Forecasting the Inland Extent of Lake Effect Snow Bands Downwind of Lake Ontario
Bibliographic record
Abstract
Determining the inland extent (IE) of lake effect snow (LES) is an ongoing operational forecasting challenge at the Albany and Binghamton National Weather Service (NWS) forecast offices, and several other NWS forecast offices in the Great Lakes region.Assuming favorable conditions for development of LES, determining how far inland snow bands will extend is critical to forecasters making decisions supporting the NWS watch/ warning/advisory program and resulting impact-based decision support services.This research sought to identify which atmospheric parameters commonly have the greatest influence on how far inland LES bands travel, and to develop forecasting techniques to assist meteorologists.Single band LES events for the 2006-2009 winter seasons were examined downwind of Lake Ontario.The IE of LES bands was measured over the duration of each event and broken into quartiles.The quartiles were used to create categories for IE (short, moderate, and long).Several parameters were analyzed, using statistical correlations at data points within, and just outside of, LES bands.Box-and-whiskers plots were constructed for individual parameters relative to each IE category.The most strongly correlated parameters to IE included existence of a multi-lake/upstream moisture source connection (MLC), mixed-layer (ML) stability (represented by lake-air temperature differentials), 0-1-km bulk shear, and mean ML wind speed.LES bands featuring an MLC showed a greater tendency to progress farther inland, compared to those without.A predictive equation for forecasting IE of LES downwind of Lake Ontario was developed from a statistical model using a stepwise and backwards selection algorithm.A crossvalidation method was used to determine skill.
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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 | 0.000 | 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".