UPSurgeML (v1.0) – A Machine Learning Workflow for Probabilistic Ensemble Forecast of Tropical Cyclone Storm Surge

Abstract. UPSurgeML is an open-source software package for probabilistic storm surge analysis. The code is written in Python, C, and Fortran, and the codebase complies with the National Centers for Environmental Prediction’s (NCEP) guidelines, making it suitable for deployment on High Performance Computers (HPC) like the Weather and Climate Operational Supercomputing System (WCOSS). By utilizing an unstructured mesh, UPSurgeML refines the prediction of water elevations in complex coastal regions. It provides both deterministic and probabilistic storm surge forecasts driven by a small ensemble of hurricane forecasts. UPSurgeML uses parametric hurricane wind and pressure fields, and astronomical tide forcing with a hydrodynamic ocean model to simulate water elevations. Unlike the current Probabilistic Surge (PSurge) model utilized by the National Hurricane Center (NHC), UPSurgeML employs a machine learning surrogate modeling framework. This approach enables a smaller (order of magnitude reduction) hydrodynamic ensemble size that would otherwise be necessary. Consequently, UPSurgeML provides high-resolution water elevation probability fields and exceedance levels with reasonably low computational cost, and has demonstrated its usability as experimental guidance during the hurricane seasons.