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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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 |
work_keys_str_mv |
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