A synoptic climatology of potential seiche‐inducing winds in a large intermontane lake: Quesnel Lake, British Columbia, Canada
Bibliographic record
Abstract
Abstract Excitation of basin‐scale, internal waves (i.e., internal seiches) in lakes require spatially homogeneous wind fields that vary on time scales comparable to the seiche period, which can be on the order of several days in large lakes. We evaluate 2 years (October 1, 2016 to September 30, 2018) of 15‐min wind data from a shore‐based meteorological station to identify strong wind episodes likely to excite internal seiches in a large and geometrically complex lake surrounded by convoluted topography, Quesnel Lake, British Columbia, Canada. Our findings include the identification of strong wind event seasonality, with peak mean monthly wind speeds in April and November, and minimum mean monthly wind speeds in August. Using geopotential heights (GPHs) from 1.5° × 1.5° gridded reanalysis data to reconstruct the atmospheric state for each strong wind episode, two primary synoptic patterns are identified that, in conjunction with local topographic channelling, lead to either easterlies or westerlies occurring at our sampling station, with strong easterly episodes three times more frequent than westerly episodes. This highlights the important role that developing low‐pressure systems in the Northeastern Pacific Basin have in setting up the GPH gradient required for persistent strong winds at Quesnel Lake, in the hours and days before these storms make landfall. The projection of synoptic patterns of strong wind events onto a 4 × 3 self‐organizing map clustered strong wind events by mean wind direction, similar to the results of a manual classification. Methods to identify the strong wind episodes and the resulting self‐organizing map are both evaluated in‐part by two case studies where strong winds are known to have excited a basin‐scale baroclinic response in Quesnel Lake.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".