Multisite Statistical Downscaling of Daily Precipitation Processes
Bibliographic record
Abstract
The main objective of the present study is to develop an efficient statistical downscaling (SD) approach for simulating simultaneously and concurrently daily precipitation series at many sites. The proposed approach consists of a combination of two distinct multiple regression models to represent the linkage between global climate predictors, the probability of local daily rainfall occurrences, and daily rainfall amounts, and the singular value decomposition (SVD) technique to represent the observed statistical properties of the stochastic component of the proposed combined model. The feasibility of the suggested multisite downscaling method was assessed using observed daily precipitation data available at 10 weather stations located in the southwest region of Quebec and southeast region of Ontario in Canada and the climate predictors estimated from the National Centre for Environmental Prediction (NCEP) re-analysis data set for the period from 1961 to 2000. It was found that the proposed SD approach was able to describe accurately various precipitation characteristics, including their spatial and temporal variations and their interannual anomalies. In particular, it has been shown that the proposed procedure was quite efficient in the simulation of daily precipitation series for many sites because of the effective computation of its SVD component.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".