Impact study with observations assimilated over North America and the North Pacific Ocean on the MSC global forecast system. Part I: Contribution of radiosonde, aircraft and satellite data
Bibliographic record
Abstract
A series of observing system experiments for the two‐month period January–February 2007 was carried out to assess the impact of radiosonde and aircraft data available over North America, as well as the impact of satellite data available over the North Pacific Ocean, on the global data assimilation and forecast system of the Meteorological Service of Canada (MSC). The impact is estimated by comparing two data assimilation and forecast cycles: one (control) assimilates all the observations operationally available while the other (experimental) is identical to the control except that the data from the observing network being examined is denied. In the first part of this study, presented in this paper, particular attention is given to the propagation and magnitude of the impact of the data from these observing networks. It is found that the impact on the accuracy of forecasts over the North American continent is not uniform. Radiosonde and aircraft data combined are the main contributors to the skill of short‐range forecasts over North America. However, as the effect of satellite observations over the North Pacific Ocean moves downstream over the continent, their impact on forecasts becomes dominant for forecast lengths longer than 36 h over western North America, and longer than 72 h over the eastern part of the continent. The impact of these satellite observations is more important over the continental United States than over Canada. In data‐denial experiments, the impact of the aircraft and radiosonde observing networks collocated over southern Canada and the United States, used individually, is much weaker than their combined impact. For short‐range forecasts, the effect of aircraft observations is more important than radiosonde data over eastern North America. Finally, the quality of the forecasts over the Canadian Arctic relies heavily on the radiosonde network.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".