Deriving meteorological variables from numerical weather prediction model output: A nearest neighbor approach
Bibliographic record
Abstract
This paper presents the application of variations in a nearest neighbor resampler approach for generating local‐scale meteorological variables from numerical weather prediction model output. On the basis of measure of closeness and sampling strategy, six nearest neighbor models were designed. The proposed models were applied to downscale station daily precipitation and minimum and maximum temperature fields for the Chute‐du‐Diable meteorological station in northeastern Canada. Suites of deterministic diagnostic measures were employed for evaluating individual models as well as for intercomparison among the downscaling models. On the basis of intercomparison among models a relatively better nearest neighbor resampler was identified and the subsequent model was further investigated with a focus on downscaling daily precipitation. Suites of conventional and distribution‐based diagnostic measures were employed for evaluating the skill of the downscaled precipitation over the raw numerical model output. The comparative results showed that the downscaled precipitation had greater skill values based on different performance measures which include median bias, Brier skill score, ranked probability skill score, discrimination, reliability, and relative operating characteristics.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".