CMEMS-Based Coastal Analyses: Conditioning, Coupling and Limits for Applications

Abstract

Recent advances in numerical modeling, satellite data, and coastal processes, together with the rapid evolution of CMEMS products and the increasing pressures on coastal zones, suggest the timeliness of extending such products toward the coast. The CEASELESS EU H2020 project combines Sentinel and in-situ data with high-resolution models to predict coastal hydrodynamics at a variety of scales, according to stakeholder requirements. These predictions explicitly introduce land discharges into coastal oceanography, addressing local conditioning, assimilation memory and anisotropic error metrics taking into account the limited size of coastal domains. This article presents and discusses the advances achieved by CEASELESS in exploring the performance of coastal models, considering model resolution and domain scales, and assessing error generation and propagation. The project has also evaluated how underlying model uncertainties can be treated to comply with stakeholder requirements for a variety of applications, from storm-induced risks to aquaculture, from renewable energy to water quality. This has led to the refinement of a set of demonstrative applications, supported by a software environment able to provide met-ocean data on demand. The article ends with some remarks on the scientific, technical and application limits for CMEMS-based coastal products and how these products may be used to drive the extension of CMEMS toward the coast, promoting a wider uptake of CMEMS-based predictions.

Coastal Adaptation and Uncertainties: The Need of Ethics for a Shared Coastal Future

“…Such coastal pathways must consider the possible chronicity of these factors, introducing unbiased and dynamic tipping points that are ethically defined with their uncertainties to promote a cross sectoral cooperation that covers the entire polity. This sustained cooperation, based on objective knowledge and multifarious benefits across scales and social groups, should apply the wealth of data (in situ and remote) and knowledge now available (Sánchez-Arcilla et al, 2021) as well as the recent advances in lab and numerical tools (e.g., Kim et al, 2020) to steer adaptation pathways, co-developed by all relevant stakeholders. Such cooperation should contribute to overcome the lack of connectivity in present coastal systems, with barriers to biophysical fluxes, socio-economic activities and even perception by different actors, resulting in disconnected coastal realities (Nicolodi et al, 2021) that favor inactivity and ethical conflicts.…”

“…Recent advances on models and observations (e.g., Sánchez-Arcilla et al, 2021) enable such an anticipatory approach which, however, may be hindered by vested conclusions and implicit error levels that hide a lack of ethics in the sense of promoting results that comply better with the interests of the more powerful stakeholders. Such a lack of ethical basis may erode the optimism necessary for anticipatory action, hampering the development of environmentally friendly and socially shared decisions, explaining why present societies balk at investments or interventions with mid/long-term benefits, and limited shortterm returns.…”

Coastal HF radars in the Mediterranean: status of operations and a framework for future development

“…Among the research challenges, integration must be achieved by building reinforced synergies between commercial developers, academic institutions, management agencies and state government offices for a coordinated creation of tailored products to support end-user communities. In this context, HFR-derived products should evolve towards finer spatiotemporal scales to improve the coastal ocean monitoring, in line with the announced CMEMS coastal extension (Sánchez-Arcilla et al, 2021), and thereby resolve adequately littoral (sub)mesoscale processes of paramount relevance. The accurate retrieval of HFR surface currents remains as a top priority since it is a prerequisite of the existing applications of this shorebased technology.…”

Data-Driven Interpolation of Sea Surface Suspended Concentrations Derived from Ocean Colour Remote Sensing Data

“…Operational ocean color products [13] mainly exploit optimal interpolation and objective analysis methods. These methods rely on the calibration of space-time covariance priors to represent the space-time variability of SSSC fields.…”