The S2K Severe Weather Detection Algorithms and Their Performance
Bibliographic record
Abstract
One of the main goals of the Sydney 2000 Forecast Demonstration Project was to demonstrate the efficacy and utility of automated severe weather detection radar algorithms. As a contribution to this goal, this paper describes the radar-based severe weather algorithms used in the project, their performance, and related radar issues. Participants in this part of the project included the National Severe Storm Laboratory (NSSL) Warning Decision Support System (WDSS), the Meteorological Service of Canada Canadian Radar Decision Support (CARDS) system, the National Center for Atmospheric Research Thunderstorm Initiation, Tracking, Analysis, and Nowcasting (TITAN) system, and a precipitation-typing algorithm from the Bureau of Meteorology Research Centre polarized C-band polarimetric (C-Pol) radar. Three radars were available: the S-band reflectivity-only operational radar, the C-band Doppler Kurnell radar, and the C-band Doppler polarization C-Pol radar. The radar algorithms attempt to diagnose the presence of storm cells; provide storm tracks; identify mesocyclone circulations, downbursts and/or microbursts, and hail; and provide storm ranking. The tracking and identification of cells was undertaken using TITAN and WDSS. Three versions of TITAN were employed to track weak and strong cells. Results show TITAN cell detection thresholds influence the ability of the algorithm to clearly identify storm cells and also the ability to correctly track the storms. WDSS algorithms are set up with lower-volume thresholds and provided many more tracks. WDSS and CARDS circulation algorithms were adapted to the Southern Hemisphere. CARDS had lower detection thresholds and, hence, detected more circulations than WDSS. Radial-velocity-based and reflectivity-based downburst algorithms were available from CARDS. Since the reflectivity-based algorithm was based on features aloft, it provided an earlier indication of strong surface winds. Three different hail algorithms from WDSS, CARDS, and C-Pol provided output on the presence, the probability, and the size of hail. Although the algorithms differed considerably they provided similar results. Size distributions were similar to observations. The WDSS provided a ranking algorithm to identify the most severe storm. Many of the algorithms had been adapted and altered to account for differences in radar technology, configuration, and meteorological regime. The various combinations of different algorithms and different radars provided an unprecedented opportunity to study the impact of radar technology on the performance of the severe weather algorithms. The algorithms were able to operate on both single- and dual-pulse repetition frequency Doppler radars and on C- and S-band radars with minimal changes. The biggest influence on the algorithms was data quality. Beamwidth smoothing limited the effective range of the algorithms and ground clutter and ground clutter filtering affected the quality of the low-level radial velocities and the detection of low-level downbursts. Cycle time of the volume scans significantly affected the tracking results.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".