Evaluation of Catch Efficiency Transfer Functions for Unshielded and Single-Alter-Shielded Solid Precipitation Measurements
Bibliographic record
Abstract
Abstract Solid precipitation undercatch can reach 20%–70% depending on meteorological conditions, the precipitation gauge, and the wind shield used. Five catch efficiency transfer functions were selected from the literature to adjust undercatch from unshielded and single-Alter-shielded precipitation gauges for different accumulation periods. The parameters from these equations were calibrated using data from 11 sites with a WMO-approved reference measurement. This paper presents an evaluation of these transfer functions using data from the Neige site, which is located in the eastern Canadian boreal climate zone and was not used to derive any of the transfer functions available for evaluation. Solid precipitation measured at the Neige site was underestimated by 34% and 21% when compared with a manual reference precipitation measurement for unshielded and single-Alter-shielded gauges, respectively. Catch efficiency transfer functions were used to adjust these solid precipitation measurements, but all equations overestimated amounts of solid precipitation by 2%–26%. Five different statistics evaluated the accuracy of the adjustments and the variance of the results. Regardless of the adjustment applied, the catch efficiency for the unshielded gauge increased after the adjustment. However, this was not the case for the single-Alter-shielded gauges, for which the improvement of the results after applying the adjustments was not seen in all of the statistics tests. The results also showed that using calibrated parameters on datasets with similar site-specific characteristics, such as the mean wind speed during precipitation and the regional climate, could guide the choice of adjustment methods. These results highlight the complexity of solid precipitation adjustments.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".