Comparison of statistical methods for downscaling daily precipitation
Bibliographic record
Abstract
There are several statistical downscaling methods available for generating local-scale meteorological variables from large-scale model outputs. There is still no universal single method, or group of methods, that is clearly superior, particularly for downscaling daily precipitation. This paper compares different statistical methods for downscaling daily precipitation from numerical weather prediction model output. Three different methods are considered: (i) hybrids; (ii) neural networks; and (iii) nearest neighbor-based approaches. These methods are implemented in the Saguenay watershed in northeastern Canada. Suites of standard diagnostic measures are computed to evaluate and inter-compare the performances of the downscaling models. Although results of the downscaling experiment show mixed performances, clear patterns emerge with respect to the reproduction of variation in daily precipitation and skill values. Artificial neural network-logistic regression (ANN-Logst), partial least squares (PLS) regression and recurrent multilayer perceptron (RMLP) models yield greater skill values, and conditional resampling method (SDSM) and K-nearest neighbor (KNN)-based models show the potential to capture the variability in daily precipitation.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".