US-Regierung hält Top-Hurrikan-Vorhersagemodell zurück, was Empörung unter tödlichen Folgen auslöst. Helene – Vom Steuerzahler finanzierte Daten werden hinter der Paywall des Versicherungsunternehmens vertraulich behandelt

https://www.theregister.com/2024/10/01/government_hurricane_forecast_secrecy/

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  1. marketrent on

    Excerpts from [article](https://www.theregister.com/2024/10/01/government_hurricane_forecast_secrecy/) by Thomas Claburn:

    *The National Oceanic and Atmospheric Administration (NOAA) cannot reveal weather forecasts from a particularly accurate hurricane prediction model to the public that pays for the American government agency – because of a deal with a private insurance risk firm.*

    *The model at issue is called the Hurricane Forecast Improvement Program (HFIP) Corrected Consensus Approach (HCCA). In 2023, it was deemed in a National Hurricane Center (NHC) report to be one of the two “best performers,” the other being a model called IVCN (Intensity Variable Consensus).*

    *A 2020 contract between NOAA and RenaissanceRe Risk Sciences, disclosed in response to a Freedom of Information Act request by The Washington Post, requires NOAA to keep HCCA forecasts – which incorporate a proprietary technique from RenaissanceRe – secret for five years.*

    *The deal is scheduled to expire next year, and NOAA reportedly expects to release HCCA model data in time for the 2025 hurricane season.*

    *But in the aftermath of Hurricane Helene – which hit the US southeast, left a 800-mile trail of destruction, and is said to have killed at least 130 people since Thursday – there are calls to make HCCA data available sooner.*

    *”Placing business interests over public disclosure cannot be the precedent for public-private industry partnerships, and certainly not for projects that have the ability to save lives,” said Lauren Harper, Daniel Ellsberg Chair on Government Secrecy at Freedom of the Press Foundation, in an article last week.*

  2. Meanwhile, the public forecasts for hurricane Helene were extremely accurate.

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