The Northern Mesonet Project: Creation of an interconnected network of surface weather station networks in Canada
Bibliographic record
Abstract
Surface weather stations are a critical technology for understanding severe convective storms due to their unique capabilities. While weather radar effectively detects precipitation and wind patterns aloft, its limitation is its inability to capture crucial surface-level weather where it matters the most for people and property. Documenting and analyzing such events offer invaluable insights into their causes, impacts, and potential future occurrences.Existing surface weather stations in Canada frequently face several known limitations that impede their effectiveness, especially when it comes to severe convective storms. In general, Canadian surface weather stations are widely spaced which often fail to capture highly localized severe convective storms. Additionally, the operation of these stations is managed by different federal and provincial agencies, which makes it difficult to utilize existing surface weather stations for nowcasting severe convective storms. Furthermore, it poses challenges in collecting data after an event, as the ease of access to this data can vary greatly across these different agencies.This is the reason for the creation of Northern Mesonet Project (NMP), a new program under the Canadian Severe Storms Laboratory, which aims to better monitor severe convective storms by increasing the spatial density of real-time advanced weather observations and enhancing data availability & quality for severe weather analysis and prediction. This presentation will specifically highlight the Canadian Mesonet Portal, a central repository and access point established by NMP to address some of the limitations faced by Canadian surface weather stations. By connecting over 30 individual surface weather station networks, the Canadian Mesonet Portal provides a unified platform for accessing over 2800 publicly available surface weather observations across Canada. This unified platform allows for better nowcasting of severe convective storms in Canada, and for better analysis of the damage caused by severe convective storms.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".