Kinetic Energy Spectra and Spectral Budget of Radiative–Convective Equilibrium
Bibliographic record
Abstract
Abstract The atmospheric kinetic energy spectrum and energy cascade are investigated in idealized simulations of radiative–convective equilibrium (RCE). WRF is employed to perform cloud-resolving simulations of an idealized radiative–convective equilibrium with and without aggregation with Δx = 4 km. The horizontal kinetic energy (HKE) spectrum for the aggregated simulation in the upper troposphere is steeper than the nonaggregated case and closer to −5/3. The HKE spectra for the nonaggregated simulation in the upper troposphere and the lower stratosphere are much shallower than the −5/3 spectrum. In the upper troposphere, the divergent kinetic energy has a similar magnitude to the rotational kinetic energy in both the nonaggregated simulation and aggregated simulation. Energy is mainly gained from the buoyancy flux and mainly lost from the vertical energy flux for scales larger than 20 km. Downscale energy transfer is found in the upper troposphere. Numerical dissipation is the main source of energy loss at small scales. In the lower stratosphere, the divergent kinetic energy dominates the kinetic energy spectrum in both simulations. Energy is mainly gained from the vertical energy flux and is balanced by the loss from the buoyancy flux term, transfer term, and dissipation. An Eliassen–Palm flux analysis suggests that wave–mean-flow interaction may be responsible for the upscale energy transfer found in the lower stratosphere. The magnitudes of our kinetic energy spectra are similar to spectra calculated from aircraft data. Rotation is found to promote aggregation and steepen the energy spectrum.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".