A Bayesian Hierarchical Model Framework to Quantify Uncertainty of Tropical Cyclone Precipitation Forecasts

• Activity Number: 413 - Analyses of Environmental Data
• Type: Contributed
• Date/Time: Thursday, August 12, 2021 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Stephen Walsh* and Marco May Ferreira and David Higdon and Stephanie Zick
• Companies: Virginia Tech and Virginia Tech Department of Statistics and Virginia Tech and Virginia Tech
• Keywords: Bayesian Statistics; Tropical Cyclone Forecasts; Massive Datasets; Meteorology; Spatial Statistics; Uncertainty Quantification

Abstract

Tropical cyclones present a serious threat to many coastal communities around the world. Most numerical weather prediction models provide deterministic forecasts without measures of their forecast uncertainty. Standard postprocessing techniques may struggle with extreme events or use a 30-day training window that will not adequately characterize the uncertainty of a tropical cyclone forecast. We propose a novel approach that uses a hierarchical model to quantify uncertainty in the spatial correlation parameters of the forecast errors for a numerical weather prediction model. From this, simulated forecast errors provide uncertainty quantification for future tropical cyclone forecasts. We illustrate the approach with the North American Mesoscale model forecasts and use observations based on the Stage IV data product for 47 tropical cyclones between 2004 and 2017. For an incoming storm, our hierarchical framework combines the forecast from the North American Mesoscale model with the information from previous storms to create 95% and 99% prediction maps of rain. For six testing storms from 2018 and 2019, these maps provide appropriate probabilistic coverage of observations.