An ensemble optimal interpolation (EnOI) data assimilation system for a high‐resolution (0.1° horizontal) version of the Community Earth System Model Version 2 (CESM2) ocean component is presented. For this purpose, a new version of the Data Assimilation Research Testbed (DART Manhattan) that enables large‐state data assimilation by distributing state vector information across multiple processors at high resolution is used. The EnOI scheme uses a static (but seasonally varying) 84‐member ensemble of precomputed perturbations to approximate samples from the forecast error covariance and utilizes a single model integration to estimate the forecast mean. Satellite altimetry and sea surface temperature observations along with in situ temperature and salinity observations are assimilated. This new data assimilation framework is then used to produce a global high‐resolution retrospective analysis for the 2005–2016 period. Not surprisingly, the assimilation is shown to generally improve the time‐mean ocean state estimate relative to an identically forced ocean model simulation where no observations are ingested. However, diminished improvements are found in undersampled regions. Lack of adequate salinity observations in the upper ocean actually results in deterioration of salinity there. The EnOI scheme is found to provide a practical and cost‐effective alternative to the use of an ensemble of forecasts.
Gliders are low‐power autonomous underwater vehicles used to obtain oceanic measurements in vertical sections. Assimilation of glider temperature and salinity into coastal ocean circulation models holds the potential to improve the ocean subsurface structure estimate. In this study, the impact of assimilation of glider observations is studied using a four‐dimensional variational (4DVAR) data assimilation and forecast system set offshore of Oregon and Washington on the U.S. West Coast. Four test cases are compared: (1) no assimilation, (2) assimilation of glider temperature and salinity data alone, (3) assimilation of the glider data in combination with the surface observations including satellite sea surface temperature, sea surface height, and high‐frequency radar surface velocities, and (4) assimilation of the surface data alone. It is found that the assimilation of glider observations alone creates unphysical eddies in the vicinity of the glider transect. As a consequence, the forecast errors in the surface velocity and temperature increase compared to the case without data assimilation. Assimilation of surface and subsurface observations in combination prevents these features from forming and reduces the errors in the forecasts for the subsurface fields compared to the other three experiments. These improvements persisted in 21‐day forecasts run after the last data assimilation cycle.
more » « less- NSF-PAR ID:
- 10459949
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Oceans
- Volume:
- 124
- Issue:
- 1
- ISSN:
- 2169-9275
- Page Range / eLocation ID:
- p. 750-772
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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