Reanalysis of the PacIOOS Hawaiian Island Ocean Forecast System, an implementation of the Regional Ocean Modeling System v3.6

<p>A 10-year reanalysis of the PacIOOS Hawaiian Island Ocean Forecast System was produced using an incremental strong-constraint 4-D variational data assimilation with the Regional Ocean Modeling System (ROMS v3.6). Observations were assimilated from a range of sources: satellite-derived sea s...

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Main Authors: D. Partridge, T. Friedrich, B. S. Powell
Format: Article
Language:English
Published: Copernicus Publications 2019-01-01
Series:Geoscientific Model Development
Online Access:https://www.geosci-model-dev.net/12/195/2019/gmd-12-195-2019.pdf
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spelling doaj-fad65dece7354f7fb005d674997fecfd2020-11-25T00:09:19ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032019-01-011219521310.5194/gmd-12-195-2019Reanalysis of the PacIOOS Hawaiian Island Ocean Forecast System, an implementation of the Regional Ocean Modeling System v3.6D. Partridge0T. Friedrich1B. S. Powell2University of Hawai`i at Mānoa, Department of Oceanography, Marine Sciences Building, 1000 Pope Road, Honolulu, Hawai`i 96822, USAUniversity of Hawai`i at Mānoa, Department of Oceanography, Marine Sciences Building, 1000 Pope Road, Honolulu, Hawai`i 96822, USAUniversity of Hawai`i at Mānoa, Department of Oceanography, Marine Sciences Building, 1000 Pope Road, Honolulu, Hawai`i 96822, USA<p>A 10-year reanalysis of the PacIOOS Hawaiian Island Ocean Forecast System was produced using an incremental strong-constraint 4-D variational data assimilation with the Regional Ocean Modeling System (ROMS v3.6). Observations were assimilated from a range of sources: satellite-derived sea surface temperature (SST), salinity (SSS), and height anomalies (SSHAs); depth profiles of temperature and salinity from Argo floats, autonomous Seagliders, and shipboard conductivity–temperature–depth (CTD); and surface velocity measurements from high-frequency radar (HFR). The performance of the state estimate is examined against a forecast showing an improved representation of the observations, especially the realization of HFR surface currents. EOFs of the increments made during the assimilation to the initial conditions and atmospheric forcing components are computed, revealing the variables that are influential in producing the state-estimate solution and the spatial structure the increments form.</p>https://www.geosci-model-dev.net/12/195/2019/gmd-12-195-2019.pdf
collection DOAJ
language English
format Article
sources DOAJ
author D. Partridge
T. Friedrich
B. S. Powell
spellingShingle D. Partridge
T. Friedrich
B. S. Powell
Reanalysis of the PacIOOS Hawaiian Island Ocean Forecast System, an implementation of the Regional Ocean Modeling System v3.6
Geoscientific Model Development
author_facet D. Partridge
T. Friedrich
B. S. Powell
author_sort D. Partridge
title Reanalysis of the PacIOOS Hawaiian Island Ocean Forecast System, an implementation of the Regional Ocean Modeling System v3.6
title_short Reanalysis of the PacIOOS Hawaiian Island Ocean Forecast System, an implementation of the Regional Ocean Modeling System v3.6
title_full Reanalysis of the PacIOOS Hawaiian Island Ocean Forecast System, an implementation of the Regional Ocean Modeling System v3.6
title_fullStr Reanalysis of the PacIOOS Hawaiian Island Ocean Forecast System, an implementation of the Regional Ocean Modeling System v3.6
title_full_unstemmed Reanalysis of the PacIOOS Hawaiian Island Ocean Forecast System, an implementation of the Regional Ocean Modeling System v3.6
title_sort reanalysis of the pacioos hawaiian island ocean forecast system, an implementation of the regional ocean modeling system v3.6
publisher Copernicus Publications
series Geoscientific Model Development
issn 1991-959X
1991-9603
publishDate 2019-01-01
description <p>A 10-year reanalysis of the PacIOOS Hawaiian Island Ocean Forecast System was produced using an incremental strong-constraint 4-D variational data assimilation with the Regional Ocean Modeling System (ROMS v3.6). Observations were assimilated from a range of sources: satellite-derived sea surface temperature (SST), salinity (SSS), and height anomalies (SSHAs); depth profiles of temperature and salinity from Argo floats, autonomous Seagliders, and shipboard conductivity–temperature–depth (CTD); and surface velocity measurements from high-frequency radar (HFR). The performance of the state estimate is examined against a forecast showing an improved representation of the observations, especially the realization of HFR surface currents. EOFs of the increments made during the assimilation to the initial conditions and atmospheric forcing components are computed, revealing the variables that are influential in producing the state-estimate solution and the spatial structure the increments form.</p>
url https://www.geosci-model-dev.net/12/195/2019/gmd-12-195-2019.pdf
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