Inter-Comparisons of Daily Sea Surface Temperatures and In-Situ Temperatures in the Coastal Regions
Bibliographic record
Abstract
In this study, seven, global, blended, sea surface temperature (SST) analyses, including Operational SST and Sea Ice Analysis (OSTIA), Canadian Meteorological Centre (CMC) analysis, Optimum Interpolation SST (OISST), Remote Sensing System (REMSS) analysis, Multi-scale Ultra-high Resolution SST (MURSST), Merged Satellite and In situ Data Global Daily SST (MGDSST), and Geo-Polar Blended SST (Blended SST) were conducted. In-situ temperature measurements were used for the years 2014–2018, from 35 narrowly-spaced buoys distributed along the Korean Peninsula coast, to investigate how well the SST analyses represent the temperatures at the coastal regions. Contrary to the overall accuracy of the SSTs in the global ocean and offshore regions, the root-mean-square errors for the analyses were relatively large over 1.27 K. Specifically, all SST analyses resulted in warm biases over 0.31 K, which became quite distinctive in the western and the southwestern coastal regions. Investigation of the errors identified relationships with the coastal zones of vigorous tidal mixing, shallow bathymetry, and absence of microwave measurements. Overall, temporal wavelet coherency between in-situ measurements and SST products revealed high coherency of greater than 0.8 in periods longer than 180 days, however, low coherency (<0.5) in the period shorter than 10 days was observed. Inter-comparisons between the SST analyses illustrated clear spatial differences in the correlations at both the coastal regions, along the southwestern coast of the Korean Peninsula and in the frontal regions, and in the marginal seas of the Northwest Pacific. Overall, the results emphasized on the importance of using real-time in-situ measurements as much as possible, to overcome the increasing SST errors in coastal regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".