Copernicus Sentinel-3 Sea (and sea-Ice) Surface Temperature: product status, evolutions and projects
Bibliographic record
Abstract
Presented at the GHRSST XXIII international science team meeting, 27 June-1 July 2022, online and in-person (Barcelona). #GHRSST23 Short abstract The first Copernicus Sentinel-3 satellite was launched on 16th February 2016 and the second on 25th April 2018. The Sentinel-3A/B satellites observe high quality Sea Surface Temperature (SST) from the Sea and Land Surface Temperature Radiometer (SLSTR). These accurate SSTs provide a reference satellite SST dataset and time-series for other satellite SST missions and are important for climate monitoring. Operational SLSTR SST products have been distributed from the EUMETSAT marine centre since 5th July 2017. EUMETSAT performs ongoing validation activities for SLSTR SST, together in coordination with the Sentinel-3 validation team, and real time monitoring is shown from the link to metis.eumetsat.int. Validation results show the products performing extremely well, and dual-view SSTs recommended to be used as a reference SST source, with some users already using SLSTR SST reference data. EUMETSAT began activities in 2021 towards revised and improved algorithms for SLSTR SST and sea-ice Surface Temperature (ST) with the intention of the operational implementation of SLSTR day-2 SST and day-1 sea-ice ST by 2025. This includes improvements to the Bayesian cloud-screening, retrieval coefficient updates, inclusion of depth SST in addition to skin SST, potential evolution to include full nadir grid, and the first operational implementation of sea-ice ST for SLSTR. Shorter term improvements, planned by 2023, include revised SST coefficients, updates to ADI and DDI tests, and updates to SSES scheme. A demonstrational sea-ice ST prototype processor has been implemented on the Copernicus WEkEO platform. Recent results and information on further ongoing projects and evolutions relating to Sea Surface Temperature at EUMETSAT will be presented. Ongoing validation activities are important for assessing and maintaining SLSTR SST product quality. In addition to inter-comparisons with other satellite SST, key components are collocations and analyses with drifting buoy SSTs. A Copernicus-funded EUMETSAT project called ‘Towards Fiducial Reference Measurements (FRM) of Sea-Surface Temperature by European Drifters’ (TRUSTED) is now in its fifth year. Over 150 high-resolution drifting buoys (HRSST-2), plus calibration per sensor, have been deployed so far. A service of measurements will continue at a rate of additional 25 buoys per year, with a focus at higher latitudes and other priority regions. Activities have also begun towards the requirements, design and prototype of sea-ice surface temperature drifting buoys needed for the validation of Copernicus satellite sea-ice surface temperature products. Activities continue to assess and validate these reference buoys as FRM for SLSTR together in coordination with the GHRSST HRSST Task Team.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.022 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".