Vertical cascade structure of the atmosphere and multifractal dropsonde outages
Bibliographic record
Abstract
We use 220 atmospheric profiles from state‐of‐the‐art dropsondes to test the predictions of multiplicative cascade models of the atmosphere on the horizontal velocity, pressure, temperature, log potential temperature, log equivalent potential temperature, air density, humidity, and vertical sonde velocity. We found that the predictions were accurately verified (to within ±1 to ±2% over 10 m to 1 km for the statistical moments up to second order); the effective outer cascade scale Leffwas in the range 1–30 km. In order to perform the analyses and to correctly interpret the results, we needed to overcome technical difficulties caused by the sonde's highly intermittent sampling. This intermittency is the result of both data outages and variable sonde fall speeds; we (surprisingly) found that the outages also had a cascade structure. The wide‐range scaling of the sampling rate implies a variable sonde resolution, so that interpolation onto regular grids should generally be avoided (e.g., it would give rise to serious artifacts in estimating the corresponding spectra). In earlier studies, before the cascade nature of the outages was understood, interpolation was avoided by studying the fluctuations using all the pairs of measurement points; this was adequate for fluctuation scaling exponents in the range 0 ≤ H ≤ 1. However, determining the cascade structure involves systematically degrading the resolution of fluxes (not fluctuations) so that the variable resolution and their attendant biases could not be avoided. We therefore developed a new method of estimating the fluxes and theoretically determined the corrections necessary to estimate the unbiased exponents. The resulting sonde cascade picture was given further support by (much more straightforward) analysis of uniformly sampled vertical cross sections of the atmosphere obtained from airborne lidar. Using the turbulent fluxes obtained from these various sources, we determined the corresponding cascade regimes and the corresponding exponents as well as the small deviations from the theoretical behavior. In addition to the fluxes, we also studied the fluctuations. To do this we generalized the data point pair method (restricted to nonconservation parameters 0 ≤ H ≤ 1) to data triplets (extending the method to 0 ≤ H ≤ 2). The resulting fluctuations were analyzed using (generalized) structure functions. We found that while the scaling of the fluxes often broke down at scales greater than about 1 km, the scaling of the fluctuations extended over the entire range 10 m to 10 km.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".