An overview of sea state conditions and air‐sea fluxes during RaDyO
Bibliographic record
Abstract
Refining radiative‐transfer modeling capabilities for light transmission through the sea surface requires a more detailed prescription of the sea surface roughness beyond the probability density function of the sea surface slope field. To meet this need, exciting new measurement methodologies now provide the opportunity to enhance present knowledge of sea surface roughness, especially at the microscale. In this context, two intensive field experiments using R/P Floating Instrument Platform were staged within the Office of Naval Research's Radiance in a Dynamic Ocean (RaDyO) field program in the Santa Barbara Channel and in the central Pacific Ocean south of Hawaii. As part of this program, our team gathered and analyzed a comprehensive suite of sea surface roughness measurements designed to provide optimal coverage of fundamental optical distortion processes associated with the air‐sea interface. This contribution describes the ensemble of instrumentation deployed. It provides a detailed documentation of the ambient environmental conditions that prevailed during the RaDyO field experiments. It also highlights exciting new sea surface roughness measurement capabilities that underpin a number of the scientific advances resulting from the RaDyO program. For instance, a new polarimetric imaging camera highlights the complex interplay of wind and surface currents in shaping the roughness of the sea surface that suggests the traditional Cox‐Munk framework is not sufficient. In addition, the breaking crest length spectral density derived from visible and infrared imagery is shown to be modulated by the development of the wavefield (wave age) and alignment of wind and surface currents at the intermediate (dominant) scale of wave breaking.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".