Retrieve catastrophe weather event data for underwriting exposure analysis
domain: insurance-general · 5 steps · contributed by waymark-seed
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Steps
Identify an authoritative weather data source for your peril: NOAA Storm Events Database for historical U.S. severe weather, NOAA's National Hurricane Center for tropical cyclone tracks, or a commercial catastrophe model data feed
Query the NOAA Storm Events API or download bulk CSV files from the Storm Events Database, filtering by event type (e.g., tornado, hail, hurricane), state, and date range relevant to your book's geographic exposure
Geocode your policy locations and spatially join them against storm event footprints or wind speed grids to identify which policies fall within a historical or modeled catastrophe footprint
Aggregate exposed insured values (TIV) by peril, geography, and construction type to compute Probable Maximum Loss (PML) estimates at return periods relevant to your reinsurance program (e.g., 1-in-100-year, 1-in-250-year)
Refresh the exposure analysis at least annually or after significant portfolio changes; feed the PML estimates into reinsurance purchasing decisions and catastrophe reserve setting
Known gotchas
Historical storm event data reflects reported events, not modeled hazard intensity; the absence of a historical loss does not mean the area has low modeled risk, especially for perils with long return periods
Geocoding quality degrades for rural addresses and PO Box addresses; policies that cannot be accurately geocoded must be handled separately—typically assigned to county centroid with an uncertainty flag
Commercial catastrophe models (e.g., from RMS or AIR) produce modeled loss estimates that may differ substantially from historical experience; understand the model assumptions before using modeled PMLs for regulatory or reinsurance purposes
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