The Measurement Factors in Estimating Snowfall Derived from Snow Cover Surfaces Using Acoustic Snow Depth Sensors
Bibliographic record
Abstract
Abstract At three Canadian test locations during the cold seasons of 2006 and 2007/08, snowfall measurements are derived from changes in the total depth of snow on the ground using multiple Campbell Scientific, Inc., SR50 ultrasonic ranging sensors over very short (minute–hour) time scales. Data analysis reveals that, because of the interplay of numerous essential factors that influence snow cover levels, the measurements exhibit a strong dependence on the time interval between consecutive measurements used to generate the snowfall value. This finding brings into question the reasonable accuracy of snowfall measurements that are derived from the snow cover surface using automated methods over very short time scales. In this study, two mathematical methods are developed to assist in quantifying the magnitude of the snowfall measurement error. From time-series analysis, the suggested characteristics of the snowdrift signal in the snow depth time series is shown by using measurements taken by FlowCapt Snowdrift acoustic sensors. Furthermore, the use of three collocated SR50s shows that repeated snow depth measurements represent three pairwise essentially different time series. These results question the reasonable accuracy of snowfall measurements derived using only a single ultrasonic ranging sensor, especially in cases in which the snow cover is redistributed by the wind and in which snow depth spatial variability is prominent.
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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 | 0.005 |
| 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 | 0.001 | 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".