Synoptic Patterns Associated with Northeast and Southeast Ice Storms
Bibliographic record
Abstract
Wintertime storms that produce precipitation events such as snow, freezing rain, and ice pellets cause significant damage to utility services and disrupt travel. These synoptic systems involve deep isothermal regions where warm, moist air over-runs surface sub-freezing air. Much attention has been focused on Northeast ice storms, where a study by Cortinas et al. (2004) identified the Northeast as the region with the highest spatial distribution of freezing rain and ice pellets. Castellano (2012) also identified 2 types events where ice storms occur in the Northeast as a result of cold-air intrusions from Canada and from an absence of cold air. However, little else is known about the synoptic evolution of the storms. Therefore this study analyzes the dynamic and thermodynamic conditions of ice events along the east coast. The National Climatic Data Center (NCDC) Storm Events Database is used to pull the dates of ice storms from the Northeast and Southeast climate regions for 1996-2013. The spatial coverage of each ice storm is computed from county size data. A separation technique is then used to isolate larger storms from smaller storms. In addition, a grouping method is applied to objectively identify cyclone tracks. Next, we analyze the synoptic control of ice storms from both regions in an effort to identify difference between Northeast and Southeast cases. For the ice storms gathered from the Storm Events Database, composites are generated for sea level pressure, 2-meter temperatures, 2-meter temperature anomalies, and 850-500 hPa dθ/dz from reanalysis data. Separately, MODIS retrievals of optical thickness, cloud-top pressure and temperature are analyzed to understand the cloud characteristics of the ice storms. A comparison of the composites for the Southeast and Northeast storms suggests that the size differences relate in part to the synoptic structure, where 3 Northeast storms typically occur as a result of warm frontal features or the occlusion phase of a cyclone. For the Southeast, most storms occur as a result of frontal features. Finally, case studies are analyzed for a Northeast and Southeast storm that occurred on December 11, 2008 and January 26, 2004 respectively. For these events vertical cross-sections of temperature and zonal wind amplitude are analyzed to understand the thermodynamic conditions of ice storms. In addition, NCEP Stage IV radar data is utilized along with the concept of a partial thickness and elevated warm layer method as a precursor for predicting ice storms. For the Northeast case study the partial thickness method verifies. The vertical cross-sections not only confirm findings from Martner et al. (1993) that an elevated warm layer exists above a shallow subfreezing cold layer, but it verifies the elevated warm layer method for predicting freezing rain for the Southeast case-study.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".