Bibliographic record
Abstract
Abstract The role of moist processes in the development of the mesoscale kinetic energy spectrum is investigated with numerical simulations of idealized moist baroclinic waves. Dry baroclinic waves yield upper-tropospheric kinetic energy spectra that resemble a −3 power law. Decomposition into horizontally rotational and divergent kinetic energy shows that the divergent energy has a much shallower spectrum, but its amplitude is too small to yield a characteristic kink in the total spectrum, which is dominated by the rotational part. The inclusion of moist processes energizes the mesoscale. In the upper troposphere, the effect is mainly in the divergent part of the kinetic energy; the spectral slope remains shallow (around −) as in the dry case, but the amplitude increases with increasing humidity. The divergence field in physical space is consistent with inertia–gravity waves being generated in regions of latent heating and propagating throughout the baroclinic wave. Buoyancy flux spectra are used to diagnose the scale at which moist forcing—via buoyant production from latent heating—injects kinetic energy. There is significant input of kinetic energy in the mesoscale, with a peak at scales of around 800 km and a plateau at smaller scales. If the latent heating is artificially set to zero at some time, the enhanced divergent kinetic energy decays over several days toward the level obtained in the dry simulation. The effect of moist forcing of mesoscale kinetic energy presents a challenge for theories of the mesoscale spectrum based on the idealization of a turbulent inertial subrange.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".