Arctic System Reanalysis improvements in topographically forced winds near Greenland
Bibliographic record
Abstract
Southern Greenland is home to a number of weather systems characterized by high speed low‐level winds that are the result of topographic flow distortion. These systems include tip jets, barrier winds and katabatic flows. Global atmospheric reanalyses have proven to be important tools in furthering our understanding of these systems and their role in the climate system. However, there is evidence that their mesoscale structure may be poorly resolved in these global products. Here output from the regional Arctic System Reanalysis (ASRv1–30 km and ASRv2–15 km grid resolutions) are compared to the global ERA‐Interim Reanalysis (ERA‐I–80 km grid resolution), focusing on their ability to represent winds in the vicinity of southern Greenland. Comparisons are made to observations from surface and upper‐air stations, as well as from research aircraft flights during the Greenland Flow Distortion Experiment (GFDex). The ERA‐I reanalysis has a tendency to underestimate high wind speeds and overestimate low wind speeds, which is reduced in ASRv1 and nearly eliminated in ASRv2. In addition, there is generally a systematic reduction in the root‐mean‐square error between the observed and the reanalysis wind speeds from ERA‐I to ASRv1 to ASRv2, the exception being low‐level marine winds where the correspondence is similar in all reanalyses. Case‐studies reveal that mesoscale spatial features of the wind field are better captured in ASRv2 as compared to the ERA‐I or ASRv1. These results confirm that a horizontal grid size on the order of 15 km is needed to characterize the impact that Greenland's topography has on the regional wind field and climate. However even at this resolution, there are still features of the wind field that are under‐resolved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".