Optimizing multi-platform sampling strategies to anticipate SWOT validation

Abstract:

Funded by the European Commission, the H2020 EuroSea project has the objective to improve the European ocean observing system as an integrated entity within a global context, delivering ocean observations and forecasts to advance scientific knowledge about ocean climate, marine ecosystems, and their vulnerability to human impacts and to demonstrate the importance of the ocean to an economically viable and healthy society. In the framework of this project, our goal is to improve the design of multi-platform in situ experiments for validation of high-resolution SWOT observations with the aim of optimizing the utility of these observing platforms. To achieve this goal, a set of Observing System Simulation Experiments (OSSEs) will be developed to evaluate different sampling strategies and their impact on the reconstruction of fine-scale sea surface height fields and currents. Observations from CTD, ADCP, gliders, and altimetry will be simulated from three nature run models to study the sensitivity of the results to the model used. Different sampling strategies will be evaluated to analyze the impact of the spatial and temporal resolution of the observations, the depth of the measurements, the season of the multi-platform experiment, and the impact of changing rosette CTD casts for a continuous underway CTD, and adding gliders. After generating the simulated observations in different scenarios, three methods of reconstruction will be tested: multivariate reconstruction analysis, machine-learning techniques, and modelling data assimilation. To assess the best sampling strategies to validate SWOT observations during the fast-sampling phase, the reconstructed fields will be compared to (i) the ocean “truth” from the nature run models, (ii) simulated SWOT observations, and (iii) simulated observations of drifters, Argo buoys and moorings. The regions of study are the western Mediterranean Sea and the northwestern Atlantic Ocean.