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Activity Number: 277 - SPEED: Biometrics and Environmental Statistics Part 1
Type: Contributed
Date/Time: Tuesday, August 9, 2022 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract #323047
Title: A Deep Gaussian Process Framework to Quantify Uncertainty of Tropical Cyclone Precipitation Forecasts
Author(s): Stephen Walsh* and Marco Ferreira and David Higdon and Stephanie Zick and Annie Sauer
Companies: Virginia Tech and Virginia Tech and Virginia Tech and Virginia Tech and Virginia Tech
Keywords: Bayesian Statistics; Tropical Cyclone Forecasts; Deep Gaussian Processes; Meteorology; Spatial Statistics; Uncertainty Quantification

Tropical cyclones present a serious threat to many coastal communities around the world. Many numerical weather prediction models provide limited measures of their forecast uncertainty and standard postprocessing techniques may struggle with extreme events. We propose a novel approach that leverages information from past storm events, using a hierarchical model to quantify uncertainty in the spatial correlation parameters of the forecast errors (modeled as deep Gaussian processes) for a numerical weather prediction model. From this, simulated forecast errors provide uncertainty quantification for future tropical cyclone forecasts. We apply this method to the North American Mesoscale model forecasts and use observations based on the Stage IV data product for tropical cyclones between 2004 and 2019.

Authors who are presenting talks have a * after their name.

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