Modelling convectively induced secondary circulations in the <i>terra incognita</i> with <scp>TerrSysMP</scp>
Bibliographic record
Abstract
Advances in high‐performance computing have led to kilometre and even sub‐kilometre scale regional simulations with numerical weather prediction models. This range of grid resolution – also termed ‘terra incognita’ – approaches the length‐scale of the most energetic eddies of the convective atmospheric boundary layer (ABL), which accordingly can only be inadequately resolved. This dilemma becomes particularly obvious when simulating convectively induced secondary circulations (CISCs) in the terra incognita, because the modelled CISC‐like circulations – also termed M‐CISCs – are poorly resolved and exhibit a grid‐resolution dependence. Previous studies have pointed out this problem using different modelling platforms and also suggested options for attenuating poorly resolved M‐CISCs with methods specific to the applied ABL schemes. This study examines M‐CISCs at horizontal grid resolutions of O(1 km), using the Terrestrial Systems Modelling Platform (TerrSysMP) for idealized and real case studies. The analysis of simulations using different surface heterogeneity and grid resolutions of O(1 km) shows that the presence of a superadiabatic layer near the surface and the increase of the horizontal grid resolution allow the critical Rayleigh number to be exceeded, generating poorly resolved M‐CISCs, whose amplitudes are strongly dependent on the horizontal grid resolution. We show that the asymptotic turbulent mixing length‐scale in the used ABL scheme can be tuned in a way that M‐CISCs are attenuated while the non‐resolved turbulence dealt with by the ABL scheme effectively propagates the surface fluxes into the ABL and sustains reasonable ABL profiles.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".