Diurnal Climatology of the Boundary Layer in Southern California Using AMDAR Temperature and Wind Profiles
Bibliographic record
Abstract
Abstract Observations from commercial aircraft [e.g., the Aircraft Meteorological Data Relay (AMDAR) automated weather reports] have been increasing dramatically. Two main applications of the aircraft data are use in short-term forecasts and assimilation into numerical weather prediction models. Now that more than 10 years of measurements exist, using this dataset to construct a description of the long-term climatological behavior (a “climatology”) of the lower atmosphere is explored with two main objectives. The first objective is to examine strengths and weaknesses of using the dataset to construct a climatology of the lower atmosphere. Unlike the traditional twice-daily radiosonde launches, the high frequency of observations at major airports allows for an unprecedented set of diurnal information at many locations globally. The second objective is to obtain a climatology of the lower atmosphere of Southern California, specifically at Los Angeles, San Diego, and Ontario, during the spring and summer when the boundary layer is well defined and easily detected. The June 2001–14 climatology reveals that the deepening of the boundary layer overnight is consistent with a cloud-topped boundary layer. Whereas the average boundary layer height decreases right after sunrise at San Diego, at Los Angeles the deeper boundary layer persists about 4 h after sunrise and then decreases rapidly over 2 h as the onshore sea breeze strengthens. Morning intrusions of the marine air inland are easily detected at Ontario in some months but are practically nonexistent during July and August.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".