Evaluation of reanalysis precipitation estimates in the Canadian precipitation analysis (CaPA)
Bibliographic record
Abstract
Canadian Precipitation Analysis (CaPA) has been developed by Environment Canada to produce the most accurate near-real-time gridded precipitation estimates. It uses the Global Environmental Multiscale model (GEM) as a background and assimilates the synoptic network of weather stations through Optimal Interpolation. Accurate estimation of gridded precipitation is useful for hydrological modeling, stream ow forecasting, and climate change studies. However, the calibration and validation of hydrologic models requires long temporal coverage of data for a better performance. Since GEM/CaPA data are available only for the recent past (2002-present), the development of historical data sets starting earlier than 2002 becomes important. Using alternative models for producing the atmospheric gridded background is one solution to overcome the short temporal coverage of archived GEM data. This thesis evaluates and analyzes two candidate data sets. ERA-Interim and NARR were selected as potential alternatives to GEM background. The general conclusion of the study is that the use of ERA-Interim and NARR as background elds leads to performance results that are not signi cantly inferior to GEM after assimilation with stations in the CaPA framework. While result with the GEM background remains the best, one can cautiously conclude that for most practical applications, ERA-Interim and/or NARR may be used for the period that predates archived GEM data. The thesis presents a more detailed evaluation of ERA-Interim and NARR for di erent seasons and di erent regions of Canada.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".