Aeolus winds improve Arctic weather prediction
Bibliographic record
Abstract
It has been proven that assimilating winds from the Aeolus global Doppler wind lidar would enhance the predictive skill of weather forecast models. In this study, we use a series of Observing System Experiments to examine how operational winds and Aeolus winds impact Environment and Climate Change Canada’s global forecast system over the data-sparse Arctic region. Aeolus winds improve the tropospheric wind and temperature forecasts by about 0.7 to 0.9% of error reduction (a 15-20% effect compared to the impact of operational wind products), while having little impact on the specific humidity field. In particular, Aeolus winds have an impact on forecasts of strong wind days on the wind and temperature fields that is double the impact of the forecasts of less intense wind days and provides a disproportionate improvement to forecasts on these days compared to other operational wind measurements. These findings suggest significant potential for global doppler wind lidar observations to enhance severe-weather prediction in polar regions.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".