The spectral composition of fluxes and variances over land and sea out to the mesoscale
Bibliographic record
Abstract
We discuss the accuracy requirements for measuring mesoscale (roughly horizontal scales > 10 km or 5 to 10 times the planetary boundary-layer (PBL) depth) fluxes in the convective PBL, and the ability of current research aircraft to achieve this accuracy. We conclude that aircraft equipped with inertial nagivation systems capable of < 3 km hr −1 navigational accuracy are able to resolve mesoscale fluctuations in velocity, and thus variances and fluxes on the mesoscale. We then discuss measurements of velocity and scalar spectra, and cospectra of vertical velocity with horizontal velocity components and scalars, obtained from long flight legs with the National Center for Atmospheric Research Electra aircraft over the boreal forest of Canada in summer during the BOreal Ecosystem-Atmosphere Study (BOREAS), over the tropical Pacific Ocean from the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE), and over the East China Sea during wintertime cold-air outbreaks from the Air Mass Transformation Experiment (AMTEX). Each of these studies has somewhat different forcings and boundary conditions, so we can compare their consequences on the spectra and cospectra. On average, we found no significant scalar or momentum fluxes for horizontal scales > 10 km. We also develop a simple model based on observed thermal structure to explain the phase angle between vertical velocity and the along-wind horizontal velocity as a function of height, which shows good agreement with the observed phase angle in AMTEX.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".