Evaluation of AIRS and CrIS SST Measurements Relative to Three Globally Gridded SST Products Between 2013 and 2019
Bibliographic record
Abstract
Globally gridded sea surface temperatures (SSTs) provide key data for long-term monitoring of the stability of satellite data. Despite apparent limitations, accurate hyperspectral data can provide useful independent information to critique the stability of global SST products on the annual-to-decadal time scale. We compared data from atmospheric infrared sounder (AIRS) on EOS Aqua and Crosstrack Interferometer Sounder (CrIS) on SNPP with the SST products from NOAA/NESDIS [real time global (RTG)], the Canadian Meteorological Centre (CMC), and the U.K. Met Office [Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA)]. For the 2013–2019 period, the overall standard deviation of the difference between AIRS and the RTG was 0.55 K, with an increasing trend over time. In contrast, the standard deviation of the difference between the AIRS, the CMC, and the OSTIA dropped steadily to below 0.4 K, a level previously seen only in SST products relative to independent buoy data. Unexplained biases between the observed and the gridded SSTs at the 100-mK level are consistent with the already existing estimates of the AIRS and CrIS absolute calibration accuracy. However, the AIRS and CrIS observations both show artifacts in all three SST products, increasing with distance from the equator, with the CMC artifacts being the smallest. Even with the CMC, a trend of 4 mK per year relative to AIRS and CrIS was observed between 2013 and 2019 for the 30S–30N oceans. Investigation of the underlying causes of the observed discrepancies requires further work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".