A Probabilistic Framework for Site-Specific Tropical Cyclone Loss Quantification and Reinsurance Optimization
Abstract
Quantifying the financial risk posed by landfalling tropical cyclones requires statistical methods that extend beyond the limits of a short and non-stationary historical observational record. This paper explores techniques for improving site-specific loss estimation in tropicalcyclone catastrophe risk, an area of ongoing interest to insurance and reinsurance pricingand solvency capital management. We present an end-to-end probabilistic catastrophe riskmodel that captures extreme tail behavior by fitting a continuous Weibull intensity distribution to multi-era NOAA HURDAT2 meteorological data, simulating a 10,000-year stochasticevent set. This hazard catalog is evaluated against an Industry Exposure Database derivedfrom NOAA Digital Coast economic indices, representing a total insured asset value of$39.01 Billion across primary high-hazard coastal corridors. Physical damage is simulatedvia structural vulnerability functions parameterized as log-normal cumulative distributionfunctions, and liabilities are reshaped through vectorized financial policy layers, tracking individual properties rather than county-averaged zones. The resulting property-level resolution reveals loss concentration patterns within a single metro area that a regionally-averagedmodel cannot recover.
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BibTeX
@misc{rathore2026probabilistic,
title = {{A Probabilistic Framework for Site-Specific Tropical Cyclone Loss Quantification and Reinsurance Optimization}},
author = {Kunal J. Rathore},
howpublished = {EarthArXiv},
year = {2026},
month = sep,
doi = {10.31223/x5dv48},
url = {https://eartharxiv.org/repository/view/15152/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A Probabilistic Framework for Site-Specific Tropical Cyclone Loss Quantification and Reinsurance Optimization
AU - Kunal J. Rathore
PY - 2026
DA - 2026/09/24
PB - EarthArXiv
DO - 10.31223/x5dv48
UR - https://eartharxiv.org/repository/view/15152/
AB - Quantifying the financial risk posed by landfalling tropical cyclones requires statistical methods that extend beyond the limits of a short and non-stationary historical observational record. This paper explores techniques for improving site-specific loss estimation in tropicalcyclone catastrophe risk, an area of ongoing interest to insurance and reinsurance pricingand solvency capital management. We present an end-to-end probabilistic catastrophe riskmodel that captures extreme tail behavior by fitting a continuous Weibull intensity distribution to multi-era NOAA HURDAT2 meteorological data, simulating a 10,000-year stochasticevent set. This hazard catalog is evaluated against an Industry Exposure Database derivedfrom NOAA Digital Coast economic indices, representing a total insured asset value of$39.01 Billion across primary high-hazard coastal corridors. Physical damage is simulatedvia structural vulnerability functions parameterized as log-normal cumulative distributionfunctions, and liabilities are reshaped through vectorized financial policy layers, tracking individual properties rather than county-averaged zones. The resulting property-level resolution reveals loss concentration patterns within a single metro area that a regionally-averagedmodel cannot recover.
ER -