Impact of the Arctic observing systems on the <scp>ECCC</scp> global weather forecasts
Bibliographic record
Abstract
Abstract This study examines the impact of the conventional (in situ) and satellite observing systems in the Arctic on forecasts in the Northern Hemisphere polar and midlatitude regions using the Canadian global forecast system. It is part of an international cooperation to understand the role and the relative importance of the polar observing systems in numerical prediction systems in order to make recommendations for future optimal observing network design in this area. Both Observing System Experiment and Forecast Sensitivity to Observation Impact methods are used to assess the relative importance of the observing systems north of 60°N. The experiments are conducted during winter 2017–2018 and summer 2018, which include the first two Special Observing Periods of the Year of Polar Prediction. The impact of supplementary radiosondes launched during these periods is also assessed. It is found that the joint impact of all Arctic satellite data on forecasts is two to four times larger than the impact of all conventional data in this area. The microwave sounders and the radiosonde network have the largest impact among the satellite and convention observing systems, respectively. The impact of the conventional data is larger than that of microwave sounders in both winter and summer seasons. The impact of the supplementary radiosondes on short‐range forecast in the Arctic is positive, but becomes negligible beyond 24 hr forecast lead‐time. The impact of observations in the Arctic on medium‐range forecasts in the midlatitudes is larger over eastern North America and northern Asia for both seasons examined.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".