Comment on amt-2021-247
Bibliographic record
Abstract
In August 2018, the European Space Agency launched the Aeolus satellite, whose Atmospheric LAser Doppler INstrument (ALADIN) is the first spaceborne Doppler wind lidar to regularly measure vertical profiles of horizontal line-of-sight (HLOS) winds with global sampling. This mission is intended to assess improvement to numerical weather prediction provided by wind observations in regions poorly constrained by atmospheric mass, such as the tropics, but also, potentially, in polar regions such as the Arctic where direct wind observations are especially sparse. There remain gaps in the evaluation of the Aeolus products over the Arctic region, which is the focus of this contribution. Here, an assessment of the Aeolus Level-2B wind product is carried out from measurement stations in Canada’s north, to the pan-Arctic, with Aeolus data being compared to Ka-band radar measurements at Iqaluit, Nunavut; to radiosonde measurements over Northern Canada; to Environment and Climate Change Canada (ECCC)’s short-range forecast; and to the reanalysis product, ERA5, from the European Centre for Medium-Range Weather Forecasts (ECMWF). Periods covered include the early phase during the first laser nominal flight model (FM-A; 2018-09 to 2018-10), the early phase during the second flight laser (FM-B; 2019-08 to 2019-09), and the mid-FM-B periods (2019-12 to 2020-01). The adjusted r-square between Aeolus and other local datasets are around 0.9, except for somewhat lower values in comparison with the ground-based radar, presumably due to limited sampling. This consistency degraded by about 10 % for the Rayleigh winds in the summer, presumably due to scattering from the solar background. Over the pan-Arctic, consistency, with correlation greater than 0.8, is found in the Mie channel from the planetary boundary layer to the lower stratosphere (near surface to 16 km a.g.l.) and in the Rayleigh channel from the troposphere to the stratosphere (2 km to 25 km a.g.l.). Zonal and meridional projections of the HLOS winds are separated to account for the systematic changes in HLOS winds arising from sampling wind components from different viewing orientations in the ascending and descending phases. In all cases, Aeolus standard deviations are found to be 20 % greater than those from ECCC-B and ERA5. We found that L2B estimated error product for Aeolus is coherent with the differences between Aeolus and the other datasets, and can be used as a guide for expected consistency. Thus, our work confirms the quality of the Aeolus dataset over the Arctic and shows that the new Aeolus L2B wind product provides a valuable addition to current wind products in regions such as the Arctic Ocean region where few direct wind observations have been available to date.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.038 | 0.026 |
| Insufficient payload (model declined to judge) | 0.051 | 0.046 |
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".