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Implementation of storm-following nest for the next-generation Hurricane Analysis and Forecast System (HAFS)

Authors :
William Ramstrom
Xuejin Zhang
Kyle Ahern
Sundararaman Gopalakrishnan
Source :
Frontiers in Earth Science, Vol 12 (2024)
Publication Year :
2024
Publisher :
Frontiers Media S.A., 2024.

Abstract

Tropical cyclones models have long used nesting to achieve higher resolution of the inner core than was feasible for entire model domains. These high resolution nests have been shown to better capture storm structures and improve forecast accuracy. The Hurricane Analysis and Forecast System (HAFS) is the new-generation numerical model embedded within NOAA’s Unified Forecast System (UFS). The document highlights the importance of high horizontal resolution (2 km or finer) in accurately simulating the small-scale features of tropical cyclones, such as the eyewall and eye. To meet this need, HAFS was developed by NOAA leveraging a high-resolution, storm-following nest. This nest moves with the cyclone, allowing better representation of small-scale features and more accurate feedback between the cyclone’s inner core and the larger environment. This hurricane following nest capability, implemented in the Finite-Volume Cubed-Sphere (FV3) dynamical core within the UFS framework, can be run both within the regional as well as global forecast systems. A regional version of HAFS with a single moving nest went into operations in 2023. HAFS also includes the first ever moving nest implemented within a global model which is currently being used for research. In this document we provide details of the implementation of moving nests and provide some of the results from both global and regional simulations. For the first time NOAA P3 flight data was used to evaluate the inner core structure from the global run.

Details

Language :
English
ISSN :
22966463
Volume :
12
Database :
Directory of Open Access Journals
Journal :
Frontiers in Earth Science
Publication Type :
Academic Journal
Accession number :
edsdoj.37c045188ea444e2a20962a61db219a9
Document Type :
article
Full Text :
https://doi.org/10.3389/feart.2024.1419233