Horizontal cascade structure of atmospheric fields determined from aircraft data
Bibliographic record
Abstract
Aircraft measurements of the power spectra of the horizontal wind field typically find a transition from ≈ k−5/3 to ≈ k−2.4 at scales somewhere around 40 km (k is a wave number). In the usual interpretation this represents a transition between an isotropic three‐dimensional (3‐D) (k−5/3) and an isotropic 2‐D (k−3) turbulence; we have recently argued that the turbulence is so highly anisotropic that it has different exponents in the horizontal and vertical. When coupled with gently sloping isobaric aircraft trajectories this predicts the break as a transition from a roughly horizontal spectrum at small scales to the spurious appearance of the vertical spectrum at large scales. If the atmosphere indeed has wide‐range horizontal scaling, then it is important to test out the multiplicative cascade models that predict its statistical behavior. In this paper, we do this by analyzing wind, temperature, pressure, and humidity data from the Winter Storm 2004 experiment using 24 aircraft legs, each 1120 km long and at 280 m resolution. We analyze both the turbulent fluxes and the fluctuations showing that in spite of the nonflat trajectories, there is good evidence of roughly planetary‐scale multiplicative cascades. By carefully determining the scale‐by‐scale effects of intermittency on the aircraft altitude and measurements, we estimate the corresponding scaling exponents. We argue that our results should finally permit the emergence of a long‐needed consensus about the basic scale‐by‐scale statistical properties of the atmosphere. They also point to the urgent need to develop anisotropic scaling models of turbulence.
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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 | 0.002 |
| 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".