Climatology of cold season lake‐effect cloud bands for the North American Great Lakes
Bibliographic record
Abstract
ABSTRACT Geostationary Operational Environmental Satellite (GOES) visible imagery was used to identify lake‐effect (LE) clouds in the North American Great Lakes region for the cold seasons (October–March) of 1997/1998 through 2013/2014 to provide a comprehensive climatological description of the seasonal and interannual variability of LE cloud bands. During the average cold season, at least 60% of days each month had LE clouds over some portion of the Great Lakes region and nearly 75% of all LE days had LE clouds present over several lakes simultaneously. Wind‐parallel bands (WPB) are observed far more frequently than any other type of LE over Lakes Superior, Michigan, and Huron during the months of December, January, and February. Over Lake Erie, the occurrence of days per month with WPB was found to be approximately 5–10% greater than days with shore‐parallel bands (SPBs) throughout the entire cold season. The greatest frequency of SPB occurrences in the Great Lakes region was over Lake Ontario during the months of January and February (∼20% of days). In addition, Lake Ontario was the only lake where the frequencies of WPB and SPB occurrences were fairly similar each month. The annual frequency of WPB occurrences are the most variable among the Great Lakes, decreasing in frequency from the western lakes toward the eastern lakes. Lake Ontario has the largest annual frequency of SPB occurrences and the greatest variation in SPB annual frequency. Lake Huron has the second largest annual frequency of SPB days with small interannual variation. The primary differences of the annual frequency of lake‐to‐lake (L2L) LE occurrences when compared with previous research were a greater variability in the L2L annual frequency of Superior‐to‐Michigan connections, greater frequency of Michigan‐to‐Huron connections, and less frequent occurrences for Superior‐to‐Huron and Michigan‐to‐Erie connections.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".