Evolution of Copernicus Marine Surface Temperature Products in Coastal Regions and High Latitudes
Bibliographic record
Abstract
This talk was given at the 25th International SST Users’ Symposium and GHRSST Science Team Meeting (GHRSST25) held in Montreal, Canada/Online from 10 – 14 June 2024. Explore the full program at GHRSST25 on the GHRSST Website here. Abstract Accurate long-term measurements of marine surface temperatures are required to understand key physical processes at the ocean-atmosphere interface and any changes that may occur to these processes over time. The Copernicus Sea and Land Surface Temperature Radiometer (SLSTR) is a multi-spectral dual-view radiometer with two on-board blackbodies and cooled detectors ensuring accurate radiometric measurements for the estimation of sea surface temperature (SST) and sea-ice surface temperature (sea-IST). Operational retrieval of SST from satellite thermal infrared (TIR) radiances relies on a pre-processing step to identify and discard cloud-affected observations. Coastal regions offer particularly challenging regimes for cloud detection owing to an increased frequency of turbid waters modifying ocean colour and SST fronts generating sharp gradients in the TIR. Here we present new approaches for cloud detection prior to SST retrieval from SLSTR and illustrate the benefits using case studies in coastal zones covering optically bright waters and around strong ocean fronts. The evolution also includes a new high-latitude combined SST and sea-IST product implemented at the nominal SLSTR TIR resolution (1 km) and routinely processed for SLSTR-A/B over the high latitude regions (>50° poleward in both the Northern and Southern hemispheres). Sea-IST cloud masking is provided by the EUMETSAT NWC SAF PPS cloud and cloud probability algorithms. Initial validation results indicate the sea-IST performance for SLSTR-B in particular is very good and already close to the Essential Climate Variable (ECV) goal of 1 degree Celsius. Moreover, the challenging cases of winter / night-time performance is also of good quality and not too far from daytime performance. Pre-operational products in GHRSST L2P format will be available to users from the Copernicus WEkEO DIAS reference service for evaluation from April 2024, with operational implementation expected from Spring 2025.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".