Assessing the Impact of the Assimilation of SWOT Observations in a Global High-Resolution Analysis and Forecasting System Part 1: Methods

Abstract

The future Surface Water Ocean Topography (SWOT) mission due to be launched in 2022 will extend the capability of existing nadir altimeters to enable two-dimensional mapping at a much higher effective resolution. A significant challenge will be to assimilate this kind of data in high-resolution models. In this context, Observing System Simulation Experiments (OSSEs) have been performed to assess the impact of SWOT on the Mercator Ocean and Copernicus Marine Environment Monitoring Service (CMEMS) global, high-resolution analysis and forecasting system. This paper focusses on the design of these OSSEs, in terms of simulated observations and assimilation systems (ocean model and data assimilation schemes). The main results are discussed in a companion paper. Two main updates of the current Mercator Ocean data assimilation scheme have been made to improve the assimilation of information from SWOT data. The first one is related to a different parametrisation of the model error covariance, and the second to the use of a four-dimensional (4D) version of the data assimilation scheme. These improvements are described in detail and their contribution is quantified. The Nature Run (NR) used to represent the “truth ocean” is validated by comparing it with altimeter observations, and is then used to simulate pseudo-observations required for the OSSEs. Finally, the design of the OSSEs is evaluated by ensuring that the differences between the assimilation system and the NR are statistically consistent with the misfits between real ocean observations and real-time operational systems.

Assessing the Impact of the Assimilation of SWOT Observations in a Global High-Resolution Analysis and Forecasting System – Part 2: Results

“…Another important aspect is the use of a four-dimensional (4D) version of the filter, i.e., the analysis uses a 4D subspace and produces daily increments (model corrections) of SSH, temperature and salinity (T/S), and zonal and meridional velocities (U/V). The 4D aspect improved the temporal and spatial representation of the forecast error (Benkiran et al, 2021). This fact is most significant for periods shorter than 20 days and longer than 7 days.…”

“…SAM implements a reduced-order local Kalman Filter for which the analysis subspace is constructed using a band-passed time series of model states from a climatological run. In part 1 of this article (Benkiran et al, 2021), the parameters of the bandpass filtering were revisited to improve the representation of mesoscale (100-400 km) signals. The new set of anomalies has a flatter spectrum that is more coherent with the forecast errors.…”

“…Therefore, one assimilation cycle comprises a model forecast, an analysis step where corrections are calculated, and the analysis trajectory, which is the model integration using the IAU over the same time window as the forecast. A complete description of the assimilation method and the impact of the 4D analysis scheme is reported in the companion article (Benkiran et al, 2021).…”

“…For this study, the MO data assimilation system used operationally for a current real-time forecasting system (Lellouche et al, 2018), had to be modified. The main updates of the assimilation scheme, the choice of NR (including its validation) and AR, as well as the design and calibration of the OSSEs have been described in detail in a companion article (Benkiran et al, 2021). In the present article, results from these experiments are analyzed to evaluate the impact of SWOT data assimilation on several parameters: SSH, temperature, salinity, and horizontal velocity fields.…”

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A first attempt was made to quantify the impact of the assimilation of Surface Water Ocean Topography (SWOT) swath altimeter data in a global 1/12 • high resolution analysis and forecasting system through a series of Observing System Simulation Experiments (OSSEs). The OSSE framework (Nature Run and Free Run) and data assimilation scheme have been described in detail in a companion article (Benkiran et al, 2021). The impact of assimilating data from SWOT and three nadir altimeters was quantified by estimating analysis and forecast error variances for sea surface height (SSH), temperature, salinity, zonal, and meridional velocities.

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