Stochastic modeling of radar-derived maximum estimated size of hail for scenario-based hail loss estimation
Bibliographic record
Abstract
This study develops a stochastic method for simulating the maximum estimated size of hail (MESH) values at locations within a hail swath and conducts a scenario-based hail loss estimation at regional scale. The method is based on MESHmax (which is obtained by taking the maximum value of MESH data over a hailstorm per location) and hail insurance loss data for the June 13th , 2020, July 2nd , 2021, and August 5th , 2024 Calgary hailstorms. The stochastic MESHmax modeling identifies the hail swath (centerline and surrounding points) using MESH data and characterizes MESHmax along the centerline of the hail swath and at off-centerline locations. In the proposed method, spatial correlations of the MESHmax values along the centerline and off-centerline locations are considered. In addition, an empirical vulnerability curve is developed by relating MESHmax to insurance losses for residential properties. The scenario-based hail loss estimation generates numerous realizations of regional MESHmax maps and integrates them with a hail vulnerability curve for residential properties that is derived from the insurance loss data of three recent hailstorms in Calgary. An illustrative hail loss estimation is performed by considering a hypothetical event similar to the June 13th , 2020 Calgary hailstorm. Using the developed scenario-based hail loss estimation tool, the probability distribution of the regional hail loss can be obtained. The stochastic simulation of the 2020 hailstorm is capable of reasonably hindcasting actual loss in that event. The sensitivity analysis results highlight significant influences of spatial variability of MESHmax values and uncertainty of the insurance loss generations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".