Scale‐dependent impact of <i>Aeolus</i> winds on a global forecast system
Bibliographic record
Abstract
Abstract The European Space Agency's Aeolus mission, launched in August 2018, provides the first global horizontal line‐of‐sight wind profile measurements. Previous studies have shown that Aeolus winds in global forecast systems improve the overall forecast skill, especially in the upper tropospheric tropics and in other data‐sparse regions. In this study, we use a series of observing system experiments with the latest version of the reprocessed Aeolus wind product (2B11) to better characterize the locations and drivers of improved skill from Aeolus with Environment and Climate Change Canada's Global Deterministic Prediction System. Observing system experiments that test the impact of Aeolus winds and the impact of all operational wind observations are carried out, covering the period summer 2019 and winter 2019–2020. Assimilation of operational winds improves the tropospheric wind forecast over the Tropics by a reduction of 8% in the forecast error, and adding the Aeolus winds to the assimilations results in an extra 0.7–0.9%. Aeolus wind impacts are improvements are 0.7–0.9% for the Arctic, and 0.4–0.6% over the Northern and Southern Hemisphere extratropics. The scale dependence of these impacts is investigated using spatial spectra (spherical harmonic decomposition). The improvement is quantified using the difference of the 250 hPa kinetic energy forecast error spectra between experiments. The operational winds largely improve the forecast of planetary scale to intermediate scale for spherical wave numbers between 1 and 20 in the short‐range forecasts. The operational wind impact decreases as the forecast lead time increases. On the other hand, the impact of Aeolus is mostly seen in the intermediate to large scale range with a peak around spherical wave number 8. The Aeolus‐related improvement around this wave number increases with forecast lead time. This analysis suggests that Aeolus winds provide estimates of the wind state that are valuable and complementary to that provided from current operational winds.
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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 | 0.003 |
| 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 | 0.000 |
| 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".