Utilizing Normalized Anomalies to Assess Synoptic-Scale Weather Events in the Western United States
Bibliographic record
Abstract
Abstract Synoptic-scale weather events over the western United States are objectively ranked based on their associated tropospheric anomalies. Data from the NCEP 6-h reanalysis fields from 1948 to 2006 are compared to a 30-yr (1971–2000) reanalysis climatology. The relative rarity of an event is measured by the number of standard deviations that the 1000–200-hPa height, temperature, wind, and moisture fields depart from climatology. The top 20 synoptic-scale events were identified over the western United States, adjacent eastern Pacific Ocean, Mexico, and Canada. Events that composed the top 20 tended to be very anomalous in several, if not all four, of the atmospheric variables. The events included the northern Intermountain West region heavy rainfall and Yellowstone tornado of mid-July 1987 (ranked 5th), the Montana floods of September 1986 (ranked 4th), and the historic 1962 “Columbus Day” windstorm in the Pacific Northwest (ranked 10th). In addition, the top 10 most anomalous events were identified for each month and for each of the variables investigated revealing additional significant weather events. Finally, anomaly return periods were computed for each variable at a variety of levels. To place a given anomaly in perspective for a specific level or element, forecasters need information on the frequency with which that anomaly is observed. These return periods can be utilized by forecasters to compare forecast anomalies to the actual occurrence of similar anomalies for the element and level of interest to gauge the potential significance of the event. It is believed that this approach may allow forecasters to better understand the historical significance of an event and provide additional information to the user community.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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".