Understanding the Interannual Variability in Severe Hail Storms in Australia
Bibliographic record
Abstract
Hailstorms are a leading cause of insured losses in Australia, damaging vehicles, buildings, and agriculture amongst other infrastructure. Individual hailstorms have caused damage bills exceeding AUD$1b, particularly in the densely populated cities of Brisbane and Sydney on Australia’s east coast. Despite hail’s high damage potential, the drivers of the large observed interannual variability in large-hail events in Australia remain highly uncertain.Here, we use the radar product Maximum Expected Size of Hail (MESH) to investigate the drivers of interannual hail variability across Australia’s most hail-affected regions. We use a MESH threshold of 30 mm for hail occurrences. Various studies have mentioned that MESH is a good discriminator for hail occurrences and that a great proportion of hailstorms and hail reports are captured by a 30-mm MESH threshold. We examine how different drivers of variability such as the El Niño Southern Oscillation (ENSO), Southern Annular Mode (SAM), and Indian Ocean Dipole (IOD) affect large-hail occurrence in Australia. We focus on areas where hailstorms cause the greatest damage, to perform a robust analysis of year-to-year variations in large-hail occurrences.We found a strong correlation between the ENSO 3.4 index and large hail occurrences in Australia, mainly around these major cities of Sydney and Brisbane. We will present the results of our analysis for the Australian east coast’s most impacted cities: Sydney, Brisbane, Melbourne, and Canberra. This includes analysis of the interannual variability and its connection to large-scale climate drivers, convective parameters, and future research needs.
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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 | 0.001 | 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".