A comparison of the regional Arctic System Reanalysis and the global ERA‐Interim Reanalysis for the Arctic
Bibliographic record
Abstract
The Arctic System Reanalysis version 1 (ASRv1), a high‐resolution regional assimilation of model output, observations and satellite data across the mid‐ and high latitudes of the Northern Hemisphere, and the global European Centre for Medium Range Forecasting Interim Reanalysis (ERAI) are compared with atmospheric observations for the period December 2006 to November 2007. Results throughout the troposphere show observations to be well assimilated in the ASRv1, as monthly and annual near‐surface (upper‐level) temperature, dew‐point (relative humidity), pressure (geopotential height) and wind‐speed biases compared with surface stations and radiosondes are very small. These results are similar to the ERAI, although wind‐speed biases are significantly smaller in the ASRv1. Despite the ASRv1's use of a 3D‐variational (Var) assimilation compared with the ERAI's 4D‐Var, similar results suggest that a regional approach with higher‐resolution terrain and a detailed land‐surface description forced by a global reanalysis may improve the assimilation of observations and help offset temporal information lost by the 3D‐Var compared with the 4D‐Var. However, the ASRv1 forecast field results compared with the ERAI are mixed. The ASRv1 and ERAI show negative precipitation biases during cool months compared with gauge observations, and too much precipitation falls in the ASRv1 during summer in the midlatitudes. Stations north of 60°N demonstrate smaller precipitation biases in the ASRv1 than the ERAI except during the summer, when the ASRv1 is very dry. Short‐wave radiation compared with observations is much too large in the ASRv1, and both reanalyses show long‐wave radiation deficits during most months. These results point to inadequacies in model physics in the ASRv1 (e.g. convective and radiation schemes) that will continue to be refined in subsequent versions of the ASR.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".