Case studies characterizing fine-scale flow fields prior to precipitation events in the Canadian rockies using Doppler lidars
Bibliographic record
Abstract
Orography can disturb atmospheric flow fields by affecting the flows's interactions with precipitating particles. Turbulence influences localized flow patterns and impacts the meteorological conditions at the surface and aloft. This study aims to characterize the origin of turbulence prior to and during precipitation events. Automatic measurements were collected at two different elevations in the south-eastern Canadian Rockies: Fortress Junction Station (FJS) in the valley (1591 m MSL) and Fortress Powerline Station (FPS) at a higher elevation (2076 m MSL). Doppler lidars collected measurements for high-precision (spatial and temporal) atmospheric motion, three-dimensional wind fields, planetary boundary layer (PBL) properties, cloud, and precipitation layers. The turbulence origin and intensity of the fine-scale flow were characterized. We conducted an in-depth investigation of one event associated with heavy precipitation and compared the data from that event with a clear-sky reference day. Data reveals higher wind variability and turbulence at the high elevation site (FPS), with a thicker PBL and more solid hydrometeor compared to the lower elevation site (FJS). The turbulence originated either from surface heating, cooling from aloft, or hydrometeor phase change depending on the time of day, altitude, and atmospheric conditions. Overall, this study provides new experimental observations of the interactions between fine-scale flow fields, small scale turbulence processes, and precipitation in the Canadian Rockies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".