A composite look at short‐time‐scale sea‐surface temperature changes in the western North Pacific based on ships and buoys
Bibliographic record
Abstract
Abstract A simple method of interpolating sea‐surface temperature (SST) and its measurement method is applied to western North Pacific ship and buoy observations taken between 1970 and 2000. Comparisons are made between the resulting quasi‐daily in situ analyses and more inclusive analyses based mainly on satellite infrared observations. In terms of analysis differences, spatial correlation, and temporal spectra, the in situ analyses are found to be of moderate quality and are taken to be appropriate for composite diagnoses. This use is illustrated in a novel comparison of short‐time‐scale SST changes observed during the passage of two groups of midlatitude cold‐season cyclones. Transient cooling is found to be preferentially associated with the stronger of the two cyclone groups. The significance of this cooling is quantified and the possibility of oceanic mixed‐layer cooling and heat loss from bucket observations is explored. Although the bias in bucket observations is confirmed using composites constructed without such observations, the signature of transient cooling is still apparent. (Independent bucket observations also permit composite analysis errors to be estimated.) Engine intake observations are then identified as a primary source of both SST information in general and of the transient cooling signal in particular. A related bias in this type of observation is proposed. Greater sophistication in the analysis of in situ observations is also briefly discussed. Copyright © 2010 Royal Meteorological Society
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".