Downscaling and Projection of Winter Extreme Daily Precipitation over North America
Bibliographic record
Abstract
Abstract Large-scale atmospheric variables have been statistically downscaled to derive winter (December–March) maximum daily precipitation at stations over North America using the generalized extreme value distribution (GEV). Here, the leading principal components of the sea level pressure field and local specific humidity are covariates of the distribution parameters. The GEV parameters are estimated using data from 1949 to 1999 and the r-largest method. This statistical downscaling procedure is found to yield skill over the southern and northern West Coast, central United States, and areas of western and eastern Canada when tested with independent data. The projected changes in covariates or predictors are obtained from transient climate change simulations conducted with the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled General Circulation Model, version 3.1 (CGCM3.1) forced by the Intergovernmental Panel on Climate Change (IPCC) A2 forcing scenario. They are then used to derive the GEV distribution parameters for the period 2050–99. The projected frequency of the current 20-yr return maximum daily precipitation for that period suggests that extreme precipitation risk will increase heavily over the south and central United States but decrease over the Canadian prairies. The difference between the statistical downscaling results and those estimated using GCM simulation is also discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".