Lidar data remote sensing of aerosols and anisotropic space-time generalizations of Corrsin-Obukov and Kolmogorov laws
Bibliographic record
Abstract
In this paper we concentrate on the space-time behaviour of atmospheric passive scalars. We first recall that – although the full ( , , ,)x y z t turbulent processes respect an anisotropic scale invariance, that due to advection, the generator will generally not be a diagonal matrix. This implies that the scaling of (1D) temporal series will generally involve three exponents in real space: 1/3, , 3/5; for spectra τβ = 5/3, 2, 11/5 with the first and last corresponding to domination by advection (horizontal and vertical respectively), and the middle to pure temporal development. We survey the literature and find that almost all the empirical results indeed have τβ in the range 5/3 or 2 (the 11/5 value requires apparently unrealistic vertical winds). We then use state-of-the-art vertically pointing lidar data of backscatter ratios from both aerosols and cirrus clouds yielding several ( ,)z t vertical space- time cross-sections with resolution of 3.75 m in the vertical, 0.5 s-30 s in time and spanning 3-4 orders of magnitude in temporal scale. We first tested the predictions of the anisotropic, multifractal extension of the Corrsin-Obukhov law in the vertical and in time separately finding that the cirrus and aerosols both followed the theoretical (anisotropic) scalings accurately; most (but not all) of the cases showed a dominance by the horizontal
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".