Scaling turbulent atmospheric stratification. II: Spatial stratification and intermittency from lidar data
Bibliographic record
Abstract
Abstract We critically re‐examine existing empirical studies of vertical and horizontal statistics of the horizontal wind and find that the balance of evidence is in favour of the Kolmogorov kx−5/3 scaling in the horizontal, Bolgiano‐Obukov scaling kz−11/5 in the vertical corresponding to a Ds = 23/9 stratified atmosphere in (x, y, z) space. This interpretation is particularly compelling once one recognizes that the 23/9‐D turbulence can lead to long‐range biases in aircraft trajectories and hence to spurious statistical exponents in wind, temperature and other statistics reported in the literature. Indeed, we show quantitatively that one is easily able to reinterpret the major aircraft‐based campaigns (GASP, MOZAIC) in terms of the model. In part I, we have seen that this model is compatible with ‘turbulence waves’ which can be close to classical linear gravity waves in spite of their very different nonlinear mechanism. We then use state‐of‐the‐art lidar data of atmospheric aerosols (considered as passive tracers) in order to obtain direct estimates of the effective (‘elliptical’) dimension of the spatial part: Ds = 23/9 = 2.55 ± 0.02. This result essentially rules out the standard 3‐D or 2‐D isotropic theories or the anisotropic quasi‐linear gravity wave theories which have Ds = 3, 2, 7/3 respectively. In this paper we focus on the multifractal (intermittency) statistics showing that there is a very small but apparently real variation in the value of Ds, ranging for the weak and intense structures so that Ds ranges from roughly 2.53 to 2.57. We also show that the passive scalars are well approximated by universal multifractals; we estimate the exponents to be αh = 1.82 ± 0.05, αv = 1.83 ± 0.04, C1h = 0.037 ± 0.0061 and C1v = 0.059 ± 0.007 (h for horizontal, v for vertical). Copyright © 2008 Royal Meteorological Society
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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 | 0.004 |
| 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 | 0.001 |
| 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".