2022 real-time Hurricane forecasts from an experimental version of the Hurricane analysis and forecast system (HAFSV0.3S)
During the 2022 hurricane season, real-time forecasts were conducted using an experimental version of the Hurricane Analysis and Forecast System (HAFS). The version of HAFS detailed in this paper (HAFSV0.3S, hereafter HAFS-S) featured the moving nest recently developed at NOAA AOML, and also model p...
Published in: | Frontiers in Earth Science |
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Main Authors: | , , , , , , , , , , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Frontiers Media S.A.
2023
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Online Access: | https://doi.org/10.3389/feart.2023.1264969 https://doaj.org/article/dd05a2f179e3456f9d9cae556028dc4d |
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author | Andrew Hazelton Ghassan J. Alaka Lew Gramer William Ramstrom Sarah Ditchek Xiaomin Chen Bin Liu Zhan Zhang Lin Zhu Weiguo Wang Biju Thomas JungHoon Shin Chuan-Kai Wang Hyun-Sook Kim Xuejin Zhang Avichal Mehra Frank Marks Sundararaman Gopalakrishnan |
author_facet | Andrew Hazelton Ghassan J. Alaka Lew Gramer William Ramstrom Sarah Ditchek Xiaomin Chen Bin Liu Zhan Zhang Lin Zhu Weiguo Wang Biju Thomas JungHoon Shin Chuan-Kai Wang Hyun-Sook Kim Xuejin Zhang Avichal Mehra Frank Marks Sundararaman Gopalakrishnan |
author_sort | Andrew Hazelton |
collection | Directory of Open Access Journals: DOAJ Articles |
container_title | Frontiers in Earth Science |
container_volume | 11 |
description | During the 2022 hurricane season, real-time forecasts were conducted using an experimental version of the Hurricane Analysis and Forecast System (HAFS). The version of HAFS detailed in this paper (HAFSV0.3S, hereafter HAFS-S) featured the moving nest recently developed at NOAA AOML, and also model physics upgrades: TC-specific modifications to the planetary boundary layer (PBL) scheme and introduction of the Thompson microphysics scheme. The real-time forecasts covered a large dataset of cases across the North Atlantic and eastern North Pacific 2022 hurricane seasons, providing an opportunity to evaluate this version of HAFS ahead of planned operational implementation of a similar version in 2023. The track forecast results show that HAFS-S outperformed the 2022 version of the operational HWRF model in the Atlantic, and was the best of several regional hurricane models in the eastern North Pacific for track. The intensity results were more mixed, with a dropoff in skill at Days 4–5 in the Atlantic but increased skill in the eastern North Pacific. HAFS-S also showed some larger errors than the long-time operational Hurricane Weather Research and Forecasting (HWRF) model in the radius of 34-knot wind, but other radii metrics are improved. Detailed analysis of Hurricane Ian in the Atlantic highlights both the strengths of HAFS and opportunities for further development and improvement. |
format | Article in Journal/Newspaper |
genre | North Atlantic |
genre_facet | North Atlantic |
geographic | Pacific |
geographic_facet | Pacific |
id | ftdoajarticles:oai:doaj.org/article:dd05a2f179e3456f9d9cae556028dc4d |
institution | Open Polar |
language | English |
op_collection_id | ftdoajarticles |
op_doi | https://doi.org/10.3389/feart.2023.1264969 |
op_relation | https://www.frontiersin.org/articles/10.3389/feart.2023.1264969/full https://doaj.org/toc/2296-6463 2296-6463 doi:10.3389/feart.2023.1264969 https://doaj.org/article/dd05a2f179e3456f9d9cae556028dc4d |
op_source | Frontiers in Earth Science, Vol 11 (2023) |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | openpolar |
spelling | ftdoajarticles:oai:doaj.org/article:dd05a2f179e3456f9d9cae556028dc4d 2025-01-16T23:42:00+00:00 2022 real-time Hurricane forecasts from an experimental version of the Hurricane analysis and forecast system (HAFSV0.3S) Andrew Hazelton Ghassan J. Alaka Lew Gramer William Ramstrom Sarah Ditchek Xiaomin Chen Bin Liu Zhan Zhang Lin Zhu Weiguo Wang Biju Thomas JungHoon Shin Chuan-Kai Wang Hyun-Sook Kim Xuejin Zhang Avichal Mehra Frank Marks Sundararaman Gopalakrishnan 2023-10-01T00:00:00Z https://doi.org/10.3389/feart.2023.1264969 https://doaj.org/article/dd05a2f179e3456f9d9cae556028dc4d EN eng Frontiers Media S.A. https://www.frontiersin.org/articles/10.3389/feart.2023.1264969/full https://doaj.org/toc/2296-6463 2296-6463 doi:10.3389/feart.2023.1264969 https://doaj.org/article/dd05a2f179e3456f9d9cae556028dc4d Frontiers in Earth Science, Vol 11 (2023) HAFS tropical cyclones numerical modeling verification real-time prediction Science Q article 2023 ftdoajarticles https://doi.org/10.3389/feart.2023.1264969 2023-10-08T00:36:51Z During the 2022 hurricane season, real-time forecasts were conducted using an experimental version of the Hurricane Analysis and Forecast System (HAFS). The version of HAFS detailed in this paper (HAFSV0.3S, hereafter HAFS-S) featured the moving nest recently developed at NOAA AOML, and also model physics upgrades: TC-specific modifications to the planetary boundary layer (PBL) scheme and introduction of the Thompson microphysics scheme. The real-time forecasts covered a large dataset of cases across the North Atlantic and eastern North Pacific 2022 hurricane seasons, providing an opportunity to evaluate this version of HAFS ahead of planned operational implementation of a similar version in 2023. The track forecast results show that HAFS-S outperformed the 2022 version of the operational HWRF model in the Atlantic, and was the best of several regional hurricane models in the eastern North Pacific for track. The intensity results were more mixed, with a dropoff in skill at Days 4–5 in the Atlantic but increased skill in the eastern North Pacific. HAFS-S also showed some larger errors than the long-time operational Hurricane Weather Research and Forecasting (HWRF) model in the radius of 34-knot wind, but other radii metrics are improved. Detailed analysis of Hurricane Ian in the Atlantic highlights both the strengths of HAFS and opportunities for further development and improvement. Article in Journal/Newspaper North Atlantic Directory of Open Access Journals: DOAJ Articles Pacific Frontiers in Earth Science 11 |
spellingShingle | HAFS tropical cyclones numerical modeling verification real-time prediction Science Q Andrew Hazelton Ghassan J. Alaka Lew Gramer William Ramstrom Sarah Ditchek Xiaomin Chen Bin Liu Zhan Zhang Lin Zhu Weiguo Wang Biju Thomas JungHoon Shin Chuan-Kai Wang Hyun-Sook Kim Xuejin Zhang Avichal Mehra Frank Marks Sundararaman Gopalakrishnan 2022 real-time Hurricane forecasts from an experimental version of the Hurricane analysis and forecast system (HAFSV0.3S) |
title | 2022 real-time Hurricane forecasts from an experimental version of the Hurricane analysis and forecast system (HAFSV0.3S) |
title_full | 2022 real-time Hurricane forecasts from an experimental version of the Hurricane analysis and forecast system (HAFSV0.3S) |
title_fullStr | 2022 real-time Hurricane forecasts from an experimental version of the Hurricane analysis and forecast system (HAFSV0.3S) |
title_full_unstemmed | 2022 real-time Hurricane forecasts from an experimental version of the Hurricane analysis and forecast system (HAFSV0.3S) |
title_short | 2022 real-time Hurricane forecasts from an experimental version of the Hurricane analysis and forecast system (HAFSV0.3S) |
title_sort | 2022 real-time hurricane forecasts from an experimental version of the hurricane analysis and forecast system (hafsv0.3s) |
topic | HAFS tropical cyclones numerical modeling verification real-time prediction Science Q |
topic_facet | HAFS tropical cyclones numerical modeling verification real-time prediction Science Q |
url | https://doi.org/10.3389/feart.2023.1264969 https://doaj.org/article/dd05a2f179e3456f9d9cae556028dc4d |