Evaluation of globally gridded SST products from NOAA, CMC and UKMeto using AIRS and CrIS SST measurements.
Bibliographic record
Abstract
The Sea Surface Temperature (SST) is a key component of climate research and daily globally gridded SST products are a key input to this effort. Here we evaluate the NOAA RTGSST, which goes back to 1996, the Canada Meteorological Center (CMC) SST, available since 2002, and the OSTIA SST by the UK MetOffice, available since 2012. The calibration of the three products is tied to the moored and floating buoys along the equator, but there are differences in the way all grid points are optimally filled. The 2016 annual mean between 30S and 30N, 299.7K, differed by only 8 mK. However zonal mean differences between the three products north of 30N and south of 30S latitude are of the order of 150 mK, and of opposite signs. Even more puzzling is that during 2016 the CMC was on average 150 mK colder than the OSTIA at 280K, while being warmer by 150mK at 290K. Differences of this magnitude are of concern when measure warming of the oceans at the rate of 15 mK/year. We use the daily mean and standard deviation and trends of the difference between the SST measured with AIRS (Atmospheric Infrared Sounder) since 2002 and CrIS (Crosstrack Interferometer Sounder) since 2012 to evaluate the three products.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".