Assessing Downburst Kinematics Using Video Footage Analysis
Bibliographic record
Abstract
Measurements of downburst outflows using standard meteorological instruments (e.g., anemometers) are rare due to their transient and localized nature. However, video recordings of such events are becoming more frequent. This short communication (Technical Note) study presents a new approach to estimating the kinematics of a downburst event using video footage recordings of the event. The main geometric dimensions of the event, such as downdraft diameter, cloud base height, outflow depth, and the radius of the outflow at a given moment in time, are estimated by sizing them against reference structures of known dimensions that are present in the video footage. From this analysis, and knowing the frame rate of the video recording, one can estimate the characteristic velocities in the downburst event, such as the mean downdraft velocity and the mean velocity of the radial outflow propagation. The proposed method is tested on an August 2015 downburst event that hit Tucson, Arizona, United States. The diameter of the downburst outflow increased with the time from approximately 1.10 km to 3.35 km. This range of values indicates that the event was a microburst. The mean descending velocity of downburst downdraft was 8.9 m s−1 and the horizontal velocity of outflow propagation was 17.7 m s−1. The latter velocity is similar to the measured wind gust at the nearby weather station and Doppler radar. The outflow depth is estimated at 160 m, and the cloud base height was approximately 1.24 km. Estimating the kinematics of downbursts using video footage, while subject to certain limitations, does yield a useful estimation of the main downburst kinematics that contribute to a better quantification of these localized windstorms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.009 | 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 teacher head, 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".