Climatological Features of the Weakly and Very Stably Stratified Nocturnal Boundary Layers. Part III: The Structure of Meteorological State Variables in Persistent Regime Nights and across Regime Transitions
Bibliographic record
Abstract
Abstract The evolution of profiles of meteorological state variables during nights with and without transitions in the nocturnal stably stratified boundary layer (SBL) between weakly stable (wSBL) and very stable (vSBL) regimes, as classified by a hidden Markov model, is examined at nine different tower sites. During wSBL-to-vSBL transitions, inversion strengths increase, near-surface winds decelerate, and atmospheric layers vertically decouple. Turbulence kinetic energy (TKE) steadily decreases before wSBL-to-vSBL transitions and fluctuations of the vertical velocity become weak. In contrast to land-based sites where wSBL-to-vSBL transitions are normally caused by surface cooling, at sea-based stations the transitions generally are initiated by advection of warm air aloft. The vSBL-to-wSBL transition is characterized by a fast breakdown of the inversion strength, acceleration of wind profiles, and a restored vertical coupling of the atmospheric flow. TKE recovers on time scales of minutes first in atmospheric levels between 50 and 100 m. Profiles of state variables for the two different regimes during very persistent nights (nights without SBL regime transitions) are clearly separated and similar to structures during nights with transitions away from transition times. During very persistent nights the wind conditions stay relatively steady. Similarly, the temperature is steady after an initial adjustment time at sunset (wSBL) or shortly after sunset (vSBL). Even though nights with and without transitions are a common feature of the SBL, there is no clear indicator in Reynolds-averaged mean variables that distinguishes very persistent nights from nights with transitions.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".