# Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms
Indeed, to discover the laws governing Earth system processes and better predict their evolution over several spatial and temporal scales, a combination of precise observations and theoretical/numerical models is needed [2]. In fact, even considering the significant increase in the number of acquisitions by remote sensing platforms and autonomous instruments, it will never be possible to describe and predict the state of the Earth system at all scales (or even of just one of its subsystems, such as the ocean) only through observed data. Satellite observations over the ocean, for example, only measure surface properties, with distinct temporal sampling and coverage depending on the sensor and mission. Conversely, information on the vertical distribution of properties along the water column can only be provided through in situ sensors that clearly lack the ability to simultaneously provide large spatial coverage and high space-time resolution. Empirical/statistical methodologies and historical data are thus often used to interpolate or reconstruct approximated 2D or 3D descriptions of ocean dynamics from a limited number of observed state variables, with poor to no physically driven constraints (e.g., [3]). On the other hand, full descriptions of the ocean state evolution (over a predefined set of scales) can be obtained through numerical models, still requiring a prior knowledge/guess of the initial state and of the forcings over time, as well as the parameterization of sub-grid physics. Due to the uncertainties in the initial conditions and parameterizations and the non-linearity of the dynamics, model predictions easily drift away from what is seen in the observations, unless observations are ingested within the simulation itself through data assimilation (DA), e.g., [4]. At present, DA is mostly based on probabilistic approaches, and it is also not rigorously tractable due to the huge number of variables and nonlinear processes involved, as well as the difficulty in simultaneously and properly characterizing model and observation errors. Despite some scepticism due to the generally limited interpretability and explainability of complex neural networks, deep learning methods are perfect candidates to cope with the high-dimensional spaces, multiple processes, non-linear relations and noisy data involved in Earth system observations and models [5]. While it is beyond our scope to provide a comprehensive list of the ever-growing applications of AI algorithms to Earth system science, it is worth citing some of the most relevant objectives, which span from hybrid modelling approaches, such as the development of new sub-grid-scale parameterizations [6; 7] and the improvement of DA techniques to be used in classical numerical circulation models [8; 9], to the downscaling of low resolution models [10], to supervised learning approaches for data augmentation, filtering, interpolation or prediction [11; 12; 13; 14; 15; 16; 17; 18], to the detection of dynamical features [19], to the set-up of neural networks for partial differential equation solution/identification and modelling of latent dynamics, e.g., [20; 21; 22; 23; 24]. Indeed, whenever sufficient information is available, physically informed neural network models can be designed to explicitly include physical constraints, for example, by building custom loss-functions that enforce the structure of the network to obey a known governing equation (through automatic differentiation), and/or by exploiting the similarities between residual networks and the numerical schemes used to integrate the ordinary differential equations that govern dynamical systems. However, sometimes sequential observations can merely resolve the large-scale dynamics, as high-resolution spatial snapshots are available only episodically and provide only limited information to describe (and directly learn) the evolution of small-scale processes.