The effects of the spatial scales in the initial conditions of numerical weather forecasts
Bibliographic record
Abstract
Despite numerous improvements in technology, it is still very difficult for numerical weather forecasting to predict accurately the evolution of meteorological systems.While much of the research on this problem is focused on improving the initial conditions (ICs) at the small scales for more accurate forecasts, several recent studies have shown that errors at the large scales can significantly reduce the predictability of the numerical weather prediction.This thesis investigates the influence of different spatial scales in the ICs on the evolution of two precipitation events.To achieve that, the potential vorticity (PV) field of the ICs is first filtered to remove a certain amount of the small scales.Then, the new filtered state variable fields, obtained by using the PV inversion technique, are used as the new ICs for the Weather and Research Forecasting model (WRF) to simulate the selected cases.The results show that the large scales are the main factors in the development and the motion of the meteorological systems under study.Also, the small scales are still reconstructed by the model despite their removal in the ICs.Therefore, these results suggest that obtaining accurate observation data spreading across a large domain would improve more the numerical forecast than reducing the errors of the observation on small local scales.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".