Three-dimensional Analytical Models of Background Winds Interacting with Thunderstorm Outflows
Bibliographic record
Abstract
Thunderstorm outflows interact with background atmospheric boundary layer winds in complex ways: opposing background winds can decelerate the outflow, while aligned winds can amplify it. Moreover, as the background winds pass through a thunderstorm outflow, they lose momentum due to the turbulence interaction with the outflow. Despite this, an analytical framework to model the deceleration of background winds as they penetrate a thunderstorm outflow has been lacking. This study introduces the first set of analytical expressions to quantify this interaction using a turbulence drag law adapted from classical atmospheric boundary layer theory. Two models are proposed: (1) a bulk interaction model, which characterizes the background and downburst winds using their representative (constant) velocities, and (2) an explicit interaction model, which incorporates the spatial variation of the downburst wind field. Turbulence drag coefficients for both models are derived from wind tunnel experiments. While the bulk model offers a simplified approach, it closely approximates the results of the more detailed explicit model, demonstrating its robustness. Our findings also reveal that simple vector addition of undisturbed wind fields can significantly overestimate, and occasionally underestimate, actual wind speeds within the interaction zone. Finally, we compare both models with a field observation of a downburst event, demonstrating the utility of the analytical framework for practical applications.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".