Abstract's details
Reconstruction of the surface ocean topography and associated dynamics using image data assimilation in the prospect of the SWOT mission
Event: 2017 Ocean Surface Topography Science Team Meeting
Session: Application development for Operations
Presentation type: Type Poster
Contribution: not provided
Abstract:
Reconstruction of ocean surface topography is a continuing challenge that requires enrichment of the methods according to the new capacities allowed by present (along-track) and future (wide-swath) altimetry missions, especially in the range of large wavenumber of the power spectrum. For a variety of applications, it is also key to reconstruct sub-surface information (i.e. vertical velocities) consistently with surface topography and horizontal currents, with quantified error estimates.
In the prospect of the SWOT mission, we present a new approach to reconstruct the dynamics of the upper ocean as accurately as possible, using image data assimilation to extract meso- and submesoscale information from the high-resolution scenes that will be captured by future altimetric constellations. It is based on a two-step analysis scheme that combines a reduced-order Gaussian observational update, and a non-Gaussian observational updates to adjust the fine-scale using Lyapunov exponents associated to the structure of the flow (Duran et al., 2016).
A probability distribution of the first guess is defined and updated at each step of the analysis: (i) the first step applies the analysis scheme of a SEEK-type filter to update the first guess probability distribution using SSH observation; (ii) the second step minimizes a cost function using observations of HR image structure and a new probability distribution is estimated. The analysis is extended to the vertical dimension using 3D multivariate empirical orthogonal functions (EOFs) and the probabilistic approach allows the update of the probability distribution through the two-step analysis.
Using simulated data from high-resolution (1/36°) simulations of the circulation in the Solomon Sea, the performance of the method is analysed comparing several observation scenarios that combine orbital characteristics of the Jason, Envisat, AltiKA and SWOT missions. Using the SWOT simulator to generate the noisy data sets to be tested, it is shown that specific algorithms (Ruggiero et al., 2016) are needed to take into account the correlated errors inherent to SWOT measurements.
Further applications explored in the frame of the MOMOMS OSTST proposal based on very-high resolution simulations (NATL60) of the circulation in the midlatitudes, will be illustrated and discussed.
Reference:
M.Duran-Moro, Brankart J.-M., Brasseur P. and Verron J., 2017: Exploring image data assimilation in the prospect of high-resolution satellite ocean observations, Ocean Dyn., 67(7), 875-895, 10.1007/s10236-017-1062-3.
Ruggiero, G. A., Cosme, E., Brankart, J. M., Le Sommer, J., & Ubelmann, C. (2016). An Efficient Way to Account for Observation Error Correlations in the Assimilation of Data from the Future SWOT High-Resolution Altimeter Mission. Journal Of Atmospheric And Oceanic Technology, 33(12), 2755–2768.
In the prospect of the SWOT mission, we present a new approach to reconstruct the dynamics of the upper ocean as accurately as possible, using image data assimilation to extract meso- and submesoscale information from the high-resolution scenes that will be captured by future altimetric constellations. It is based on a two-step analysis scheme that combines a reduced-order Gaussian observational update, and a non-Gaussian observational updates to adjust the fine-scale using Lyapunov exponents associated to the structure of the flow (Duran et al., 2016).
A probability distribution of the first guess is defined and updated at each step of the analysis: (i) the first step applies the analysis scheme of a SEEK-type filter to update the first guess probability distribution using SSH observation; (ii) the second step minimizes a cost function using observations of HR image structure and a new probability distribution is estimated. The analysis is extended to the vertical dimension using 3D multivariate empirical orthogonal functions (EOFs) and the probabilistic approach allows the update of the probability distribution through the two-step analysis.
Using simulated data from high-resolution (1/36°) simulations of the circulation in the Solomon Sea, the performance of the method is analysed comparing several observation scenarios that combine orbital characteristics of the Jason, Envisat, AltiKA and SWOT missions. Using the SWOT simulator to generate the noisy data sets to be tested, it is shown that specific algorithms (Ruggiero et al., 2016) are needed to take into account the correlated errors inherent to SWOT measurements.
Further applications explored in the frame of the MOMOMS OSTST proposal based on very-high resolution simulations (NATL60) of the circulation in the midlatitudes, will be illustrated and discussed.
Reference:
M.Duran-Moro, Brankart J.-M., Brasseur P. and Verron J., 2017: Exploring image data assimilation in the prospect of high-resolution satellite ocean observations, Ocean Dyn., 67(7), 875-895, 10.1007/s10236-017-1062-3.
Ruggiero, G. A., Cosme, E., Brankart, J. M., Le Sommer, J., & Ubelmann, C. (2016). An Efficient Way to Account for Observation Error Correlations in the Assimilation of Data from the Future SWOT High-Resolution Altimeter Mission. Journal Of Atmospheric And Oceanic Technology, 33(12), 2755–2768.