Developing a Predictive Department of Transportation Winter Severity Index
Bibliographic record
Abstract
Abstract Quantification and prediction of winter storm impacts, and their severity, are important for transportation agencies. The Nebraska Winter Severity Index (NEWINS) used by the Nebraska Department of Transportation provides an independent framework to determine the severity of a winter season through the categorization of individual winter storms. However, a limitation of NEWINS is that it is not predictive for individual winter storms. This study transitions the NEWINS framework from a poststorm retrospective tool to a predictive one, referred to as NEWINS-Predictive (NEWINS-P), using forecasts from the National Digital Forecast Database. The NEWINS-P framework includes five components: snow severity (NEWINS-S), precipitation type, icing, blowing snow, and drifting snow. The components aim to forecast different in-storm and poststorm winter weather hazards over a 72-h duration at a 6-h resolution. The NEWINS-P framework is assessed through spatial forecasts across Nebraska and temporal forecasts at select locations on select Colorado low and Alberta clipper systems from the 2018–19, 2020–21, and 2022–23 winter seasons. The NEWINS-S component is further investigated through assessing forecast trends and system severity. Observational data from multiple sources verify temporal forecasts and system severity. The case study results show that Colorado low systems produce a larger spatial coverage, intensity, and longevity of winter weather hazards than Alberta clipper systems. The NEWINS-P demonstrates reasonable forecasting skills and can be a useful tool to support transportation agencies in their winter maintenance operations for personnel and resource planning in advance of winter storms.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".