The Frequency and Characteristics of Lake-Effect Precipitation Events Associated with the New York State Finger Lakes
Bibliographic record
Abstract
Abstract This study presents a climatological analysis of the frequency and characteristics of lake-effect precipitation events that were initiated or enhanced by lakes within the New York State (NYS) Finger Lakes region for the 11 winters (October–March) from 1995/96 through 2005/06. Weather Surveillance Radar-1988 Doppler (WSR-88D) data from Binghamton, New York, were used to identify 125 lake-effect events. Events occurred as 1) a well-defined, isolated precipitation band over and downwind of a lake, 2) an enhancement of mesoscale lake-effect precipitation originating from Lake Ontario and extending southward over an individual Finger Lake, 3) a quasi-stationary mesoscale precipitation band positioned over a lake embedded within extensive regional precipitation from a synoptic weather system, or 4) a transition from one type to another. Results show that lake-effect precipitation routinely develops over lakes that are considerably smaller than lakes previously discussed as being associated with lake-effect precipitation, such as the Great Lakes. Lake-effect events occurred during each month (October–March) across the 11 winters studied and were identified in association with each of the six easternmost Finger Lakes examined in this study. The frequency of NYS Finger Lakes lake-effect events determined in the current investigation paired with subsequent analyses of the environmental conditions leading to these events will allow for 1) comparative analyses of necessary conditions for lake-effect development across a range of lake sizes (e.g., NYS Finger Lakes, Lake Champlain, Great Salt Lake, and Great Lakes) and 2) an informative examination of the connection between mesoscale processes and climate variability.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".