Convective environments leading to microburst, macroburst and downburst events across the United States
Bibliographic record
Abstract
Downbursts are strong downdrafts of negatively buoyant air associated with convective storms and are capable of producing severe near-surface winds. Microbursts and macrobursts are subcategories of downbursts with the horizontal extent of damaging winds smaller or larger than 4 km, respectively. From January 2000 to June 2020, the Severe Weather Event Reports provided by the National Centers for Environmental Information (hereafter: Storm Events Database) contained 927 downburst, 914 microburst, and only 27 macroburst entries. We found a spatial variability of reported downbursts that is unlikely to be a result of natural processes, but rather artificially caused by the population density. An example of this bias is the abrupt decline in the number of reported events between southern and northern Arizona. Combining the Storm Events Database, ERA5 reanalysis and lightning data from the National Lightning Detection Network, we showed that cold pool strength, low-level lapse rates , WINDEX, lifted condensation level, DCAPE, WMAXSHEAR, derecho composite parameter, 2-m temperature, delta theta-e and mean low-level relative humidity demonstrate some value in downburst prediction. By combining the best predictor (cold pool strength) with the least correlated WMAXSHEAR, we created a downburst environment index (DEI) and used it to model climatological frequency of favorable downburst environments. Our analysis has shown that favorable downburst environments conditioned on lightning are the most frequent during summer over Southwest and Southeast with the most extreme environments across Great Plains. The vertical profiles of theta-e for the downburst events from reanalysis are further compared against nonsevere thunderstorms and rawinsonde data from four downburst field measurement campaigns. The results show that changes in theta-e over the lowest 200 hPa are the most important for downburst formation.
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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 | 0.001 | 0.001 |
| 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".