A spatial and temporal stochastic cascade analysis of meteorological models and reanalyses
Bibliographic record
Abstract
This thesis investigates the hypothesis that the stochastic structure of deterministic models of the atmosphere is captured by multiplicative cascade processes. Using data from reanalyses (ERA 40) and two meteorological models (GFS, GEM), we investigate the spatial and temporal cascade structures of the temperature, humidity, and horizontal wind at various altitudes, latitudes, and forecast times. Over the range spanning from the model dissipation scales (â100 km) to at least 5000 km and for statistical moments up to order 2, the cascade predictions are satisfied to typically better than ±1%. In time, we find corresponding cascade structures with outer scales of roughly 15 days. By constructing space-time diagrammes, we find they are roughly linear up to 5-10 days with transformation velocities of about 1000 km/day, as predicted based on the solar energy flux. This transition time scale, which corresponds to planetary size structures, objectively defines the weather/climate transition.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Atmospheric science thesis on cascade structure of weather models and reanalyses; what we learn is about the atmosphere, not about research practice.
The thesis studies stochastic structures in atmospheric models and reanalyses, not research methodology.
Stochastic cascade analysis of weather models and reanalyses; atmospheric science, not study of research methods.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".