sea surface temperature (SST) on a common basis, which is a good thing to see. The formulation from the Ocean and Sea Ice Satellite Application Facility turns out to be the best performing.

A point of interest is that the OSI-SAF algorithm was not the one that gave the minimum standard deviation against drifting buoys. An algorithm from the Navy Research Laboratory gave considerably lower spread (0.36 K compared to 0.42 K). However, the NRL algorithm was rejected on the basis that its sensitivity to SST* was greatly suppressed -- only 40%. This would mean, for example, that diurnal variability would be under-estimated by 60% using the NRL algorithm. This trade-off (apparent "accuracy" vs. sensitivity) has been discussed in the literature before, but as far as I recall, this is the first paper not involving SST CCI team members that has seriously used sensitivity as a criterion in algorithm selection.

*Sensitivity is the amount the satellite SST changes per unit of real SST change. Ideally, we would want 100% -- i.e., 1 K change in the satellite SST for a 1 K change in the real SST. However, this does not happen where a retrieval relies heavily on information brought to the observation ("prior information").