Give us local expert feedback!

The annual GHRSST science team meeting has just finished. The meeting was held in Cape Town, and it was great to learn about the interesting ocean dynamics around the coasts of southern Africa and the practical uses to which satellite SST datasets are put. Mostly in the SST CCI project we focus on global scale assessment of our products, but this meeting was useful in raising the issue of long-term variability and change in the datasets at small scales in coastal regions. For example, in the image below, upwelling regions off the western cape are resolved in the SST CCI analysis dataset  (narrow stretch of colder water running north along the coast from Cape Town). The fidelity of the variability in this feature from weeks to decades has never been assessed by us specifically: although regional applications of SST CCI data were tested in the Climate Assessment Report, none of the trail-blazer users were focussed on this oceanographically interesting area. Changes in SST in this region have practical implications for fisheries and coastal industry. Users of our data please note: if you work with our data and get insight into its utility or limitations in specific areas like this, we would like to hear from you, it is useful feedback!

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").

Collaborative agreement with JAXA

Within their 5th Research Announcement of opportunity for the Global Change Observation Mission, JAXA have accepted an SST CCI proposal "SST Algorithms for AMSR-2 Intended for Use in long-term Climate Data Records". We look forward to this collaboration.

The collaborative agreement is being finalised. The objectives are:

1.     To carry out development and testing of optimal estimation to generate SSTs from passive microwave satellite instruments using optimal estimation retrievals.
2. To ensure that the optimal estimation of SSTs from the microwave measurements is done in a way consistent with infrared methods.

Nadir - Forward Shifts in ATSR Multi-mission Level 1b archive v2.1

This summarises some results from Owen Embury looking at whether, with the latest ATSR multi-mission archive, there is still a need (as in Phase 1) to shift the forward view to better line up with the nadir view for the ATSRs. We always knew that our empirical shifts in Phase 1 were only approximate and hoped that the v2.1 archive would have resolved the issue completely. Bottom line: it is an improvement, but not perfect.

Here is a scene with the v2.0 nadir-forward collocation. Note that in the Nad-Fwd differences there are red and blue (warm and cold) differences along SST fronts (wavy lines) in the thermal (middle panel) and the island have a bright edge and dark shadow. Both these effects imply the two views are not perfectly collocated.

Here is the equivalent from v2.1. The effects are much less, but the opposite effects can just be discerned.

Here is the same scene with a shift of a pixel. The fronts are less obvious and the shadowing of the land is not really much different. For SST this is suggests an improvement.

Owen did a further analysis, minimising nadir-forward variances across many small scene extracts, to see if the offsetting effect is constant in time. It appears not to be, as per the example below:

At the beginning of the AATSR mission, the best shift of the forward view is -1 pixel along track and 0 across track on the left side of the swath -- but from 2007, it appears better to shift across track by +1. For other sensors, the equivalent curves suggest the instrument geometries are even less stable.

However, the good news is that pretty much all of the required shifts are no greater than 1 pixel, which is an improvement over v2.0, where shifts up to 3 pixels were required.

Datasets from SST CCI and their DOIs

The datasets generated to date by ESA's Climate Change Initiative project for Sea Surface Temperature  are available from the Centre for Environmental Data Archival via the page www.neodc.rl.ac.uk. Possibly it is most useful to go to the dataset pages using the DOIs of the datasets:

For SSTs derived only from the Advanced Very High Resolution Radiometers:
http://dx.doi.org/10.5285/782305d4-1ded-43f9-a0ed-40cb41ff0a43

For SSTs derived only from the Along-Track Scanning Radiometers:

http://dx.doi.org/10.5285/fe7a9d81-cfc0-4023-90da-0be37b803bc7
(This points to v1.0.  V1.1, addressing a few problems in v1.0, will be coming along soon.)

For a gap-filled, daily blend ("SST CCI analysis") of the above datasets (with good feature resolution):

http://dx.doi.org/10.5285/878bef44-d32a-40cd-a02d-49b6286f0ea4

To get data, you need to register just your e-mail address with the data centre. If you try to access data without logging in, you get a page that says "access to the dataset is restricted". This is misleading: there is no restriction on obtaining SST CCI data, you simply need to be logged in to the data centre.

Below is an example day from the SST CCI analysis. This image represents the sea surface temperature (SST) across the global oceans on 5th January, 2010. It is made by blending satellite-derived estimates of SST and filling gaps by an optimal interpolation -- a process referred to as 'analysis'. This SST analysis was created using data from the Along-Track Scanning Radiometers (ATSR) and Advanced Very High Resolution Radiometers (AVHRR), analysed using the Met Office OSTIA system. 

The particular value of the SST CCI dataset is that it has relatively high feature resolution, refers to a well-defined type of SST (temperature at 20 cm depth, daily average) and, unlike most satellite SSTs, is independent of in situ observations. The full dataset covers 1991 to 2010 and was created in Phase 1 of ESA's SST Climate Change Initiative (SST CCI). 

What is a sustainable system?

The project has two aims. The most obvious is to produce "climate data records" for sea surface temperature. SST is an "essential climate variable", which means that to understand and track climate variability and change, we need high quality records of how SST changes over time. With sufficient remote-sensing know-how, this can be achieved using infra-red imagery from Earth observing satellites.

The project has a limited lifetime, but the need for climate data records won't disappear. Therefore we have a second aim: in the process of delivering data, we will build a software system that can then be sustained to provide data in future. We don't know how that will be funded, at present, but nonetheless over the next 3 years the project will develop its software to a stage of being, in the jargon, "pre-operational".

 The system won't be able simply to be run untended, even in an sustained (or "operational") mode, without maintenance of the science, and cyclic improvement to our techniques. This is because satellites come and go, and to get new observations to the standard required for inclusion in the SST CDR requirements ongoing Earth observation development work. Moreover, overtime, we discover how to make CDRs better, and therefore should reprocess the whole dataset consistently in the improved manner.

Thus, the system includes software and human experts, in a cycle of sustained production of data (as the observations come in) and periodic reprocessing. Conceptually it is like this: