Lake-Aggregate Mesoscale Disturbances. Part V: Impacts on Lake-Effect Precipitation
Bibliographic record
Abstract
It is known that lake-effect snowstorms in the Great Lakes region depend on the synoptic-scale flow conditions. These conditions are determined in part by the synoptic-scale features that traverse the area. Forecasting the development of these storms has improved dramatically in the last decade. A remaining complicating aspect, however, is that the heating and moistening from all the Great Lakes (e.g., the aggregate) affects the large-scale winds, temperature, moisture, and stability near the individual lakes, which in turn affect the characteristics of the lake-effect storms that develop. The effects of the Great Lakes aggregate on lake-effect precipitation is examined for a particular case in November 1982 that was characterized by a cold air outbreak followed by the approach of a weak trough into the region. Existing model output from numerical simulations that 1) included all of the lakes and that 2) excluded all of the lakes is used in conjunction with output from two additional numerical simulations that were performed and that include 3) only Lake Michigan and 4) only Lakes Erie and Ontario. The intercomparison of output from these simulations indicates that the lake aggregate enhanced lake-effect precipitation in northern lower Michigan and in southern Ontario but diminished lake-effect precipitation in regions south and east of Lakes Erie and Ontario. The effects were the result of combined changes in wind, temperature, moisture, and stability, which likely altered the morphology, intensity, locations, and orientations of the convective bands. These results indicate that understanding more completely how and when lake aggregate-scale circulations develop can enhance 1–2-day lake-effect and regional-scale precipitation forecasts. More importantly, these results suggest that aggregate-scale circulations that develop from clusters of heat sources or sinks can impact significantly the local precipitation distribution adjacent to a particular heat source or sink.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".