Statistical Downscaling of Daily Rainfall Processes for Climate-Related Impact Assessment Studies
Bibliographic record
Abstract
This study proposes a statistical downscaling (SD) method for describing accurately the linkage between large-scale climate predictors and observed rainfall characteristics at a local site. The proposed SD approach was based on a combination of a logistic regression model for representing the daily rainfall occurrences and a nonlinear regression model for describing the daily precipitation amounts. The feasibility of the suggested SD method was tested using the NCEP re-analysis data and the observed daily precipitation data available for the 1961–2001 period at two study sites located in completely two different climatic regions: the Seoul station in tropical-climate Korea and the Dorval Airport station in cold-climate Canada. It was found that it is feasible to link large-scale climate predictors given by GCM simulation outputs with daily precipitation characteristics at both stations. Furthermore, the proposed SD method could provide more accurate results than those given by the currently popular SDSM method.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".