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129 results for “lagrangian”
Assessment of connectivity patterns of the marbled crab Pachygrapsus marmoratus in the Adriatic and Ionian seas through combination of genetic data and Lagrangian simulations
<p>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.</p>
Lagrangian Heavy Precipitation Events in convection-permitting Regional Climate Models over the Alps and in the Mediterranean
<p>The csv datafile contains a set of heavy precipitation events identified in cpRCMs.</p> <p>Each of the entries represents an event and is described with detailed properties:</p> <p>'Start Date [YYYYMMDD.HOUR/24]', 'Latitude [°]', 'Longitude [°]',<br> 'Duration [h]', 'Volume [km² h]', 'P99(pr) [mm h-1]',<br> 'P90(pr) [mm h-1]', 'P75(pr) [mm h-1]',<br> 'P50(pr) [mm h-1]', 'P25(pr) [mm h-1]',<br> 'P10(pr) [mm h-1]', 'Total Precipitation [m3]',<br> 'Maximum Precipitation [mm h-1$]',<br> 'Mean Precipitation [mm h-1$]', 'Direction [°]',<br> 'Distance Traveled [km]', 'Eccentricity [-]', 'Track Eccentricity [-]',<br> 'Mean Ellipsicity [-]', 'Track Ellipsicity [-]', 'Mean Major Angle [°]',<br> 'Track Major Angle [°]', 'Mean Major Axis [-]', 'Track Major Axis [-]',<br> 'My Orientation [°]', 'My Track Orientation [°]', 'max(Elevation) [m]',<br> 'min(Elevation) [m]', 'Start Year [YYYY]', 'Start Month [MM]',<br> 'LandFallSea [-]', 'Scenarios', 'Models', 'situations', 'Ensemble',<br> 'Speed [km h$^{-1}$]', 'Mean(Area) [km²]', 'orographic [-]',<br> 'Severity [-]', 'I/O OBS [-]', 'Region [-]', 'orographic1500 [-]',<br> 'orographic2000 [-]', 'orographic2500 [-]', 'orographic3000 [-]']</p>
HYCOM-OceanTrack: Integrated HYCOM Eulerian Fields and Lagrangian Trajectories Dataset
<p>A combined dataset on the Registry of Open Data on AWS of simulated ocean sea surface height, near-surface velocities, and particle trajectories from a global 1/25th degree HYbrid Coordinate Ocean Model (HYCOM) 1-year run.</p>
An Economical Open-Source Lagrangian Drifter Design to Measure Deep Currents in Lakes
<p>An economical, open-source Lagrangian drifter designed to collect current data on lakes<200km2 was evaluated against existing designs. The new design was tested in deep inland lakes in the Finger Lakes region of New York, USA and is effective at tracking deep currents. The ease and low-cost of fabrication and launch/recovery should facilitate use of this design by less-advantaged communities & researchers.</p> <p>This project includes data and code for preparation of graphs and charts to illustrate Lagrangian drifter experiments in Seneca Lake and Keuka Lake, New York, USA.</p>
ESA 4DMED-Sea - Finite-Time Lagrangian Vorticity in the Mediterranean Sea derived from 4DVARNET8 geostrophic velocities (1/24°)
<p>This product provides the Finite-Time Lagrangian Vorticity derived from surface geostrophic velocities result from the application of the 4DVARNET algorithm (<a href="https://isprs-annals.copernicus.org/articles/V-3-2021/295/2021/isprs-annals-V-3-2021-295-2021.html" target="_blank" rel="noopener">Fablet et al., 2021</a>; resolution of the dynamical model used for the learning/training (<a href="https://github.com/ocean-next/eNATL60">eNATL60-BLB02</a>) downgraded to 1/8°) to altimetry L3-data (https://doi.org/10.5281/zenodo.10908416) at a resolution of 1/24° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Time Lagrangian Vorticity was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/24º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>ftlv (2D)</p>
ESA 4DMED-Sea - Finite-Time Lagrangian Vorticity in the Mediterranean Sea derived from 4DVARNET8 geostrophic velocities (1/72°)
<p>This product provides the Finite-Time Lagrangian Vorticity derived from surface geostrophic velocities result from the application of the 4DVARNET algorithm (<a href="https://isprs-annals.copernicus.org/articles/V-3-2021/295/2021/isprs-annals-V-3-2021-295-2021.html" target="_blank" rel="noopener">Fablet et al., 2021</a>; resolution of the dynamical model used for the learning/training (<a href="https://github.com/ocean-next/eNATL60">eNATL60-BLB02</a>) downgraded to 1/8°) to altimetry L3-data (https://doi.org/10.5281/zenodo.10908416) at a resolution of 1/72° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Time Lagrangian Vorticity was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/72º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>ftlv (2D)</p>
ESA 4DMED-Sea - Finite-Time Lagrangian Vorticity in the Mediterranean Sea derived from MIOST geostrophic velocities (1/24°)
<p>This product provides the Finite-Time Lagrangian Vorticity derived from surface geostrophic velocities result from the application of the MIOST algorithm (Ubelmann et al., 2019; https://doi.org/10.1029/2020JC016560) to altimetry L3-data (https://doi.org/10.5281/zenodo.10648981) at a resolution of 1/24° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Time Lagrangian Vorticity was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/24º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>ftlv (2D)</p>
ESA 4DMED-Sea - Finite-Time Lagrangian Vorticity in the Mediterranean Sea derived from MIOST geostrophic velocities (1/72°)
<p>This product provides the Finite-Time Lagrangian Vorticity derived from surface geostrophic velocities result from the application of the MIOST algorithm (Ubelmann et al., 2019; https://doi.org/10.1029/2020JC016560) to altimetry L3-data (https://doi.org/10.5281/zenodo.10648981) at a resolution of 1/72° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Time Lagrangian Vorticity was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/72º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>ftlv (2D)</p>
ESA 4DMED-Sea - Finite-Time Lagrangian Vorticity in the Mediterranean Sea derived from 4DVARNET20 geostrophic velocities (1/24°)
<p>This product provides the Finite-Time Lagrangian Vorticity derived from surface geostrophic velocities result from the application of the 4DVARNET algorithm (<a href="https://isprs-annals.copernicus.org/articles/V-3-2021/295/2021/isprs-annals-V-3-2021-295-2021.html" target="_blank" rel="noopener">Fablet et al., 2021</a>; resolution of the dynamical model used for the learning/training (<a href="https://github.com/ocean-next/eNATL60">eNATL60-BLB02</a>) downgraded to 1/20°) to altimetry L3-data (https://doi.org/10.5281/zenodo.10912777) at a resolution of 1/24° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Time Lagrangian Vorticity was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/24º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>ftlv (2D)</p>
ESA 4DMED-Sea - Finite-Time Lagrangian Vorticity in the Mediterranean Sea derived from 4DVARNET20 geostrophic velocities (1/72°)
<p>This product provides the Finite-Time Lagrangian Vorticity derived from surface geostrophic velocities result from the application of the 4DVARNET algorithm (<a href="https://isprs-annals.copernicus.org/articles/V-3-2021/295/2021/isprs-annals-V-3-2021-295-2021.html" target="_blank" rel="noopener">Fablet et al., 2021</a>; resolution of the dynamical model used for the learning/training (<a href="https://github.com/ocean-next/eNATL60">eNATL60-BLB02</a>) downgraded to 1/20°) to altimetry L3-data (https://doi.org/10.5281/zenodo.10912777) at a resolution of 1/72° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Time Lagrangian Vorticity was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/72º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>ftlv (2D)</p>
Lagrangian drifter output in the Southeast Indian Ocean using the Connectivity Modelling System output forced with TROPAC01
<p>This dataset contains Lagrangian drifter trajectories from the Connectivity Modelling System (CMS) in the Southeast Indian Ocean. CMS was forced with ocean velocity fields from TROPAC01 and this experiment was focused on sources of the Leeuwin Current. TROPAC01 is a high-resolution ocean general circulation model, developed by the European Drakkar cooperation [Barnier et al., 2007] it is based on the NEMO [v3.2 Madec, 2008] code. Specifically, it is a 1/10 horizontal resolution model of the tropical Indo- Pacific region (spanning the area from 73°E - 63°W to 49°S - 31°N), nested within a half-degree global ocean/ sea-ice model. More information on the model configuration used for this experiment can be found in [van Sebille et al., 2014]. Using the velocity fields from TROPAC01 we then use the Connectivity Modelling System (CMS) v1.1 [Paris et al., 2013] to integrate the virtual particles in three-dimensional time-evolving flow.</p> <p>Version v1.0 of this dataset includes ascii raw model output of CMS trajectories and the forcing file (seed file) that enables a user to calculate absolute time of a particle's location. Variables are: particle_number, time, longitude, latitude, depth, exit_code.</p> <p>These experiments were executed by Christopher Bull of the ARC Centre of Excellence for Climate System Science (ARCCSS) research program "Mechanisms and attribution of past and future ocean circulation change", as part of Christopher's PhD candidature.</p> <p> </p> <p>References:</p> <p> Code and documentation for the CMS is available at:</p> <p> https://github.com/beatrixparis/connectivity-modeling-system</p> <p>Claire B. Paris, Judith Helgers, Erik van Sebille, Ashwanth Srinivasan, 2013.<br> Connectivity Modeling System: A probabilistic modeling tool for the multi-scale tracking of biotic and abiotic variability in the ocean,<br> Environmental Modelling & Software, Volume 42, 2013, Pages 47-54, ISSN 1364-8152, https://doi.org/10.1016/j.envsoft.2012.12.006.</p> <p>van Sebille, E., Sprintall, J., Schwarzkopf, F. U., Gupta, A. S., Santoso, A., England, M. H., Biastoch, A., and Böning, C. W. (2014), Pacific-to-Indian Ocean connectivity: Tasman leakage, Indonesian Throughflow, and the role of ENSO, <em>J. Geophys. Res. Oceans</em>, 119, 1365– 1382, doi:<a href="https://doi.org/10.1002/2013JC009525">10.1002/2013JC009525</a>.</p>
Datasets for "LAPS v1.0.0: Lagrangian Advection of Particles at Sea, a Matlabprogram to simulate the displacement of particles in the ocean."
<p>The data, run results and analysis scripts used to produce all of the results presented in the paper "LAPS v1.0.0: Lagrangian Advection of Particles at Sea, a Matlab program to simulate the displacement of particles in the ocean."</p>
Replication data for: Spatial and temporal origins of the La Perouse low oxygen pool: A combined Lagrangian statistical approach
<p>This dataset contains:</p> <p>a) <strong>NEP36 </strong>daily Model (NEMO) Output from 20130228 till 20131005 in NetCDF format,</p> <p>b) Moving Vessel Profiler (<strong>MVP</strong>) Survey data gathered onboard the R/V Falkor during August 2013 in ASCII .mat files,</p> <p>c) Files required to initialize and run Lagrangian Particle tracking model <strong>ARIANE </strong>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</p> <p>d) the output files from running the particle tracking model ARIANE in NetCDF format</p>
An approach for modelling simultaneous fluid-phase and chemical reaction equilibria in multicomponent systems via Lagrangian duality: The reactive HELD algorithm.
<p>This is a data set associated with the paper <em>An approach for modeling simultaneous fluid-phase and chemical reaction equilibria in multicomponent systems via Lagrangian duality: The reactive HELD algorithm. </em>by Felipe A. Perdomo, George Jackson, Amparo Galindo, Claire S. Adjiman. The manuscript is presented as a proceeding of the 33<sup>rd</sup> European Symposium on Computer-Aided Process Engineering (ESCAPE33), June 18-21, 2023, in Athens, Greece.</p>
Video supplement for Lagrangian transport simulations using the extreme convection parametrization: an assessment for the ECMWF reanalyses
<p>We provide a video supplement for the paper "Lagrangian transport simulations using the extreme convection parametrization: an assessment for the ECMWF reanalyses" submitted to the journal Geoscientific Model Development.</p>
Lagrangian trajectories tracking the origin and fate of a hydrothermal plume observed in the southeastern Pacific sector of the Southern Ocean
<p>A dataset of Lagrangian trajectories which were used to estimate the origin and fate of a hydrothermal plume in the southeastern Pacific sector of the Southern Ocean observed during the DY111 CUSTARD cruise. All variables have long names and units. These files have been used for the analysis in Birchill and Baker et al. ‘Pathways and timescales of Southern Ocean hydrothermal iron and manganese transport’ with further information about the methodology available in the paper. There are five forward-tracking experiments from suspected vent sites (CUSTARD_NEPR.nc, CUSTARD_SEPR.nc, CUSTARD_PAR.nc, CUSTARD_WCR.nc and CUSTARD_ECR.nc) included in the paper and one forward-tracking experiment that was not included in the paper (CUSTARD_EM.nc) as no trajectories crossed our sampling site. There was one backward-tracking experiment undertaken from the Ocean Observatories Initiative (OOI) station at which the hydrothermal signal was observed (CUSTARD_OOI_backward.nc).</p>
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.
Estimates of surface layer net community production based on underway Lagrangian measurements of the dissolved O2/Ar ratio using Equilibrator Inlet Mass Spectrometry (EIMS), based on both steady-state and non-steady-state assumptions of the mixed-layer biological oxygen budget. Also included are estimates of the potential contribution of vertical fluxes: advection, eddy diffusion, and entrainment.
The ratio of dissolved oxygen to argon in surface seawater is frequently employed to estimate rates of net community production (NCP) in the oceanic mixed layer. The in situ O2/Ar-based method accounts for many physical factors that influence oxygen concentrations in the surface ocean, permitting isolation of the biological oxygen signal produced by the balance of photosynthesis and respiration. However, this technique traditionally relies upon several assumptions when calculating the mixed layer O2/Ar budget, most notably the absence of vertical fluxes of O2/Ar and the existence of a steady-state balance between net productivity and the air-sea gas exchange of biological oxygen. Employing a Lagrangian study design and leveraging data outputs from a regional physical oceanographic model, we conducted in situ measurements of O2/Ar in the California Current Ecosystem in spring 2016 and summer 2017 to evaluate these assumptions within a ‘worst-case’ field environment. Quantifying the magnitude of vertical fluxes and comparing NCP estimates obtained using steady-state versus non-steady-state assumptions, we find the importance of the non-steady-state term to be considerable, also observing significant potential effects from vertical flux terms, particularly advection. Additionally, we observe strong diel variability in O2/Ar and calculated NCP rates at multiple stations. Our results reemphasize the importance of accounting for vertical fluxes when interpreting O2/Ar-derived NCP data as well as the potentially large effect of non-steady-state conditions, including diel cycles in surface O2/Ar that can bias interpretation of NCP data based on local productivity and the time of day at which measurements were made.
Estimates of mixed lyer gross primary productivity and photophysiology based on underway Lagrangian measurements of the dissolved chlorophyll fluorescence of phytoplankton usung Fast Repetition Rate Fluorometry (FRRF)
GPP was estimated on the P1706 cruise based on the photo-physiology of the mixed-layer phytoplankton community measured by FRRF. Shipboard measurements were made using a bench-top FastAct 2+ Fast TRAKA instrument (Chelsea, UK) plumbed into the ship’s running seawater system. Photosynthesis versus irradiance (P vs. E) curves were run continuously on a ~45 min sampling interval.
Data set - Lagrangian observations and modelling of turbulence along a tidally influenced river
<p>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. </p> <p>The 'Dataset_Kaipara_Lagrangian.mat' file contains Lagrangian observations collected in the Kaipara river and corresponding model predictions. </p>
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