Abstract
The filtering procedure is usually mandatory for modeling mean dynamic topography (MDT) when a geodetic approach based on the Mean Sea Surface (MSS) and the Global Geopotential Model (GGM) is used. This is due to the inconsistent spectral contents between MSS and GGM. However, traditional isotropic filtering algorithms (e.g., Gaussian filter) consider neither the MDT locations nor their azimuth when smoothing the signal within the filtering radius. Hence, the isotropic filtering will attenuate the MDT signal near the current and filter the current signal into the surrounding ocean, which may lead to signal contamination and distortion. In this study, we set up a least squares-based (LS) approach to model MDT signal from the altimeter-derived MSS and geoid height using spherical harmonics from GGMs, where MDT is parameterized by Lagrange Basis Functions (LBFs). The design matrix is segmentally established, considering the error information of GGM in various spectral bands. Numerical experiments in the Gulf Stream show that applications of full error variance-covariance matrix or only diagonal error variance of GGM may have marginal effects on the MDT modeling. The MDT computed from this LS-based approach using the latest releases of Gravity Field and Steady-state Ocean Circulation Explorer (GOCE) geoid models, i.e., GO_CONS_GCF_2_DIR_R6 and Gravity Observation Combination 06s model (GOCO06s), have the best agreement with the comparison data, especially near the current region. Deduced geostrophic velocities based on the MDT solutions show that the LS-based approach recovers the current signal better than the Gaussian filtering by 1.8 cm/s. Estimated error map illustrates that errors are more concentrated near the coastal region.
Highlights
The mean dynamic topography (MDT) plays a very important role in ocean circulation, global climate change, and vertical datum unification (Le Traon et al, 2015; Woodworth et al, 2015)
The performance of the least squares-based (LS)-based MDT modeling approach is evaluated in the Gulf Stream area (Figure 1)
We focus on using an LS-based approach for MDT modeling to limit the usage of spatial filtering
Summary
The mean dynamic topography (MDT) plays a very important role in ocean circulation, global climate change, and vertical datum unification (Le Traon et al, 2015; Woodworth et al, 2015). With the operation of the Gravity Recovery And Climate Experiment (GRACE) mission (Swenson and Wahr, 2002; Tapley et al, 2004) launched in 2002 and GRACE Follow-on (GRACEFO) mission launched in 2018 (Flechtner et al, 2014; Landerer et al, 2020), especially the operation of the subsequent Gravity field and steady-state Ocean Circulation Explorer (GOCE) launched in 2009 (Drinkwater et al, 2003; Bock et al, 2014; Brockmann, 2014; Wu et al, 2017), the modeling accuracy and spatial resolution of the global gravity field have been improved unprecedentedly (Pail et al, 2010; Bruinsma et al, 2014; Tziavos et al, 2015). The GRACE and GRACE-FO missions have an initial orbit altitude of about 500 km and can accurately measure the long-wavelength of global gravity field signal, which made it possible for the first time to obtain reasonable MDT results relying solely on satellite gravity data (Vianna et al, 2007; Knudsen et al, 2011). The maximum expansion of the Global Geopotential Model (GGM) based on pure GRACE gravity data is d/o ~200 (Tapley et al, 2003; Mayer-Gürr et al, 2014; Kvas et al, 2019a), and due to the complementarity of gravity signal from the two gravity satellite missions, the pure satellite GGM integrating data from GRACE/GOCE can be expanded to d/o 300, corresponding to a spatial resolution of about 70 km
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