Preliminary results of ultrasonic snow depth sensor testing for National Weather Service (NWS) snow measurements in the US
Bibliographic record
Abstract
Abstract During the 2006–2007 winter season, 17 sites across the US including Alaska tested an automated snow measurement system. This article aims to describe successes and failures of this system and provide insight into data collected this season. The system was designed in collaboration with both Environment Canada and Snow Sensor Study participants during the summer of 2006. This system included three Campbell Scientific SR‐50 sensors oriented 120° from one another and a temperature probe centred in the plot. Data collection efforts were successful with minimal amounts of data missing because of system or sensor failures. The system integrated automated retrieval of data from dataloggers, as well as automated file transfer protocol (FTP) to the study website for data archival and graphical display. Overall, the sensors and installation worked well with only a few problems noted. The sensors compared well with both manual observations taken adjacent to each sensor as well as traditional total snow depth (TSD) on ground measurements. The comparison to depths, taken adjacent to the sensors, allows for investigation of frost heave and indicates periods where the sensors were not functioning properly. The comparison to TSD on ground reveal problems with siting at some locations that are recommended to be remedied by re‐installation or re‐location of those sites prior to the 2007–2008 snow season. These results are preliminary and research will be ongoing for signal processing, snowfall algorithm development and optimal installation in preparation for the 2007–2008 snow season. This research has potential to return important snow observations to national weather service(NWS) observing networks that were discontinued when automation began as well as provide continuous snowpack monitoring to data users. Copyright © 2008 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".