The Canadian Surface Prediction Archive (CaSPAr): A Platform to Enhance Environmental Modeling in Canada and Globally
Bibliographic record
Abstract
Abstract The Canadian Surface Prediction Archive (CaSPAr) is an archive of numerical weather predictions issued by Environment and Climate Change Canada. Among the products archived on a daily basis are five operational numerical weather forecasts, three operational analyses, and one reanalysis product. The products have hourly to daily temporal resolution and 2.5–50-km spatial resolution. To date the archive contains 394 TB of data while 368 GB of new data are added every night. The data are archived in CF-1.6-compliant netCDF-4 format. The archive is available online ( https://caspar-data.ca ) since June 2017 and allows users to precisely request data according to their needs, that is, spatial cropping based on a standard shape or uploaded shapefile of the domain of interest and selection of forecast horizons, variables, and issue dates. The degree of customization in CaSPAr is a unique feature relative to other publicly accessible numerical weather prediction archives and it minimizes user download requirements and local processing time. We benchmark the processing time and required storage of such requests based on 216 test scenarios. We also demonstrate how CaSPAr data can be employed to analyze extreme rainfall events. CaSPAr provides access to data that are fundamental for evaluating numerical weather prediction models and demonstrating the improvement in products such as flood and energy demand forecasting systems.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.011 |
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".