# Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms
## 3 Results
### Testing Single-Image, Super-Resolution Configurations and Designing a Multi-Scale Adaptive Model
(**A**) The dADR-SR network architecture; dADR-SR is based on the inclusion of dilated, convolution-based learning inception modules in the core layers of ADR-SR. (**B,C**) Relative performance of the dADR-SR reconstructions, assessed on the independent test data as the difference between the RMSD between the altimeter-like and original model output and the RMSD between the super-resolved ADT and the original model ADT (red indicates smaller RMSD from model predictions). The panels show the performance of the models trained/tested considering: (**B**) dADR-DR and the full set of predictor variables; (**C**) dADR-SR removing \(\Delta\)ADT from the predictors. ADT and related RMSD values are expressed in m.  
Applying Dilated Adaptive Residual Super-Resolution Trained on Simulated Data to Real Satellite Observations  
Our successive analysis is aimed to verify to what extent we can use the features learned from observing system simulations based on primitive equation modelling to improve present-day, data-driven reconstructions, but also to identify eventual limitations of the present OSSE set up and issues related to real observation-based products. We have thus applied the model trained on OSSE data to predict high-resolution ADT starting from properly pre-processed, satellite-altimeter-based, low-resolution ADT and high-resolution SST products (e.g., Figure 6). For both variables, we have actually taken optimally interpolated data (also known as Level 4 (L4), see Section 2), covering the 2008-2019 period. Assessing the accuracy of the observation-based, super-resolved maps is not trivial, as large-coverage, high-resolution observations of the surface topography/currents are not presently available. As such we followed a double approach: on the one hand, we performed a qualitative analysis of the reconstructed patterns, looking at areas of intense mesoscale activity visible in the satellite SST L4 data and comparing the (sub)mesoscale eddies identified by the AMEDA automatic detection algorithm in original and super-resolved data; on the other hand, we built a match-up database with the surface current estimates provided by surface drifters (see Section 2) and used it to compute the statistics of the differences with respect to the geostrophic currents estimated from standard altimeter ADT and super-resolved ADT field. The dADR-SR model reveals impressive potential to resolve mesoscale turbulent features that are generally smeared out, often misplaced or even totally missed by standard altimetry, when clear-sky thermal data are present. Figure 7 presents a wonderful example of such a turbulent field, with many mesoscale eddies, dipoles and current meanderings well visible, especially in the western Mediterranean Basin, detaching from the Algerian current towards the centre of the basin along the North Balearic front and Liguro-Provencal current. Surface geostrophic currents estimated from the dADR-SR ADT (Figure 6B) not only appear much sharper than those depicted by low-resolution altimetric data (Figure 6A) but prove also able to reconstruct dynamical features that were completely absent in the standard product (Figure 7). Specifically, dADR-SR recovers the strong cyclonic eddies associated with two mushroom-like dipoles along the Algerian coast, marked with the letters \"A\" and \"B\" in the zoomed panels of Figure 7, displaying much more consistent shapes and intensities. The cyclonic circulation in (B) is actually described as two eddies by the AMEDA eddy contours estimated from the super-resolved field, which is much more consistent with the  
Figure 5: The dADR-SR model performance compared to simulated standard altimetry. (**A**) RMSD between the altimeter-like and original model output and (**B**) RMSD between the super-resolved absolute dynamic topography (ADT) obtained with dADR-SR (using full predictors set) and the original model ADT. ADT and related RMSD values are expressed in m.  
SST patterns, while a single and almost rectangular shape is found in standard altimeter estimates.