Impact study with observations assimilated over North America and the North Pacific Ocean on the MSC global forecast system. Part ii: Sensitivity experiments
Bibliographic record
Abstract
A series of observing system experiments (OSEs) 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 systems of the Meteorological Service of Canada. This paper presents the second part of the study and examines the validity of the conclusions drawn with respect to the assimilation scheme used (i.e., the three‐ or four‐dimensional variational (3D‐Var or 4D‐Var) data assimilation scheme) and to the horizontal resolution of the forecast model selected. The effect of the weather regime that prevailed during the evaluation period is also investigated. When radiosonde data are denied over North America, as well as over the globe, the forecast impact over the Canadian Arctic is larger using the 3D‐Var scheme than the 4D‐Var scheme. This indicates that the 4D‐Var scheme is more effective at extracting information from other types of observations from nearby regions. For short‐range forecasts, the results are closer to each other when changing the horizontal resolution of the forecast model than they are when changing the assimilation scheme. In order to assess the effect of the weather regime, the individual results for January are compared with those for February. This is possible because the large‐scale circulations during these two one‐month periods are significantly different. It is found that the large‐scale flow has a significant effect on the propagation of the impacts of data denial, leading to noticeable variations of their magnitudes over areas located downstream.
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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 | 0.006 | 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".