Ceilometer observations of Vancouver's urban boundary layer : validation and mixed-layer height estimation
Bibliographic record
Abstract
A mini-lidar system, Vaisala's CL31 ceilometer, was installed within a suburban area of Vancouver, BC, for the purpose of making continuous observations of the boundary layer over a period of 11 months. Initial validation of the ceilometer for use in boundary layer observations was undertaken. This involved the comparison of ceilometer data with eight months of ground-level particulate matter measurements, as well as with 16 vertical profiles of particulate matter and meteorological data. Once a variety of persistent noise structures within the data were accounted for, it was found that the ceilometer data showed good agreement with the particulate matter data, suggesting its usefulness for assessing air-quality throughout the bottom 1km of the atmosphere. Additionally, two algorithms were developed in order to estimate the height of the convective boundary layer, or the mixed-layer height, from the ceilometer data. One involved the fitting of an ideal-profile to the measured data, while the other involved the location of a minimum-gradient in the backscatter profile. The performance of these two techniques were assessed and compared, and it was found that the ideal-profile method was the more robust of the two. Finally, mixed-layer heights were estimated for fair weather, convectively active days. In order to isolate such conditions, an automatic flagging algorithm was developed. However, additional manual assessment was needed to avoided unsuitable conditions. Mixed-layer heights were estimated for 19 days over an 11 month period. the estimates presented here were found to agree with previous observations. Daily maximum mixed-layer heights ranged from 650m in July to 350m in December, indicating that the height of the convective boundary layer within Vancouver is significantly suppressed due to the city's coastal location.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".