# Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms
## 4 Discussion  
While our findings could be of general interest to a broad range of scientists, we expect them to stimulate further investigations and suggest new ways to efficiently combine Earth system data from multiple sensors and models. In fact, the adaptation and development of AI and deep-learning tools for Earth observation encompasses an ever-growing number of potential applications. We have tested here a novel approach that not only allows users to take advantage of neural network techniques for the combination of multi-sensor, remotely sensed data, but proposes an innovative way to merge satellite observations and numerical models, building an observation-based product that implicitly includes knowledge of the physics of the system in a way different from classical statistical interpolation or data assimilation. Figure 10: Performance of dADR-SR reconstruction of the geostrophic currents with respect to standard altimeter L4 data along drifter trajectories. The plots show the difference between the absolute error of altimetry and that of super-resolved currents, estimated vs the drifter velocities: (**A**) zonal component; (**B**) meridional component. Positive values indicate an improvement with respect to altimetry. We performed an observing system simulation experiment to test the applicability of computer vision algorithms (originally designed for single image super resolution) for the improved estimation of ocean absolute dynamic topography. Usually, OSSEs provide a controlled testing environment, targeted to better tune observing system design or to set up the retrieval algorithms for future satellite missions. Indeed, one interesting aspect of the experiment carried out here is that, while it will hopefully be possible in the future to train our super-resolution model directly from observations (as soon as SWOT, Harmony or SEASTAR measurements become available), it is already possible to apply it to super-resolve present altimeter products, provided the area and period under study are effectively covered by cloud-free/noise-free infrared images. In fact, we have identified a number of issues and aspects that will need further investigation and new developments. First of all, a dedicated effort would be needed to take into account the limitations of present, satellite-based SST L4 data, which are obtained through spatio-temporal statistical interpolation of eventually cloud-contaminated infrared images and thus provide uneven spatial spectral information (depending on the persistence of cloud cover and interpolation strategy adopted) [41]. One possible strategy to expand the applicability of algorithms based on super-resolution would thus be to also fully simulate SST interpolation and related error in an improved OSSE. This could be achieved by adding realistic data voids to model data and implementing the same algorithm used operationally for SST interpolation. The interpolated SST and related formal interpolation error could then be included as predictors rather than the original model SST. As an alternative, one could test the applicability of the model to non-interpolated SST data (also known as Level 3), starting already from the training phase. Moreover, this approach requires a specific OSSE and dedicated tests. One additional aspect that deserves to be mentioned is that we carried out our tests in the Mediterranean Sea, which is likely one of the most complex areas in terms of upper ocean dynamics (though cloud coverage is less critical than elsewhere) [62]. This is due to the small Rossby radius of deformation (i.e., the scale at which buoyancy and rotation effects are comparable, which set the characteristic size of dominant flow instabilities) and the occurrence of intense, small-scale air-sea interactions (modulated by highly variable orography) that drive complex coastal dynamics that can significantly affect the source/sink terms in the upper-ocean-temperature-evolution equation. This may undermine our working hypothesis that small scale changes are substantially dominated by advection.