Performance of Emerging Technologies for Measuring Solid and Liquid Precipitation in Cold Climate as Compared to the Traditional Manual Gauges
Bibliographic record
Abstract
Abstract Precipitation amount, type, and snow depth ) have been analyzed using data collected during the 4Wing Cold Lake Research Project in northeastern Alberta, Canada. The instruments used include the Vaisala present weather detector PWD22 and present weather sensor (FS)11P, the OTT Pluvio2 automatic catchment-type gauge, the manual standard Canadian Nipher (CN) and Type B rain gauges, and a snow ruler. Both the PWD22 and FS11P performed well at detecting snow, rain, and drizzle events as compared to the human observer. The sensors predicted a higher frequency of ice pellet cases than the human observer. Segregation of precipitation phase using temperature alone appeared unrealistic at near-freezing temperatures. All the sensors agreed well at measuring liquid precipitation, but the Pluvio2 gauge with a single Alter shield underestimated the snowfall amount by 40%, mostly due to wind effects. After correcting the CN gauge catch efficiency (CE) due to wind effects, the CE of the Pluvio2 relative to the CN gauge was found dependent on wind speed (ws). Using these data, a new transfer function (TF) for the Pluvio2 as a function of ws has been developed. The new TF was used to correct the Pluvio2 gauge, and the corrected data agreed well with the PWD22 measurements. Using the and corrected CN data, snow density ratios ( were derived, varying from 4.2 to 35 with a mean value of 12.2. The mean value derived in this study is higher than the 10:1 ratio usually assumed for converting to snow water equivalent in Canada. On average increases with increasing temperature and the 10:1 ratio appears to be more appropriate for warmer temperatures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".