A Statistical Approach to Multisite Downscaling of Daily Precipitation Processes in the Context of Climate Change
Bibliographic record
Abstract
The present study proposes a statistical downscaling (SD) approach to represent the linkages between global scale climate predictors and daily precipitation processes at many local sites concurrently. This SD method is based on a combination of two multiple regression models for describing precipitation occurrences and amounts and on the use of the Singular Value Decomposition technique for modelling the stochastic components of these two models. The feasibility of the proposed procedure was assessed using the NCEP/NCAR re-analysis data and the daily precipitation data from a network of 10 raingages located in the Quebec-Ontario region (Canada). Results of this application have indicated the ability of this SD approach to reproduce accurately the observed statistical properties of daily precipitations, and the temporal-spatial dependence and intermittency of the underlying precipitation processes. The proposed SD tool can be used for assessing the climate change impacts on daily precipitations at many different sites over an area of interest.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".