AUTHOR=Peng Jiayi , Zhang Zhan , Wang Weiguo , Panda Rajendra , Liu Bin , Weng Yonghui , Mehra Avichal , Tallapragada Vijay , Zhang Xuejin , Gopalakrishnan Sundararaman , Komaromi William , Anderson Jason , Poyer Aaron 

TITLE=HAFS ensemble forecast in AWS cloud

JOURNAL=Frontiers in Earth Science

VOLUME=Volume 12 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2024.1396612

DOI=10.3389/feart.2024.1396612

ISSN=2296-6463

ABSTRACT=In the 2023 hurricane season, the Hurricane Analysis and Forecast System (HAFS) based Ensemble Prediction System (EPS) was being ported to the Amazon Web Service cloud. This relocation aimed to provide real-time hurricane probabilistic forecast guidance for National Hurricane Center (NHC) forecasters. The system comprises Stochastically Perturbed Physics Tendencies (SPPT), Stochastically Kinetic Energy Backscatter (SKEB), and Stochastically Perturbed PBL Humidity (SHUM). Initial and boundary conditions are derived from the National Centers for Environmental Prediction (NCEP) operational Global Ensemble Forecast System (GEFS) 21-member forecast data. The performance of HAFS-EPS for 2023 Atlantic hurricane forecasts was compared with the global GEFS, global ECMWF ensemble, and operational HAFS-A/B forecasts. This comparison highlighted the advantages of higher-resolution regional ensemble forecasts for hurricane track, intensity, Rapid Intensification (RI) probability, and various hazards, including wind, wave, and storm surge probability guidance.