Heat flux estimates from a synthesis of satellite observations and a hydrodynamic model (with application to Long Island Sound)
Bibliographic record
Abstract
Estimating surface heat fluxes via direct covariance measurements or bulk formulae is observation-intensive and costly. We present a methodology whereby we estimate net surface heat fluxes as the difference between the depth-integrated heat tendencies and the depth-integrated horizontal heat exchanges in a hydrodynamic model. We calibrate the model to achieve a good representation of mixing and advection and then assimilate satellite sea-surface-temperature (SST) observations into the model at an eight-day scale. The SST data assimilation forces a good representation of observed temperatures and heat tendencies both at the surface and throughout the water column. We estimate the horizontal heat exchange directly from the model output and then infer the surface fluxes required to close the budget. When we apply this methodology to a model with prescribed surface heat fluxes and without data assimilation, we can recover the prescribed fluxes with an RMS error of ±10 Wm−2 and an r2 of 0.998. When we compare our results to those estimated using COARE bulk formulae with observations in western Long Island Sound, we find similarly good agreement.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".