Evaluation and Comparison of CanRCM4 and CRCM5 to Estimate Probable Maximum Precipitation over North America
Bibliographic record
Abstract
Abstract Recently dam managers have begun to use data produced by regional climate models to estimate how probable maximum precipitation (PMP) might evolve in the future. Before accomplishing such a task, it is essential to assess PMP estimates derived from regional climate models (RCMs). In the current study PMP over North America estimated from two Canadian RCMs, CanRCM4 and CRCM5, is compared with estimates derived from three reanalysis products: ERA-Interim, NARR, and CFSR. An additional hybrid dataset (MSWEP-ERA) produced by combining precipitation from the Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset and precipitable water (PW) from ERA-Interim is also considered to derive PMP estimates that can serve as a reference. A recently developed approach using a statistical bivariate extreme values distribution is used to provide a probabilistic description of the PMP estimates using the moisture maximization method. Such a probabilistic description naturally allows an assessment of PMP estimates that includes quantification of their uncertainty. While PMP estimates based on the two RCMs exhibit spatial patterns similar to those of MSWEP-ERA and the three sets of reanalyses on the continental scale over North America, CanRCM4 has a tendency for overestimation while CRCM5 has a tendency for modest underestimation. Generally, CRCM5 shows good agreement with ERA-Interim, while CanRCM4 is more comparable to CFSR. Overall, the good ability of the two RCMs to reproduce the major characteristics of the different components involved in the estimation of PMP suggests that they may be useful tools for PMP estimation that could serve as a basis for flood studies at the basin scale.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".