Mapping altimetry in the forthcoming SWOT era by back-and-forth nudging a one-layer quasi-geostrophic model

Abstract

During the past 25 years, altimetric observations of the ocean surface from space have been mapped to provide two dimensional sea surface height (SSH) fields that are crucial for scientific research and operational applications. The SSH fields can be reconstructed from conventional altimetric data using temporal and spatial interpolation. For instance, the standard Developing Use of Altimetry for Climate Studies (DUACS) products are created with an optimal interpolation method that is effective for both low temporal and low spatial resolution. However, the upcoming nextgeneration SWOT mission will provide very high spatial resolution but with low temporal resolution. The present paper makes the case that this temporal-spatial discrepancy induces the need for new advanced mapping techniques involving information on the ocean dynamics. An algorithm is introduced, dubbed the BFN-QG, that uses a simple data assimilation method, the back-and-forth nudging (BNF), to interpolate altimetric data while respecting quasigeostrophic (QG) dynamics. The BFN-QG is tested in an observing system simulation experiments and compared to the DUACS products. The experiments consider as reference the high-resolution numerical model simulation NATL60 from which are produced realistic data: four conventional altimetric nadirs and SWOT data. In a combined nadirs and SWOT scenario, the BFN-QG substantially improves the mapping by reducing the root-mean-square errors and increasing the spectral effective resolution by 40 km. Also, the BFN-QG method can be adapted to combine large-scale corrections from nadir data and small-scale corrections from SWOT data so as to reduce the impact of SWOT correlated noises and still provide accurate SSH maps.

Unsupervised Learning of Sea Surface Height Interpolation from Multi-variate Simulated Satellite

“…• bfn (Le Guillou et al, 2020) : a data assimilation method that performs a back and forward nudging of a surface quasi-geostrophic model.…”

“…• λ x (in degrees) and λ t (in days) are two spectral metrics, introduced by (Le Guillou et al, 2020). We compute respectively the spatial and temporal power spectrum of the error, λ x is then the smallest spatial wavelength where the power spectrum of the error is equal to the power spectrum of the signal and λ t its temporal equivalent.…”

Learning Sea Surface Height Interpolation From Multi‐Variate Simulated Satellite Observations

“…• μ and σ t (in cm), are respectively the RMSE of the SSH and the temporal standard deviation of this RMSE. In the data challenge, these two metrics are normalized by the root mean square of the SSH, but we prefer giving it in centimeters to be coherent with the rest of the work; • λ x (in degrees) and λ t (in days) are two spectral metrics, introduced by Le Guillou et al (2020). We compute respectively the spatial and temporal power spectrum of the error, λ x is then the smallest spatial wavelength where the power spectrum of the error is equal to the power spectrum of the signal and λ t its temporal equivalent.…”

4DVarNet-SSH: end-to-end learning of variational interpolation schemes for nadir and wide-swath satellite altimetry

“…Ngodock et al (2015), Benkiran et al (2021) or Li et al (2021). Previous works have also considered quasigeostrophic (QG) dynamics as an approximate and reduced-order dynamical prior for sea surface dynamics, leading to stateof-the-art performance (Ubelmann et al, 2016;Le Guillou et al, 2020). Overall, BOOST-SWOT 2020 data challenge (https:…”

“…Ngodock et al (2015), Benkiran et al (2021) or Li et al (2021). Previous works have also considered quasigeostrophic (QG) dynamics as an approximate and reduced-order dynamical prior for sea surface dynamics, leading to state-of-the-art performance (Ubelmann et al, 2016;Le Guillou et al, 2020). Overall, BOOST-SWOT 2020 data challenge (https://github.com/ocean-data-challenges/2020aSSHmappingNATL60) provides a representative benchmarking framework to assess the performance of SSH mapping schemes for nadir-only and nadir+SWOT altimetry datasets.…”