Document Type
Article
Publication Date
1-1-2016
Department
Marine Science
School
Ocean Science and Engineering
Abstract
NASA's Surface Water and Ocean Topography (SWOT) satellite, scheduled for launch in 2020, will provide observations of sea surface height anomaly (SSHA) at a significantly higher spatial resolution than current satellite altimeters. This new observation type is expected to improve the ocean model mesoscale circulation. The potential improvement that SWOT will provide is investigated in this work by way of twin-data assimilation experiments using the Navy Coastal Ocean Model four-dimensional variational data assimilation (NCOM-4DVAR) system in its weak constraint formulation. Simulated SWOT observations are sampled from an ocean model run (referred to as the "nature" run) using an observation-simulator program provided by the SWOT science team. The SWOT simulator provides realistic spatial coverage, resolution, and noise characteristics based on the expected performance of the actual satellite. Twin-data assimilation experiments are run for a two-month period during which simulated observations are assimilated into a separate model (known as the background model) in a series of 96-h windows. The final condition of each analysis window is used to initialize a new 96-h forecast, and each forecast is compared to the nature run to determine the impact of the assimilated data. It is demonstrated here that the simulated SWOT observations help to constrain the model mesoscale to be more consistent with the nature run than the assimilation of traditional altimeter observations alone. The findings of this study suggest that data from SWOT may have a substantial impact on improving the ocean model forecast of mesoscale features and surface ocean velocity.
Publication Title
Monthly Weather Review
Volume
144
Issue
10
First Page
3767
Last Page
3782
Recommended Citation
Carrier, M.,
Ngodock, H.,
Smith, S.,
Souopgui, I.,
Bartels, B.
(2016). Examining the Potential Impact of SWOT Observations In an Ocean Analysis-Forecasting System. Monthly Weather Review, 144(10), 3767-3782.
Available at: https://aquila.usm.edu/fac_pubs/19730
Comments
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