The Tahtsa Ranges Atmospheric River Experiment (TRARE): experimental design and case studies
Bibliographic record
Abstract
In September and October 2021, the Tahtsa Ranges Atmospheric River Experiment (TRARE) was held in western Canada to collect detailed hydrometeorological data on atmospheric rivers and other mid-latitude storms impacting British Columbia's upper Nechako Watershed and surrounding regions. A total of 11 precipitation events including six atmospheric rivers yielded a cumulative precipitation total of 250 mm at Huckleberry Mine, our primary field site. This paper summarizes the TRARE experimental setup that included six principal field sites including Huckleberry Mine along with nine secondary ones where high-frequency (up to the minute-scale) hydrometeorological data were collected. This included an array of four micro rain radars, four optical disdrometers, four meteorological stations, a hotplate precipitation gauge, a weighing precipitation gauge, and a network of tipping bucket rain gauges plus water measurements including levels, discharge and temperatures for two alpine creeks and water levels for one lake. Additional measurements of vertical atmospheric profiles from radiosondes supplemented by in-situ visual observations at two sites provide a comprehensive database to characterize storm evolution and precipitation distribution in the area. The paper highlights sample data from two case studies including an intense atmospheric river that made landfall near the study area. The TRARE field campaign's accomplishments, challenges and lessons learned are then discussed. Furthermore, we report on the learning outcomes, outreach activities and communication strategy from TRARE. The paper closes with the next steps for atmospheric river monitoring and research in north-central British Columbia.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".