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ORIGINAL RESEARCH article

Front. Earth Sci.
Sec. Atmospheric Science
Volume 12 - 2024 | doi: 10.3389/feart.2024.1396612
This article is part of the Research Topic Tropical Cyclone Modeling and Prediction: Advances in Model Development and Its Applications View all 12 articles

HAFS Ensemble Forecast in AWS Cloud

Provisionally accepted
JIAYI PENG JIAYI PENG 1*Zhan Zhang Zhan Zhang 2Weiguo Wang Weiguo Wang 3Rajendra Panda Rajendra Panda 4Bin Liu Bin Liu 5Yonghui Weng Yonghui Weng 5Avichal Mehra Avichal Mehra 2Vijay Tallapragada Vijay Tallapragada 2Xuejin Zhang Xuejin Zhang 6Sundararaman Gopalakrishnan Sundararaman Gopalakrishnan 6William Komaromi William Komaromi 7Aaron Poyer Aaron Poyer 7
  • 1 Environmental Modeling Center, National Centers for Environmental Prediction, Axiom Consultants, Inc., Rockville, Maryland, United States
  • 2 NCEP Environmental Modeling Center (EMC), College Park, Maryland, United States
  • 3 Environmental Modeling Center, National Centers for Environmental Prediction, Science Applications International Corporation (United States), McLean, Virginia, United States
  • 4 General Dynamics IT, NCEP Environmental Modeling Center (EMC), College Park, Maryland, United States
  • 5 Environmental Modeling Center, National Centers for Environmental Prediction, Lynker Technologies LLC, Leesburg, Virginia, United States
  • 6 Atlantic Oceanographic and Meteorological Laboratory (NOAA), Miami, Florida, United States
  • 7 Office of Science and Technology Integration, National Weather Service, Silver Spring, Maryland, United States

The final, formatted version of the article will be published soon.

    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.

    Keywords: Hafs, ensemble, AWS cloud, Hurricane, forecast

    Received: 06 Mar 2024; Accepted: 13 Sep 2024.

    Copyright: © 2024 PENG, Zhang, Wang, Panda, Liu, Weng, Mehra, Tallapragada, Zhang, Gopalakrishnan, Komaromi and Poyer. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: JIAYI PENG, Environmental Modeling Center, National Centers for Environmental Prediction, Axiom Consultants, Inc., Rockville, 20850, Maryland, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.