Bibliographic record
Abstract
Predictability of geophysical fluid dynamics at various scales remains a crucial challenge for accurate weather and climate forecasting. Following the pioneering framework established by Lorenz, numerous studies on homogeneous and isotropic turbulence have demonstrated that flows characterized by diverse scales may exhibit limited predictability. This limitation arises from the inevitable amplification of errors in the initial conditions from small scales to larger scales, even if the initial error is confined to small scales. This research investigates the predictability of freely decaying homogeneous stratified turbulence, which serves as a representative model for small-scale geophysical turbulence where rotational effects are negligible. Direct numerical simulations are employed to assess predictability by analyzing the growth of errors introduced in pairs of simulations with near-identical initial conditions; errors are modeled as the difference field of the pair. Previous studies have established a connection between the finite range of predictability and the slope of the kinetic energy spectrum. In the context of stratified turbulence, the shape of the energy spectrum exhibits a dependence on the buoyancy Reynolds number (Reb), particularly at lower values of Reb. This work conducts a comparative analysis of both the energy spectra and the error growth behavior across different regimes of stratified turbulence, encompassing a range of Reb values from O(1) to O(10). The sensitivity of the obtained results to the introduced error is investigated. Modifying the geometrical shape of the error (spherical vs cylindrical complement) and the cutoff wavenumber while maintaining the initial error kinetic energy did not significantly alter the error dynamics. The results are robust to variations in the method of error introduction.
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 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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".