This dataset gathered the trajectories of 161 Lagrangian surface drifters that were deployed in the Western Mediterranean sea in 2023 by three campaigns of the SWOT Adopt-A-Crossover Consortium: C-SWOT-2023, BioSWOT-Med and FaSt-SWOT. Drifter trajectories are available between March 27th 2023 and January 22th 2024. The deployment strategy involved releasing drifters to target specific mesoscale and submesoscale structures in the vicinity of selected SWOT passes. These structures were identified using SPASSO software, which combined near-real-time remote data from COPERNICUS (DUACS) and early SWOT data provided by CLS/CNES. Several drifter designs are used in these experiments : SVP drifters drogued at 15m, 50m, and 100m; SVP-B drifters at 15m depth; a customized BGC-SVP drifter drogued at 15m and equipped with additional sensors such as a CTD (for temperature and salinity) and an optical triplet measuring biochemical properties of sea; surface drifters such as CODE, CARTHE, HEREON type with drogue within the first meter depth; and SPOTTER, MELODI-Eodyn devices as wave drifters. The original nominal sampling rates range from 5 minutes to 1 hour. Drifters were deployed in the passes 3 and 16 of SWOT orbit during its fast-sampling (cal-val) phase (1-day revisit until July 10th) and some of the drifters further crossed the satellite ground-tracks afterwords, when the satellite science orbit was set to 21 days. This dataset is a collaborative effort between the SWOT-AdAC consortium and FaSt-SWOT, BioSWOT-Med and C-SWOT cruises. To provide a single interoperable dataset, all drifter trajectories from the different campaigns were processed with the same scripts in a similar manner, resulting in three distinct levels of processing. L0 – Harmonised and preprocessed trajectoriesAll initial trajectories are merged into a single dataset with variables renamed to match database standards. The following steps are applied: removing rows with missing date/time, ordering by ascending time, trimming to valid deployment/recovery periods, dropping rows with missing values, eliminating duplicates, removing rows with repeated times but different positions, and excluding rows with erroneous latitude/longitude (e.g., outliers outside the Mediterranean Sea). L1 – Processed trajectoriesL1 trajectories are filtered based on acceleration. Velocity and acceleration are calculated at each timestep, and positions with accelerations exceeding 4 times the interquartile range (IQR) are removed. This results in irregularly spaced trajectories that retain the original gps positions and therefore the overall current dynamics signal with its multiscale components but exclude gps fix outliers as defined above.L2 – Smoothed and regularly interpolated trajectoriesL2-trajectories are obtained from the L1-trajectories, that are regularly interpolated and smoothed in order to reduce noise, especially on acceleration. Two methods are used: the LOWESS method (inspired by Elipot et al. 2016) and a variational method developed by M. Demol and A. Ponte (inspired by Yaremchuk and Coelho, 2014). L2 trajectories are available with time steps of 10 minutes, 30 minutes, or 1 hour. For more details on the smoothing and interpolating processing, please refer to the attached PDF.Data export in NetCDF formatEach drifter trajectory is stored in eight separate NetCDF files, organised into eight distinct folders based on the processing stage and temporal resolution. For a given drifter, the following files are available :l0_data/BIOSWOT_CARTHE_4388553.ncl1_data/BIOSWOT_CARTHE_4388553.ncl2_data_variational_10min/BIOSWOT_CARTHE_4388553.ncl2_data_variational_30min/BIOSWOT_CARTHE_4388553.ncl2_data_variational_1hour/BIOSWOT_CARTHE_4388553.ncl2_data_lowess_10min/BIOSWOT_CARTHE_4388553.ncl2_data_lowess_30min/BIOSWOT_CARTHE_4388553.ncl2_data_lowess_1hour/BIOSWOT_CARTHE_4388553.ncContact list : Maristella Berta ([email protected]), Margot Demol ([email protected]), Laura Gómez Navarro ([email protected]) and Lloyd Izard ([email protected])PIs contact for the different involved projects: Bio-SWOT-Med  Andrea Doglioli ([email protected]); C-SWOT Pierre Garreau ([email protected]), Franck Dumas ([email protected]) and Aurélien Ponte ([email protected]); FaSt-SWOT: Ananda Pascual ([email protected]) and Baptiste Mourre ([email protected]).ReferencesDavis, Russ E. “Drifter Observations of Coastal Surface Currents during CODE: The Method and Descriptive View.” Journal of Geophysical Research: Oceans 90, no. C3 (1985): 4741–55. https://doi.org/10.1029/JC090iC03p04741.Elipot, Shane, Rick Lumpkin, Renellys C Perez, Jonathan M Lilly, Jeffrey J Early, and Adam M Sykulski. “A Global Surface Drifter Data Set at Hourly Resolution.” Journal of Geophysical Research: Oceans 121, no. 5 (2016): 2937–66.Horstmann, Jochen, Ruben Carrasco, Paulo H. R. Calil, Daniele Iudicone, Stephane Pesant, and J. M. Erta. “Drifter Position Data from the Eastern Coast of Brazil during the Project Mission Microbiomes with the RV Tara.” Dataset publication series. PANGAEA, 2022. https://doi.org/10.1594/PANGAEA.948261.Lumpkin, Rick, and Mayra Pazos. “Measuring Surface Currents with Surface Velocity Program Drifters: The Instrument, Its Data, and Some Recent Results.” In Lagrangian Analysis and Prediction of Coastal and Ocean Dynamics, edited by Annalisa Griffa, A. D. Kirwan, Jr., Arthur J. Mariano, Tamay Özgökmen, and H. Thomas Rossby, 1st ed., 39–67. Cambridge University Press, 2007. https://doi.org/10.1017/CBO9780511535901.003.Novelli, Guillaume, Cédric M. Guigand, Michel C. Boufadel, and Tamay M. Özgökmen. “On the Transport and Landfall of Marine Oil Spills, Laboratory and Field Observations.” Marine Pollution Bulletin 150 (January 1, 2020): 110805. https://doi.org/10.1016/j.marpolbul.2019.110805.Novelli, Guillaume, Cédric M. Guigand, Charles Cousin, Edward H. Ryan, Nathan J. M. Laxague, Hanjing Dai, Brian K. Haus, and Tamay M. Özgökmen. “A Biodegradable Surface Drifter for Ocean Sampling on a Massive Scale.” Journal of Atmospheric and Oceanic Technology 34, no. 11 (November 1, 2017): 2509–32. https://doi.org/10.1175/JTECH-D-17-0055.1.Novelli, Guillaume, Cédric M. Guigand, and Tamay M. Özgökmen. “Technological Advances in Drifters for Oil Transport Studies.” Marine Technology Society Journal 52, no. 6 (November 1, 2018): 53–61. https://doi.org/10.4031/MTSJ.52.6.9.Raghukumar, Kaustubha, Grace Chang, Frank Spada, Craig Jones, Tim Janssen, and Andrew Gans. “Performance Characteristics of ‘Spotter,’ a Newly Developed Real-Time Wave Measurement Buoy.” Journal of Atmospheric and Oceanic Technology 36, no. 6 (June 1, 2019): 1127–41. https://doi.org/10.1175/JTECH-D-18-0151.1.Yaremchuk, Max, and Emanuel F. Coelho. “Filtering Drifter Trajectories Sampled at Submesoscale Resolution.” IEEE Journal of Oceanic Engineering 40, no. 3 (July 2015): 497–505. https://doi.org/10.1109/JOE.2014.2353472.

This data presents surface outputs from a simulation of the Boussinesq equations of motion on a re-rentrant zonal channel centred around 45ºN. A beam of northward-propagating mode-1 internal tide oscillating with a period of 12 hours interacts with quasi-geostrophic turbulence, created by a baroclinicially unstable jet. We separated the signal into low-frequency (i.e., evolving over two days or longer) and tidal-frequency (harmonic fits) components, in addition to the raw (instantaneous) data. The dataset contains five experiments with increasing turbulence intensities (wp50 to wp90) and split into three 100-day intervals (t1, t2 and t3). It includes sea surface height, sea surface temperature, surface vorticity, and other surface fields. For more details, see supporting publications and GitHub repository listed here, or contact the authors. V2 update (28 May 2021): wp50 was erroneously restored to the same stratification as wp75, which is now fixed. We also included the sin components of the harmonic filterings.