Ten Years of Science Based on the Canadian Precipitation Analysis: A CaPA System Overview and Literature Review
Bibliographic record
Abstract
Near real-time quantitative precipitation estimates are required for many applications including weather forecasting, flood forecasting, crop management, forest fire prevention, hydropower production, and dam safety. Since April 2011, such a product has been available from Environment and Climate Change Canada for a domain covering all North America. This product, known as the Regional Deterministic Precipitation Analysis, is generated using the Canadian Precipitation Analysis (CaPA) system. Although it was designed for near real-time use, an archive of pre-operational and operational products going back to 2002 is now available and has been used in numerous studies. This paper presents a review of the various scientific publications that have reported either using or evaluating CaPA products. We find that the product is used with success both for scientific studies and operational applications and compares well with other precipitation datasets. We summarize the strengths and weaknesses of the system as reported in the literature. We also provide users with information on how the system works, how it has changed over time, and how the archived and near real-time analyses can be accessed and used. We finally briefly report on recent and upcoming improvements to the product based, in part, on the results of this literature review.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.044 | 0.073 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".