Large scale atmospheric cross-peril stochastic catastrophe models
Bibliographic record
Abstract
Impact Forecasting, a catastrophe model development branch of Aon, develops catastrophe models for various countries and perils, including floods, windstorms, earthquakes, wildfires, hurricanes, and typhoons. These models are crucial for the insurance and reinsurance industry to estimate losses in terms of severity and frequency. To address the increasing demand for evaluating losses across multiple countries and perils, Impact Forecasting has started using large ensembles of global climate models (GCM) and regional climate models (RCM). These models serve as a common forcing input for catastrophe models related to atmospheric perils such as flooding (fluvial, pluvial, and coastal), summer storms, windstorms, and wildfires. The use of GCM/RCM as common forcing input offers two main advantages: Spatial Consistency: The data are spatially consistent at a global or continental scale, which helps in addressing the issue of cross-country correlations. Variability: The large number of available ensembles provides sufficient variability to build a representative stochastic catalogue of potential catastrophes. We will present several examples of this approach (Pan-European flood model, the Canadian flood and wildfire models), where common GCM/RCM inputs are used to provide a consistent view of losses across large regions and various perils. We will also show how we adress the issue of low resolution of GCM/RCM models using machine learning.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".