Harnessing UAS and High-Resolution Satellite Imagery to Better Characterize Wind Damage and Understand Tornado Behavior
Bibliographic record
Abstract
Abstract The severe storms research community continues to strive toward measuring and quantifying wind speeds near the ground in tornadoes as a component of meeting society’s needs of protecting life and property. Current methods to measure and characterize tornadoes and their wind speeds all have limitations, particularly when only vegetation and rural areas are impacted. To address these problems, the National Severe Storms Laboratory (NSSL) and the Cooperative Institute for Severe and High Impact Weather Research and Operations (CIWRO) employed uncrewed aerial systems (UASs) technologies and specialized satellites to assess storm damage through finescale mapping of wind damage immediately following storm events. From 2021 to 2023, NSSL-CIWRO led 48 UAS-based damage assessments spanning the northern High Plains to the Gulf Coast areas. The goals of this work were to improve our knowledge of tornado dynamics as a function of land cover and topography and to better understand mechanisms of wind damage production in convective storms. In parallel, NSSL-CIWRO developed workflows to facilitate the rapid sharing of UAS and satellite imagery to National Weather Service Weather Forecast Offices (NWS WFOs) and the Federal Emergency Management Agency (FEMA). Operationally, NSSL UAS imagery and derived products have aided traditional ground- and satellite-based surveys and have provided important clues on tornado intensity by detailing damage to vegetation in inaccessible areas. UAS and high-resolution satellite imagery may become a routine and key component to understand tornado and severe weather behavior through the analysis of specialized field observations. Significance Statement This article highlights how uncrewed aerial system (UAS) technologies and specialized satellites were used at National Severe Storms Laboratory–Cooperative Institute for Severe and High Impact Weather Research and Operations (NSSL-CIWRO) to better assess storm damage and provide new insights into tornado behavior through finescale mapping of wind damage. Combining UAS and high-resolution satellite information with observational datasets and numerical simulations can improve our knowledge of tornado intensity and dynamics as well as our understanding of wind damage production in severe convective storms. Additionally, information gained from high-resolution imagery and derived products have shown operational benefits for multiagency damage assessments [i.e., National Weather Service (NWS), Federal Emergency Management Agency (FEMA)].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".