Impact of the Aeolus <scp>Level‐2B horizontal line‐of‐sight</scp> winds in the <scp>Environment and Climate Change Canada</scp> global forecast system
Bibliographic record
Abstract
Abstract The European Space Agency Aeolus mission was launched in August 2018. This satellite carries the first Doppler lidar able to provide global measurements of wind profiles. Aeolus Level‐2B products have been generated and monitored by the European Centre for Medium‐Range Weather Forecasts (ECMWF) in near real‐time since a few weeks after the launch. These products include the horizontal line‐of‐sight (HLOS) winds that are suitable for data assimilation in numerical weather prediction systems. This article presents a series of observing system experiments conducted over summer 2019 to assess the value of the Level‐2B HLOS winds and their impact on the Environment and Climate Change Canada global forecasts. The impact of atmospheric motion vectors (AMVs) on forecasts is also examined and compared with the impact of HLOS winds. Two datasets are used: the HLOS winds produced in near real‐time at ECMWF and those reprocessed later in fall 2020. It is found that the near real‐time data are significantly biased and should be corrected. A look‐up table bias correction based on observation minus background departures is applied to this dataset as initially proposed by ECMWF. The reprocessed data are of better quality and bias corrected using the telescope's primary mirror temperature variations as predictor. The impacts of the near real‐time and reprocessed HLOS winds on forecasts are generally positive for both temperature and wind. The impacts are largest in the troposphere over the Tropics and polar regions. The positive impacts on forecasts are larger with the reprocessed data, particularly in the stratosphere, where a significant degradation over the Southern Hemisphere is found from assimilating the near real‐time data. The normalized forecast error reductions at days 1 and 2 for the wind are ∼1.25% over the Tropics and Southern Hemisphere. The positive impact of the HLOS winds on forecasts is enhanced by ∼40% when the AMVs are not assimilated in the control experiment. The forecast error reduction from assimilating AMVs is, however, two times larger than from assimilating HLOS winds in the extratropics. Conversely, the impact of HLOS winds on forecasts is generally larger in the Tropics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".