DEVELOPMENT AND ANALYSIS OF RADAR BASED THUNDERSTORM CLIMATOLOGY FOR NORTH DAKOTA
Bibliographic record
Abstract
The usage of radar data in the development of thunderstorm climatologies was investigated. Thunderstorm data for three WSR-88D radar and eight surface stations in North Dakota in the USA for 2002-2006 were analyzed in order to develop a reliable database for the thunderstorm cells in the state. The analysis results obtained from radar data matched with that obtained from the surface data and also with the results obtained by previous researchers. It was found that Jime and July are the peak months and late- afternoon to early-morning is the peak time for thunderstorms. Each year, there are 19 to 35 thunderstorm-days at a particular place in North Dakota and 9 to 14 thunderstorm-days with peak wind reports with an overall average peak wind speed of 59.4 km/hr all over the state. The presence of the Missouri river and Lake Sakakawea leads to the presence of a high thunderstorm-initiation-frequency belt in the mid-western part of the state. The life cycle analysis for individual thunderstorm cells in North Dakota was also done and it was found that the average lifetime of thunderstorm cells is 23.6 minutes, the average tracklength is 21.8 km and the average forward speed is 59.0 km/hr and the average heading varies with month from north-west to north, north-east and east directions. The distribution patterns and the life cycle characteristics of thunderstorm cells obtained in this research can further be used to develop a parametric risk model for North Dakota and southern Manitoba.
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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".