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This study evaluates the feasibility of applying the Estimating the Circulation and Climate of the Ocean (ECCO) data assimilation (DA) framework to a submesoscale-resolving model [Massachusetts Institute of Technology General Circulation Model (MITgcm), at 1-km grid resolution]. This is in preparation for future studies to understand and assimilate the novel swath measurements of sea surface height from the Surface Water and Ocean Topography (SWOT) satellite mission. The model domain is centered at the SWOT calibration/validation (CalVal) site, located 300 km offshore of Monterey Bay, California. We assimilate vertical profiles of temperature and salinity from a linear array of three SWOT prelaunch CalVal moorings, between September and December 2019. Two model solutions are analyzed: 1) a nonassimilating forward simulation termed "first-guess" and 2) an optimized solution that assimilates the in situ observations. Both runs are nested within the global 1/12° Hybrid Coordinate Ocean Model that uses Navy Coupled Ocean Data Assimilation (HYCOM/NCODA) analysis. We evaluate the performance by comparing the two model solutions against assimilated and withheld in situ observations. We show that by assimilating hydrographic data, the model performance over the first-guess solution is improved with an error-to-observation reduction of 30%–45% for temperature, 14%–41% for salinity, and 44%–56% for steric height. Over the study period, the average steric height error at the three assimilated moorings was 1.27 cm for the optimized solution versus 2.6 cm for the first-guess solution. A comparison to withheld glider observations shows that the optimized solution outperforms the first-guess solution with an error-to-observation reduction of approximately 38% in steric height (1.5 versus 2.4 cm). This study indicates, for the first time, that the MITgcm–ECCO framework can be successfully applied to the reconstruction of submesoscale ocean variability, via the nesting of a high-resolution regional domain into a global outer domain. [ABSTRACT FROM AUTHOR]

A data assimilation system for a high-resolution model has been developed to address the opportunities and challenges posed by the upcoming Surface Water and Ocean Topography (SWOT) satellite mission. This developed system is based on a three-dimensional variational data assimilation scheme (3DVAR), which is computationally highly efficient and thus can be applied to a very high-resolution model. A crucial consideration of the system is to use a multiscale data assimilation approach (MSDA) to first assimilate routinely available observations, including conventional satellite altimetry, sea surface temperature (SST) and salinity (SSS), and temperature/salinity vertical profiles, to constrain large scales and large mesoscales. High-resolution (dense) observations and future SWOT measurements can then be effectively and seamlessly assimilated to constrain the smaller scales. The 3DVAR is extended to assimilate observations over a time interval, which specifically enhances the efficacy of the assimilation of satellite along-track altimetry observations, which are limited by large repeat time intervals. Using this system, a reanalysis dataset was produced for the SWOT pre-launch field campaign that took place in the California Current System from September through December, 2019. An evaluation of this system with assimilated and withheld data demonstrates its ability to effectively utilize both routine and campaign observations to produce sea surface heights with the accuracy close to that required by SWOT. These results suggest a promising avenue for data assimilation development in the SWOT altimetry era, which will need the capability of jointly assimilating existing routine observations with SWOT measurements to resolve small-scale ocean processes. [ABSTRACT FROM AUTHOR]

We present a mapping methodology to improve the resolution of gridded sea surface height (SSH) from multisatellite along-track measurements, and apply it in the California Current system. This is motivated by the upcoming Surface Water and Ocean Topography (SWOT) satellite mission, which will collect novel high-resolution wide-swath SSH measurements. The calibration/validation of SWOT will be in the California Current and requires independent observations of SSH spatial variability. Our objective was to discover how far we could push the space--time resolution of SSH maps based on the existing altimetry constellation. Currently, the best SSH maps for the region are from AVISO's global product based on optimal interpolation (OI). Our methodology is based on 2-D variational analysis (2DVAR), and we address some limitations of the AVISO OI global parameterization that restrict the resolution. Key improvements are (1) use of a background field incorporating mesoscale information, as opposed to a long-term mean field; (2) optimized regional parameters; and (3) a representation error term in the observational error covariance matrix to account for the spread of measurements in time. Using a suite of independent remote and in situ observations, we show the 2DVAR method can produce higher-resolution SSH maps in the California Current region that successfully capture smaller-scale ocean features than AVISO, at the expense of an increase in noise. [ABSTRACT FROM AUTHOR]