DOI: 10.5281/zenodo.6798017
title: Dataset generated to evaluate in situ sampling strategies to reconstruct fine-scale ocean currents in the context of SWOT satellite mission (H2020 EuroSea project)

description: <strong>Dataset generated in Subtask 2.3.1 of the H2020 EuroSea project.</strong> <em>H2020 EuroSea project:</em><br> The H2020 EuroSea project aims at improving and integrating the European Ocean Observing and Forecasting System (see official website: https://eurosea.eu/). It has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862626). <em>Task 2.3:</em><br> Task 2.3 has the objective to improve the design of multi-platform experiments aimed to validate the Surface Water and Ocean Topography (SWOT) satellite observations with the goal to optimize the utility of these observing platforms. Observing System Simulation Experiments (OSSEs) have been conducted to evaluate different configurations of the in situ observing system, including rosette and underway CTD, gliders, conventional satellite nadir altimetry and velocities from drifters. High-resolution models have been used to simulate the observations and to represent the “ocean truth”. Several methods of reconstruction have been tested: spatio-temporal optimal interpolation, machine-learning techniques, model data assimilation and the MIOST tool. The planned OSSEs are detailed in this public report Barceló-Llull et al. (2020) and the complete analysis is available here Barceló-Llull et al. (2022). Contributors to Task 2.3 are CSIC (Spain), CLS (France), SOCIB (Spain), IMT-Atlantique (France) and Ocean-Next (France). <em>Subtask 2.3.1:</em><br> Subtask 2.3.1 aims to evaluate different in situ sampling strategies to reconstruct fine-scale ocean currents (~20 km) in the context of SWOT. An advanced version of the classic optimal interpolation used in field experiments, which considers the spatial and temporal variability of the observations, has been applied to reconstruct different configurations with the objective to evaluate the best sampling strategy to validate SWOT. <em>Where?</em><br> The analysis focuses on two regions of interest: (i) the western Mediterranean Sea and (ii) the Subpolar North West Atlantic. In the western Mediterranean Sea, the target area is located within a swath of SWOT, while in the North West Atlantic the region of study includes a crossover of SWOT during the fast-sampling phase. <strong>Report with the full analysis</strong> The complete analysis can be found in this report: Barceló-Llull et al. (2022). <strong>Codes for the analysis</strong> The codes generated to develop Subtask 2.3.1 can be found on GitHub: https://github.com/bbarcelollull/EuroSea_subTask_2.3.1 <strong>The dataset</strong> The dataset includes: 1) Model outputs used to simulate the observations in different configurations in both regions of study. The folder "2D_model_outputs" contains 2D data used to simulate SSH observations for the analysis of the temporal correlation scale (Barceló-Llull et al., 2022, p. 28-42). The folder "3D_model_outputs" contains 3D model outputs used to simulate observations of temperature and salinity. Note that eNATL60 outputs have been interpolated onto a new regular grid. 2) Simulated configurations (or sampling strategies) in each region (PKL file format). 3) Observations simulated in each configuration in both regions of study. The observations simulated are temperature and salinity. ADCP horizontal velocities are also simulated, however for eNATL60 they will be corrected in the future to account for the rotated original axes. File format: region_configuration_period_model.nc. The folder "SSH" includes the simulated SSH observations for the analysis of the temporal correlation scale (Barceló-Llull et al., 2022, p. 28-42). 4) Reconstructed fields with the spatio-temporal optimal interpolation. File format: region_configuration_period_model_stOI_Lx_Lt_cd_YYYYMMDDhhmm_var.nc (stOI = spatio-temporal optimal interpolation, Lx = spatial correlation scale, Lt = temporal correlation scale, cd = map on the central date of the sampling, YYYYMMDDhhmm = date and time of the map, var = variable interpolated (temperature and salinity) or the derived variables (dynamic height, geostrophic velocities and the Rossby number)). 5) Compared fields (ocean truth from model outputs vs. reconstructed fields) for each region and model (PKL file format).
description: {"references": ["Barcel\u00f3-Llull, B., Pascual, A., Cutolo, E., Fablet, R., Gasparin, F., Guinehut, S., Hern\u00e1ndez-Lasheras, J., Leroux, S., Mignot, A., Mourre, B., Mulet, S., R\u00e9my, E., Speich, S. & Verbrugge, N. (2020). Design of the Observing System Simulation Experiments with multi-platform in situ data and impact on fine-scale structures. EuroSea project, Deliverable 2.1, DOI: https://doi.org/10.3289/eurosea_d2.1", "Barcel\u00f3-Llull, B., Pascual, A., Albert, A., Beauchamp, M., Fablet, R., Guinehut, S., Hern\u00e1ndez-Lasheras, J., Herrero Gonz\u00e1lez, M., Jousset, S., Leroux, S., Mourre, B. & Mulet, S. (2022). Analysis of the OSSEs with multi-platform in situ data and impact on fine-scale structures. EuroSea project, Deliverable 2.3, DOI: https: //doi.org/10.3289/eurosea_d2.3", "Barcel\u00f3-Llull, B. (2022). Codes to design in situ sampling strategies. GitHub, https://github.com/bbarcelollull/EuroSea_subTask_2.3.1"]}