The increase in emissions associated with aviation requires deeper research into novel sensing and flow-control strategies to obtain improved aerodynamic performances. In this context, data-driven methods are suitable for exploring new approaches to control the flow and develop more efficient strategies. Deep artificial neural networks (ANNs) used together with reinforcement learning, i.e., deep reinforcement learning (DRL), are receiving more attention due to their capabilities of controlling complex problems in multiple areas. In particular, these techniques have been recently used to solve problems related to flow control. In this work, an ANN trained through a DRL agent, coupled with the numerical solver Alya, is used to perform active flow control. The Tensorforce library was used to apply DRL to the simulated flow. Two-dimensional simulations of the flow around a cylinder were conducted and an active control based on two jets located on the walls of the cylinder was considered. By gathering information from the flow surrounding the cylinder, the ANN agent is able to learn through proximal policy optimization (PPO) effective control strategies for the jets, leading to a significant drag reduction. Furthermore, the agent needs to account for the coupled effects of the friction- and pressure-drag components, as well as the interaction between the two boundary layers on both sides of the cylinder and the wake. In the present work, a Reynolds number range beyond those previously considered was studied and compared with results obtained using classical flow-control methods. Significantly different forms of nature in the control strategies were identified by the DRL as the Reynolds number $Re$ increased. On the one hand, for $Re\le 1000$, the classical control strategy based on an opposition control relative to the wake oscillation was obtained. On the other hand, for $Re=2000$, the new strategy consisted of energization of the boundary layers and the separation area, which modulated the flow separation and reduced the drag in a fashion similar to that of the drag crisis, through a high-frequency actuation. A cross-application of agents was performed for a flow at $Re=2000$, obtaining similar results in terms of the drag reduction with the agents trained at $Re=1000$ and 2000. The fact that two different strategies yielded the same performance made us question whether this Reynolds number regime ($Re=2000$) belongs to a transition towards a nature-different flow, which would only admits a high-frequency actuation strategy to obtain the drag reduction. At the same time, this finding allows for the application of ANNs trained at lower Reynolds numbers, but are comparable in nature, saving computational resources.

The Mediterranean Forecasting System produces operational analyses and reanalyses and 10 d forecasts for many essential ocean variables (EOVs), from currents, temperature, salinity, and sea level to wind waves and pelagic biogeochemistry. The products are available at a horizontal resolution of $\mathrm{1}/\mathrm{24}$ ^∘ (approximately 4 km) and with 141 unevenly spaced vertical levels. The core of the Mediterranean Forecasting System is constituted by the physical (PHY), the biogeochemical (BIO), and the wave (WAV) components, consisting of both numerical models and data assimilation modules. The three components together constitute the so-called Mediterranean Monitoring and Forecasting Center (Med-MFC) of the Copernicus Marine Service. Daily 10 d forecasts and analyses are produced by the PHY, BIO, and WAV operational systems, while reanalyses are produced every ∼ 3 years for the past 30 years and are extended (yearly). The modelling systems, their coupling strategy, and their evolutions are illustrated in detail. For the first time, the quality of the products is documented in terms of skill metrics evaluated over a common 3-year period (2018–2020), giving the first complete assessment of uncertainties for all the Mediterranean environmental variable analyses.

Active flow control strategies for three-dimensional bluff bodies are challenging to design, yet critical for industrial applications. Here we explore the potential of discovering novel drag-reduction strategies using deep reinforcement learning. We introduce a high-dimensional active flow control setup on a three-dimensional cylinder at Reynolds numbers (R e _D ) from 100 to 400, spanning the transition to three-dimensional wake instabilities. The setup involves multiple zero-net-mass-flux jets and couples a computational fluid dynamics solver with a numerical multi-agent reinforcement learning framework based on the proximal policy optimization algorithm. Our results demonstrate up to 16% drag reduction at R e _D = 400, outperforming classical periodic control strategies. A proper orthogonal decomposition analysis reveals that the control leads to a stabilized wake structure with an elongated recirculation bubble. These findings represent the first demonstration of training on three-dimensional cylinders and pave the way toward active flow control of complex turbulent flows.

A radiative transfer model was parameterized and validated using Biogeochemical Argo float data acquired between 2012 and 2017 across the Mediterranean Sea. Fluorescence-derived chlorophyll a concentration, particulate optical backscattering at 700 nm and fluorescence of chromophoric dissolved organic matter were used to parametrize the light absorption and scattering coefficients of the optically significant water constituents (pure water, non-algal particles, chromophoric dissolved organic matter and phytoplankton). The model was validated with in-situ downwelling irradiance profiles and apparent optical properties derived both from irradiance profiles and satellite data, such as the diffuse attenuation coefficients and remote sensing reflectance. Results showed that by using regional parameterizations that are not only related to chlorophyll concentration and vertical distribution, the model was able to capture a more accurate spectral response in the examined wavelength range compared to chlorophyll-related (or Case 1) optical models. When using alternative models that incorporated also measurements of chromophoric dissolved organic matter fluorescence or particulate optical backscattering, the model skill increased at all examined wavelengths. Finally, using a multi-spectral optical configuration also enabled the estimation of the relative contribution of separate water constituents in the examined spectral range. Simulations including non-algal particles and chromophoric dissolved organic matter performed up to 61% and 79% better than when considering the optical properties of pure seawater alone. Moreover, a simulation including phytoplankton light absorption resulted in an error reduction of up to 42%, especially at 412 nm and with a more uniform response at the wavelengths considered.