Catch Characteristics of Precipitation Gauges in High-Latitude Regions with High Winds
Bibliographic record
Abstract
Abstract Intercomparison of solid precipitation measurement at Barrow, Alaska, has been carried out to examine the catch characteristics of various precipitation gauges in high-latitude regions with high winds and to evaluate the applicability of the WMO precipitation correction procedures. Five manual precipitation gauges (Canadian Nipher, Hellmann, Russian Tretyakov, U.S. 8-in., and Wyoming gauges) and a double fence intercomparison reference (DFIR) as an international reference standard have been installed. The data collected in the last three winters indicates that the amount of solid precipitation is characteristically low, and the zero-catch frequency of the nonshielded gauges is considerably high, 60%–80% of precipitation occurrences. The zero catch in high-latitude high-wind regions becomes a significant fraction of the total precipitation. At low wind speeds, the catch characteristics of the gauges are roughly similar to the DFIR, although it is noteworthy that the daily catch ratios decreased more rapidly with increasing wind speed compared to the WMO correction equations. The dependency of the daily catch ratios on air temperature was confirmed, and the rapid decrease in the daily catch ratios is due to small snow particles caused by the cold climate. The daily catch ratio of the Wyoming gauge clearly shows wind-induced losses. In addition, the daily catch ratios are considerably scattered under strong wind conditions due to the influence of blowing snow. This result suggests that it is not appropriate to extrapolate the WMO correction equations for the shielded gauges in high-latitude regions for high wind speed of over 6 m s−1.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".