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opencc-by-4.0Feb 2022View details →

Deformation composite of the RADARSAT Geophysical Processor System (RGPS) Lagrangian motion data

Deformation composite constructed from the Lagrangian RADARSAT Geophysical Processor System (RGPS) Lagrangian motion data for January-February-March, 1997 to 2008. The nominal temporal and spatial scales for the composite data are T* = 3 days, and L* = 10 km. This data is analyzed and compared with model deformation statistics in Bouchat et al., Sea Ice Rheology Experiment (SIREx), Part I: Scaling and statistical properties of sea-ice deformation fields, Journal of Geophysical Research: Oceans (2022). The original RGPS Lagrangian motion data set consists in lists of trajectories (time and positions records) for points that are tracked in sequential synthetic aperture radar (SAR) images. The trajectories are organized in different “streams”, corresponding to different initial satellite passes over which a set of tracked points were initialized. For all streams, the trajectories are initialized on a uniform 10 km x 10 km grid at the beginning of the winter in November. Each tracked point can therefore be assigned to (i,j) indices corresponding to its initialization location on the grid. As time increases and the position records are updated, the tracked points are no longer uniformly separated, but their assigned (i,j) indices do not change. The trajectory records are updated when the tracking algorithm detects the tracked points in a new SAR image. The update interval is therefore not always the same for all points, nor is it always on the same time/day within a given stream as the tracking algorithm may be unsuccessful for certain images/points. Moreover, the multiple streams can overlap spatially, such that more than one trajectory can be assigned to the same (i,j) indices. Computing strain rates directly from the original RGPS Lagrangian motion product therefore results in deformation estimates that can span a wide range of spatio-temporal scales, that are not temporally coherent across all streams, and that can also be spatially redundant. The goal of constructing a deformation composite from the original RGPS Lagrangian motion product is to generate a coherent set of non-overlapping Lagrangian deformation estimates at fixed time intervals and with a uniform spatial scale that can be used for statistical analysis. The RGPS Lagrangian deformation composite is constructed using the weighted-average pre-processing method described in Bouchat & Tremblay (2020) and Hutter et al. (2020) and summarized here. For each stream separately, we first define quadrilateral Lagrangian cells assigned to the (i,j) indices by combining records from the (i,j), (i+1,j), (i,j+1), and (i+1, j+1) available Lagrangian trajectories. For each (i,j) cell, we then compute the Lagrangian strain rates if, between any two update times, the cell's records have: (i) simultaneous (plus or minus 3 hours) start and end times for all fours corners, (ii) an average time interval for all corners that corresponds to the nominal temporal resolution of T*= 3 days, and (iii) an area at the start time that corresponds to the nominal spatial resolution of L*= 10 km. The strain rates, the cell area, and the start and end times used to compute the cell's strain rates are also assigned to the (i,j) indices. Then, to create the composite deformation estimates at the same fixed start and end dates for all cells, we average the strain rate and area records at each (i,j) indices in fixed 3-day periods starting on January 1st, using the overlapping time between their start/end date interval with the fixed 3-day periods as weight. For visualization purposes only, we also average the cells' corners' starting positions from all records overlapping with the fixed 3-day interval and use these averaged positions as approximate coordinates for the composite deformation cells. Finally, all streams are spatially combined into a single strain rate composite. In the case of spatial overlap between two or more streams, we keep the cells that have the longest time coverage and discard the other ones.   There is one netCDF file per year. Data are organized in matrices where the (i,j) indices are the Lagrangian cells identifier. This allows us to keep track of neighbouring cells for the scaling analysis. See below for more information on what variables are included in the files and their structure.    1. Variables included <ul> <li>(x1,y1), (x1,y2), (x3,y3), (x4,y4): Average positions of the composite cells' corners. Used for visualization only (deformations should not be computed using these positions) - (meters);</li> <li>A: Composite cells' area - (meters squared);</li> <li>dudx, dudy, dvdx, dvdy: Composite cell's velocity derivatives (strain rates/deformation) - (1/seconds);</li> <li>d_dudx, d_dudy, d_dvdx, d_dvdy: Trajectory error on the composite cells' velocity derivatives - (1/seconds);</li> <li>time: Day of year.</li> </ul> *Note: The composite cells were removed if their average position was within 100 km from land. Before comparing the deformation statistics with sea-ice models, one should only keep cells available in both the model and the RGPS composite.   2. Variable structure All variables (except time) are matrices with axes (it, i, j ), where it is the time stamp/iteration and i,j are the cells identifiers. See below for how the cells are defined:   |--------------------------------------------------------------> j-axis    |    |   (x1_ij,y1_ij) o --------------------o (x2_ij,y2_ij)    |                        |                                  |    |                        |     A_ij  or dudx_ij   |    |                        |                                  |    |   (x4_ij,y4_ij) o ------------------- o (x3_ij,y3_ij)    |    | Vi-axis         References: Bouchat, A., & Tremblay, B. (2020). Reassessing the Quality of Sea-Ice Deformation Estimates Derived From the RADARSAT Geophysical Processor System and Its Impact on the Spatiotemporal Scaling Statistics. Journal of Geophysical Research: Oceans, 125(8), https://doi.org/10.1029/2019JC015944 Hutter, N. and Losch, M.: Feature-based comparison of sea ice deformation in lead-permitting sea ice simulations, The Cryosphere, 14, 93–113, https://doi.org/10.5194/tc-14-93-2020, 2020. The original RGPS Lagrangian Motion data set can be accessed here: https://asf.alaska.edu/data-sets/derived-data-sets/seaice-measures/sea-ice-measures-data-products/

opencc-by-4.0Feb 2022View details →

Model Lagrangian trajectories and deformation data analyzed in the Sea Ice Rheology Experiment - Part I

Model Lagrangian trajectories and deformation estimates for sea-ice models participating in the Sea Ice Rheology Experiment (SIREx) - Part I. Model Lagrangian trajectories are integrated offline, starting on January 1st with all available raw RGPS cells positions (interpolated to January 1st 00:00:00 UTC). The trajectories are advected at an hourly time step with the models daily velocity output until March 31st. The trajectories are then sampled at a 3-day interval to match the RGPS composite time stamps, and the velocity derivatives (deformation) are calculated using the line integral approximations on the cells' contour. All model trajectories and Lagrangian deformation data therefore have nominal temporal and spatial scales of 3-days and 10-km (same as the RGPS composite), regardless of the original resolution of the model output. The model Lagrangian deformation estimates form the basis quantity for the statistical and spatio-temporal scaling analysis presented in Bouchat et al., Sea Ice Rheology Experiment (SIREx), Part I: Scaling and statistical properties of sea-ice deformation fields, Journal of Geophysical Research: Oceans (2022). This paper also provides further details on the model trajectory integration and deformation calculation. There is one netCDF file per model, per year (1997 and/or 2008). Data are organized in matrices where the (i,j) indices are the Lagrangian cells identifier. This allows us to keep track of neighbouring cells for the scaling analysis. See below for more information on what variables are included in the files, their structure, and how to cite.    1. File naming convention "< Model simulation label >" + _ + "deformation" + _ +  "< year >"    2. Variables included <ul> <li>(x1,y1), (x1,y2), (x3,y3), (x4,y4): Position of the cells' corners (Lagrangian trajectories) - (meters);</li> <li>A: Cells' area - (meters squared);</li> <li>dudx, dudy, dvdx, dvdy: Cell's velocity derivatives (strain rates/deformation) - (1/seconds);</li> <li>d_dudx, d_dudy, d_dvdx, d_dvdy: Trajectory error on cells' velocity derivatives - (1/seconds);</li> <li>time: Day of year.</li> </ul> *Note: the model trajectories are terminated if they move within 100 km from land. Before computing deformation statistics to compare with RGPS composite data, one should mask both deformation sets to only keep cells available in both the model and RGPS data sets.   3. Variable structure All variables (except time) are matrices with axes (it, i, j ), where it is the time stamp/iteration and i,j are the cells identifiers. See below for how the cells are defined:   |--------------------------------------------------------------> j-axis    |    |   (x1_ij,y1_ij) o ------------------- o (x2_ij,y2_ij)    |                        |                                    |    |                        |     A_ij  or dudx_ij     |    |                        |                                    |    |   (x4_ij,y4_ij) o ------------------- o (x3_ij,y3_ij)    |    | Vi-axis     Hence, coordinates are repeated between neighbouring cells, for example: (x2_ij,y2_ij) = (x1_ij+1,y1_ij+1) and (x4_ij,y4_ij) = (x1_i+1j,y1_i+1j)   4. Recommended citation usage If all simulations included in the current archive are used in a future study, we ask to cite this archive and the SIREx paper (Bouchat et al., 2022).  If only selected simulations are used, we ask to cite both this archive and the reference paper(s) applying to the selected simulation(s) (as stated indicated in Table 1 of the SIREx papers).

opencc-by-4.0Nov 2019View details →

Larval dispersal histogram data used for ATLAS deliverable D1.6: Biologically realistic Lagrangian dispersal and connectivity

Larval dispersal histogram data for ATLAS deliverable D1.6  "Biologically realistic Lagrangian connectivity" (https://www.eu-atlas.org/resources/atlas-partners-document-area/atlas-deliverables/455-d1-6-biologically-realistic-lagrangian-connectivity/file). Tar archive files are ordered by ATLAS case study source region and with folders by larval behaviour type. The numbered behaviour types are described in deliverable D1.6. Each netcdf histogram file,  e.g. hists_age_21.nc,  contains the histogram for larvae of a single age in 5-day steps, from 00 (0 days) to 37 (185 days). Within each file histogram file, particle counts in each Viking20 model grid-cell are contained in a 4-d array with dimensions (launch month, lauch year, model gridsquare y index, model gridsquare x index). The Viking20 grid in the North Atlantic is the ORCA tripolar grid. Details of the model mesh are in the included file viking20_mesh_mask.tgz Histograms are in netcdf files: ============================ $ ncdump -h hists_age_00.nc netcdf hists_age_00 { dimensions:         coordinate = 4 ;         coordinate_1 = 50 ;         coordinate_2 = 1719 ;         coordinate_3 = 1784 ; variables:         int64 coordinate(coordinate) ;                 coordinate:units = "month" ;                 coordinate:long_name = "Launch month" ;         int64 coordinate_1(coordinate_1) ;                 coordinate_1:units = "year" ;                 coordinate_1:long_name = "Launch year" ;         int64 coordinate_2(coordinate_2) ;                 coordinate_2:units = "index" ;                 coordinate_2:long_name = "J index" ;         int64 coordinate_3(coordinate_3) ;                 coordinate_3:units = "index" ;                 coordinate_3:long_name = "I index" ;         int64 data(coordinate, coordinate_1, coordinate_2, coordinate_3) ;                 data :long_name = "particle count" ; // global attributes:                 :Conventions = "CF-1.6" ; } ==========================================

opencc-by-4.0Aug 2024View details →

Supporting Data for "Identifying the Origins of Nanoplastics in the Abyssal South Atlantic Using Backtracking Lagrangian Simulations with Fragmentation"

Supporting Data for "Identifying the Origins of Nanoplastics in the Abyssal South Atlantic Using Backtracking Lagrangian Simulations with Fragmentation", published in the Ocean and Coastal Research Journal. The repository consists of the code used for the simulations and analysis, and the supplementary information document associated with the main manuscript, the Lagrangian simulation outputs and aditional data used for the analysis.

opencc-by-4.0Jan 2020View details →

They Came From The Pacific: How changing Arctic currents could contribute to an ecological regime shift in the Atlantic Ocean - Lagrangian Data 1990-2002 (2 of 2)

Supporting data for Kelly et al.: They Came From The Pacific: How changing Arctic currents could contribute to an ecological regime shift in the Atlantic Ocean (Earth's Future, submitted) Trajectories saved by year of release in the Bering Strait. All months from that year are included in the same file, with the first 1000 trajectories corresponding to January release, second 1000 from February release, and so on.  Due to the size of files, this is split into two uploads. Part 1 covers 1970-1989 releases, 1990 onward is saved in Part 2.

opencc-by-4.0Jan 2020View details →

They Came From The Pacific: How changing Arctic currents could contribute to an ecological regime shift in the Atlantic Ocean - Lagrangian Data 1970-1989 (1 of 2)

opencc-by-4.0Nov 2020View details →

A 3-km model configuration of the southern Benguela Current upwelling system: ROMS model data and Pyticles Lagrangian data

This dataset contains model output data from the Regional Ocean Modelling System (ROMS) configuration of the southern Benguela upwelling system (SBUS) to study the interannual variability of Lagrangian transport in the SBUS. This is a 3-km model resolution that ran for 22 years from 1989-2011 period with the first 3 years considered as spin-up. The model outputs were archived at a daily frequency. The 3-km model was nested in a 7.5 km model resolution described by Ragoasha et.al., 2019. The model output data provided here is a monthly climatology (1995-2011) NetCDF file of the surface temperature, salinity, the velocity fields (u,v & w), and sea surface height (SSH). The file that contains the model grid is also provided. An eddy detection and tracking algorithm were also performed on the daily 3-km SSH model outputs to study mean eddy characteristics of the region for the 1992-2011 period.  The file contains identifications of the Eddies detected and tracked in out model domain, their position (longitude and latitude), vorticity, amplitude, propagation and rotational speed.   An example of a Pyticles (Gula et al., 2014; Ragoasha et.al., 2019) Lagrangian output subset for 3000 Lagrangian drifters tracked for 60 days. The drifters were released in the upper 100 m depth at an across-shore transect off Cape Point (34oS).  A Matlab file is also provided for monthly (1992-2011) percentage of drifters that reach St Helena Bay (32oS) from Cape Point.       Dataset provided: Monthly climatology file: “roms_avg_Y1995M1-Y2011M12.nc” Model grid file: “grid_roms_avg_r3km.nc” Eddy tracking file: “TRA02_SEL01_DET02_eddies_r3km_1992M1_2011M12.nc” Pyticles Lagrangian experiment output example file: “Pyticles_Y2010M10.nc” Monthly transport success Matlab file: "R3km_monthly_transport_1992_2011.mat"     Citations:   Ragoasha, N., Herbette, S., Cambon, G., Reason, C., Roy, C., 2019. Lagrangian pathways in the southern Benguela upwelling system. Journal of Marine Systems, 195: 50-66.   Gula, J., Molemaker, M. J., & McWilliams, J. C., 2014. Submesoscale Cold Filaments in the Gulf Stream. Journal of Physical Oceanography., 44 (10), 2617–2643. DOI: 10.1175/JPO-D-14-0029.1   Corresponding author: M.N. Ragoasha, ORCID identifier:  0000-0002-1500-6259. Email: moagaboragoasha@gmail.com   Acknowledgements: The authors acknowledge the funding of N. Ragoasha’s PhD by the South-Africa’s National Research Foundation (NRF, South Africa) and the French Institute for Research and Sustainable Development (IRD, France). This work was also supported by the French National Program LEFE/INSU under the project’s name Benguela Upwelling Innershelf 647 Circulation (BUIC). This work was granted access to the HPC resources of [TGCC/CINES/IDRIS] under the allocation 2017- [DARI n◦A0020107443] attributed by GENCI (Grand Equipement National de Calcul Intensif).

opencc-by-4.0Feb 2022View details →

NetCDF data used in analysis presented in "Assessment of the z~ time-filtered Arbitrary Lagrangian-Eulerian coordinate in a global eddy-permitting ocean model"

NetCDF data used in analysis presented in "Assessment of the z~ time-filtered Arbitrary Lagrangian-Eulerian coordinate in a global eddy-permitting ocean model", submitted to Journal of Advances in Modelling the Earth System. The data are produced from an ensemble of six experiments based on the GO8p0 configuration of NEMO v4.0.1 on a global 1/4° grid, as described in the paper. The ensemble is intended to test the z~ vertical coordinate, and includes a control with the default "z-star" fixed coordinate, and five experiments with the z-tilde vertical coordinate, using a selection of values for the two z-tilde timescale parameters. The data includes time series of global mean ocean and ice fields; large-scale transports; and fields from diapycnal mixing analysis. The first part of each filename refers to the experiment from the ensemble ("zstar", "ztilde_5_30", "ztilde_10_30", "ztilde_20_30", ztilde_20_60" and "ztilde_40_60"); the following five-character string identifies the respective suite on the Met Office Rose system and the MASS archive system; and the rest of the name specifies the type of data contained in the file.

opencc-by-4.0Jul 2022View details →

Lagrangian particles in turbulence: An experimental data set

This data set contains the coordinates of Lagrangian trajectories in a quasi-homogeneous isotropic turbulent flow. The trajectories were measured in a water tank experiment using the 3D-PTV method through the MyPTV open-source software (https://github.com/ronshnapp/MyPTV). The total duration of this data set corresponds to 20 seconds of recording at a rate of 500 Hz, and it holds roughly 575,000 trajectories.   The data is stored in two text files in a tab-separated format, where each file coresponds to 10 seconds of recording. Each row in the files outlines a single measurement point from the experiment. The various columns correspond to the following information: 1 -   trajectory_id  2 -   x [mm]  3 -   y [mm] 4 -   z [mm] 5 -   vx [mm/frame] 6 -   vy [mm/frame] 7 -   vz [mm/frame] 8 -   ax [mm/frame^2] 9 -   ay [mm/frame^2] 10 -   az [mm/frame^2] 11 -   time [frame] where trajectory_id uniquely marks samples that correspond to the same physical trajectory; x, y, and z are the position components in the three orthogonal space directions; vx, vy, and vz correspond to the velocity components; ax, ay, and az that correspond to the acceleration components. The flow in the experiment was forced using a system of 8 propellers, powered by DC motors that were positioned at the corners of the cylindrical, octagonally shaped, water tank. The propellers were changing their direction of rotation at random time intervals with an average interval duration of 0.1 seconds. The root mean square of the turbulent flow fluctuations is about 100 millimeters per second. There is a time-averaged secondary circulation with a magnitude of roughly 66% of the root mean squared fluctuation strength. The Taylor microscale Reynolds number is estimated as about 188.

opencc-by-4.0Jun 2023View details →

FESOM-REcoM model data: Lagrangian particle trajectories

This data set includes the results of Lagrangian particle tracking experiments with FESOM1.4-REcoM2. Particles were seeded at 596 positions near the Filchner Ice Shelf front at 78°S between Berkner Island and Coats Land and tracked forwards and backwards using daily mean model output. Particles were seeded every 10th day in 1990 and 1991 (historical, forward), 2009 and 2008 (historical, backward), 2080 and 2081 (future/SSP5-8.5 scenario, forward), and 2099 and 2098 (future/SSP5-8.5 scenario, backward). Particles were tracked outside of ice-shelf cavities for 20 (19) years or until the the particle left the domain of interest in the north (62°S), west (65°W), or east (2°E).  The following information for each particle is stored twice a day: longitude (blon in files), latitude (blat), time (bday and time), depth (bdepth), temperature (btemp), salinity (bsalt), density (bsigma0; potential density anomaly referenced to 0dbar) dissolved inorganic carbon (bdic), and total carbon (btotc). Each *tar.gz archive contains one experiment, i.e., all trajectories for e.g., forward/1990-2009 (seeding days 1,11,...,361 in 1990). For each seeding day, there are 12 *nc files, which together constitute the 596 particles seeded on a given day.   Naming convention / *tar.gz files:  Nissen2023_FESOM1.4_REcoM2_Trajectories_FT_EXPERIMENT_START_END_YEAR.tar.gz EXPERIMENT: bw (backward) or fw (forward) START_END_YEAR: 2009_1990, 2008_1990, 2098_2080, or 2099_2080 for backward experiments; 1990_2009, 1990_2008, 2080_2099, or 2081_2099 for forward experiments (EXAMPLE: Nissen2023_FESOM1.4_REcoM2_Trajectories_FT_fw_1990_2009.tar.gz) Naming convention / *nc files:  drifter_start_Filchner_ice_shelf_daySEEDING_DAY_START_END_YEAR_NUM_FILE_reduced.nc SEEDING_DAY: 1, 11, ..., 361 START_END_YEAR: same as above NUM_FILE: 1, ..., 12 (EXAMPLE: drifter_start_Filchner_ice_shelf_day1_1990_2009_1_reduced.nc)   NOTE:  The following files are duplicates and also contained in the larger *tar archives described above (disregard them if all tar archives starting with "Nissen2023_FESOM1.4_REcoM2_Trajectories_FT_" have been downloaded): Nissen2023_FESOM1.4_REcoM2_LagrangianExperiments_FT_fw_day91_1991_2009.tar.gz Nissen2023_FESOM1.4_REcoM2_LagrangianExperiments_FT_fw_day91_2080_2099.tar.gz   The Lagrangian particle trajectories have been analyzed here:  Nissen, C., Timmermann, R., van Caspel, M., and Wekerle, C.: Altered Weddell Sea warm- and dense-water pathways in response to 21st-century climate change, Ocean Sci., 20, 85–101, <a href="https://doi.org/10.5194/os-20-85-2024">https://doi.org/10.5194/os-20-85-2024</a>, 2024 The Eulerian fields underlying the Lagrangian experiments are described in more detail here:  Nissen, C., Timmermann, R., Hoppema, M. et al. Abruptly attenuated carbon sequestration with Weddell Sea dense waters by 2100. Nat Commun 13, 3402 (2022). <a href="https://doi.org/10.1038/s41467-022-30671-3">https://doi.org/10.1038/s41467-022-30671-3</a>  Nissen, C., R. Timmermann, M. Hoppema, and J. Hauck, 2023: A regime shift on Weddell Sea continental shelves with local and remote physical-biogeochemical implications is avoidable in a 2°C scenario. J. Climate, 36, 6613–6630, <a href="https://doi.org/10.1175/JCLI-D-22-0926.1">https://doi.org/10.1175/JCLI-D-22-0926.1</a>.  Original model output is available at the World Data Center for Climate (WDCC) under the following DOIs: <ul> <li>simA, historical: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC</a></li> <li>simA, ssp585: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC</a></li> </ul>

zenodo36/100

Data and scripts accompanying the paper "University of Warsaw Lagrangian Cloud Model (UWLCM) 2.0"

The archive contains datasets, run scripts and plotting scripts used when preparing the paper: P. Dziekan and P. Zmijewski "University of Warsaw Lagrangian Cloud Model (UWLCM) 2.0: Adaptation of a mixed Eulerian-Lagrangian numerical model for heterogeneous computing clusters" submitted to Geoscientific Model Development on 19.11.2021.

opencc-by-4.0Nov 2021View details →

dryad36/100

Assessment of connectivity patterns of the marbled crab Pachygrapsus marmoratus in the Adriatic and Ionian seas through combination of genetic data and Lagrangian simulations

Seascape connectivity studies, informing the level of exchange of individuals between populations, can provide extremely valuable data for marine population biology and conservation strategy definition. Here we used a multidisciplinary approach to investigate the connectivity of the marbled crab (Pachygrapsus marmoratus), a high dispersal species, in the Adriatic and Ionian basins. A combination of genetic analyses (based on 15 microsatellites screened in 314 specimens), Lagrangian simulations (obtained with a biophysical model of larval dispersal) and individual-based forward-time simulations (incorporating species-specific fecundity and a wide range of population sizes) disclosed the realized and potential connectivity among eight different locations, including existing or planned Marine Protected Areas (MPAs). Overall, data indicated a general genetic homogeneity, after removing a single outlier locus potentially under directional selection. Lagrangian simulations showed that direct connections potentially exist between several sites, but most sites did not exchange larvae. Forward-time simulations indicated that a few generations of drift would produce detectable genetic differentiation in case of complete isolation as well as when considering the direct connections predicted by Lagrangian simulations.Overall, our results suggest that the observed genetic homogeneity reflects a high level of realized connectivity among sites, which might result from a regional metapopulation dynamics, rather than from direct exchange among populations of the existing or planned MPAs. Thus, in the Adriatic and Ionian basins, connectivity might be critically dependent on unsampled, unprotected, populations, even in species with very high dispersal potential like the marbled crab. Our study pointed out the pitfalls of using wide-dispersing species with broad habitat availability when assessing genetic connectivity among MPAs or areas deserving protection and prompts for the careful consideration of appropriate dispersing features, habitat suitability, reproductive timing and duration in the selection of informative species.

opencc-zeroMay 2022View details →

zenodo36/100

Replication data for: Spatial and temporal origins of the La Perouse low oxygen pool: A combined Lagrangian statistical approach

This dataset contains: a) NEP36 daily Model (NEMO) Output from 20130228 till 20131005 in NetCDF format, b) Moving Vessel Profiler (MVP) Survey data gathered onboard the R/V Falkor during August 2013 in ASCII .mat files, c) Files required to initialize and run Lagrangian Particle tracking model ARIANE i.e. one mesh_mask file in netCDF format and one text file containing initial positions based on the Eulerian grid of the sliced NEP36 model d) the output files from running the particle tracking model ARIANE in NetCDF format

opencc-by-4.0Oct 2021View details →

dryad36/100

Assessment of connectivity patterns of the marbled crab Pachygrapsus marmoratus in the Adriatic and Ionian seas through combination of genetic data and Lagrangian simulations

Open the record for dataset details and reuse information.

publicOct 2022View details →

opencc-by-4.0May 2020View details →

Data set - Lagrangian observations and modelling of turbulence along a tidally influenced river

The 'Kaipara_model.mat' files contains the grid and bathymetry of a model of the Kaipara River, New Zealand, created notably in order to study turbulence in a Lagrangian frame of reference.  The 'Dataset_Kaipara_Lagrangian.mat' file contains Lagrangian observations collected in the Kaipara river and corresponding model predictions.

opencc-by-4.0Nov 2020View details →

Data set - Lagrangian observations and modelling of turbulence along a tidally influenced river - Refined model

The 'Dataset_Kaipara_Lagrangian_refined.mat' file contains Lagrangian observations collected in the Kaipara river and corresponding model predictions, after refinement of the model.    The 'Kaipara_model_refined.mat' files contains the grid and bathymetry of a model of the Kaipara River, New Zealand, created notably in order to study turbulence in a Lagrangian frame of reference.

opencc-by-4.0Mar 2024View details →

Parcels-WAOM model data: Lagrangian particle trajectories

This data set includes the results of Lagrangian particle tracking experiments with Parsels in Weddell and Ross seas.  The following information for each particle is stored once a day: longitude (lon in files), latitude (lat), time (time), depth (z), temperature (temp), salinity (sal), ice draft (ice). Each *.zip archive contains one experiment in netcdf file for one of the seas (Weddell  or Ross seas) and for one of four seasons during 20 years of simulations.

opencc-by-4.0Mar 2024View details →

NetCDF data matrix with Lagrangian dispersal simulation output

zipped NetCDF file (unzipped ~444GB) The NetCDF file contains a data matrix with the number of particles per bin (lon 0.015°, lat 0.01°) in the geographic area from -12°W - 10°E, and 47°N - 63°N. Particle numbers are labelled according to the three simulated scenarios ("period" 0-2, 14-28, 0-28 days after release), by station ("station" as named in stations.csv)", by year ("year" 2019-2022), and by day when the simulation was started ("offset", 000-122 days counting from 01.05.-31.08.) data variable: particle_number [111,007,858,176  values, float32] dimensions:  lon_bin [length 1468, float32] lat_bin [length 1601, float32] period [length 3, object '0-2','14-28','0-28'] station [length 32, object 'DK_044','FR_0206'....(all station names)] year [length 4, object '2019'...'2022'] offset [length 123, object '000'...'122']