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129 results for “lagrangian”
North Atlantic EXtratropical CYCLONE TRAcks and Lagrangian-Derived MOisture Uptake Dataset: ExCyclone-TRAMO (Part I)
<h3>Abstract:</h3> <p>This dataset focuses on <strong>extratropical cyclones (ETCs)</strong> over the <strong>North Atlantic Ocean (NATL)</strong> during the extended winter seasons, spanning from October to April, between 1985 and 2022. The relationship between moisture uptake and precipitation in various mesoscale ETC structures remains an active research area. The provision of moisture parameters through <strong>tracer dispersion models</strong> enhances our understanding of moisture dynamics, the behaviour of ETCs, and the associated meteorological fields.</p> <h3>Description:</h3> <p>The dataset was constructed through <strong>dynamic downscaling</strong> of ERA5 reanalysis data using the Weather Research and Forecasting (WRF) model, in conjunction with the <strong>Lagrangian dispersion model FELXPART-WRF</strong>. It consists of <strong>6-hourly </strong>time intervals with a horizontal resolution of <strong>0.18°</strong>. This database is completely described in <em>Coll-Hidalgo, P., Gimeno-Sotelo, L., Fernández-Alvarez, J.C. et al. North Atlantic Extratropical Cyclone Tracks and Lagrangian-Derived Moisture Uptake Dataset. Sci Data 11, 1258 (2024). <a href="https://doi.org/10.1038/s41597-024-04091-5" target="_blank" rel="noopener">https://doi.org/10.1038/s41597-024-04091-5</a></em></p> <p>The dataset includes:</p> <ul> <li>Detailed tracking of ETCs across the North Atlantic: <ul> <li>Date in year-month-day format</li> <li>hh (in UTC): Hour in Coordinated Universal Time</li> <li>latitude (degrees north): Latitude in degrees north</li> <li>longitude (degrees west): Longitude in degrees west</li> <li>MSLP (hPa): Mean sea level pressure in hectopascals (hPa)</li> <li>Radius (km): Cyclone radius in kilometres (km)</li> <li>Last closed isobar (hPa): Pressure at the last closed isobar in hectopascals (hPa)</li> <li>Low-level thermal wind parameter (VTL)</li> <li>High-level thermal wind parameter (VTU)</li> <li>The thermal asymmetry parameter (B)</li> </ul> </li> </ul> <ul> <li>Two-Dimensional Masks Shaping ETCs: <ul> <li>Radius</li> <li>Warm Conveyor Belt (WCB) footprint Related Areas</li> <li>A theoretical boundary encompassing a broader extent of the ETC circulation within a spiral shape </li> </ul> </li> </ul> <ul> <li>Cyclone Moisture Uptake-Related Variables by ETCs life timesteps: <ul> <li>Total moisture uptake</li> <li>Discrete Moisture Uptake (10-Day Backward)</li> <li>Moisture uptake by layers from the surface to 100 hPa.</li> </ul> </li> </ul> <h3>Dataset Repository Information</h3> <p>This repository contains the dataset for the years 1985-1999. For users interested in datasets from subsequent periods, please refer to the following links:</p> <p>Years 2000-2014:<a href="https://zenodo.org/records/13844450"> Access the dataset here</a><br>Years 2015-2022: <a href="https://zenodo.org/records/13847453" target="_blank" rel="noopener">Access the dataset here</a></p> <p>A <a href="https://github.com/ECMOISTDATABASE/North-Atlantic-Extratropical-Cyclones-database.git">GitHub repository</a> is available, providing example code that demonstrates how to use and handle the dataset effectively. </p>
North Atlantic EXtratropical CYCLONE TRAcks and Lagrangian-Derived MOisture Uptake Dataset: ExCyclone-TRAMO (Part II)
<h3>Abstract:</h3> <p>This dataset focuses on <strong>extratropical cyclones (ETCs)</strong> over the <strong>North Atlantic Ocean (NATL)</strong> during the extended winter seasons, spanning from October to April, between 1985 and 2022. The relationship between moisture uptake and precipitation in various mesoscale ETC structures remains an active research area. The provision of moisture parameters through <strong>tracer dispersion models</strong> enhances our understanding of moisture dynamics, the behaviour of ETCs, and the associated meteorological fields.</p> <h3>Description:</h3> <p>The dataset was constructed through <strong>dynamic downscaling</strong> of ERA5 reanalysis data using the Weather Research and Forecasting (WRF) model, in conjunction with the <strong>Lagrangian dispersion model FELXPART-WRF</strong>. It consists of <strong>6-hourly </strong>time intervals with a horizontal resolution of <strong>0.18°</strong>. This database is completely described in<em> Coll-Hidalgo, P., Gimeno-Sotelo, L., Fernández-Alvarez, J.C. et al. North Atlantic Extratropical Cyclone Tracks and Lagrangian-Derived Moisture Uptake Dataset. Sci Data 11, 1258 (2024). <a href="https://doi.org/10.1038/s41597-024-04091-5" target="_blank" rel="noopener">https://doi.org/10.1038/s41597-024-04091-5</a></em></p> <p>The dataset includes:</p> <ul> <li>Detailed tracking of ETCs across the North Atlantic: <ul> <li>Date in year-month-day format</li> <li>hh (in UTC): Hour in Coordinated Universal Time</li> <li>latitude (degrees north): Latitude in degrees north</li> <li>longitude (degrees west): Longitude in degrees west</li> <li>MSLP (hPa): Mean sea level pressure in hectopascals (hPa)</li> <li>Radius (km): Cyclone radius in kilometres (km)</li> <li>Last closed isobar (hPa): Pressure at the last closed isobar in hectopascals (hPa)</li> <li>Low-level thermal wind parameter (VTL)</li> <li>High-level thermal wind parameter (VTU)</li> <li>The thermal asymmetry parameter (B)</li> </ul> </li> </ul> <ul> <li>Two-Dimensional Masks Shaping ETCs: <ul> <li>Radius</li> <li>Warm Conveyor Belt (WCB) footprint Related Areas</li> <li>A theoretical boundary encompassing a broader extent of the ETC circulation within a spiral shape </li> </ul> </li> </ul> <ul> <li>Cyclone Moisture Uptake-Related Variables by ETCs life timesteps: <ul> <li>Total moisture uptake</li> <li>Discrete Moisture Uptake (10-Day Backward)</li> <li>Moisture uptake by layers from the surface to 100 hPa.</li> </ul> </li> </ul> <h3>Dataset Repository Information</h3> <p>This repository contains the dataset for the years 2000-2016. For users interested in datasets from subsequent periods, please refer to the following links:</p> <p>Years 1985-1999: <a href="https://zenodo.org/records/13844378">Access the dataset here</a><br>Years 2015-2022: <a href="https://zenodo.org/records/13847453" target="_blank" rel="noopener">Access the dataset here</a></p> <p>A <a href="https://github.com/ECMOISTDATABASE/North-Atlantic-Extratropical-Cyclones-database.git">GitHub repository</a> is available, providing example code that demonstrates how to use and handle the dataset effectively. </p>
Data - Evaluation performance 2 TCM Lagrangian
<p>The dataset contains the processed model output used to create the figure for the paper `Evaluation of the performance of two turbulence closure schemes in a Lagrangian frame of reference along a fluvial-to-marine transition zone'. </p>
Data accompanying the article "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020"
<p>The .zip file contains temporal-spatial averaged metrics for evaluating simulations against observed ice thickness, concentration, volume, and drift. These quantities are presented in the manuscript "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020"</p> <p>Subfolders are named by the experiment IDs, including metrics obtained from the relevant experimental results and observations.</p> <p>In case information is missing, do not hesitate to contact chengsukun@hotmail.com</p> <p>We thank Pavel Sakov for helpful discussions and improvement regarding the EnKF-C code and Jiping Xie for contributing the TOPAZ interface to sea ice observations. We are grateful for the support from Timothy Williams and Anton Korosov regarding the environments of neXtSIM and its analysis tools. The work is funded by the DASIM-II grant from ONR (grant nos. N00014-18-1-2493 and N00014-18-1-2204). Alberto Carrassi, Christopher K. R. T. Jones, Ali Aydo ̆gdu, and Pierre Rampal acknowledge the support of the project SASIP funded by Schmidt Futures – a philanthropic initiative that seeks to improve societal outcomes through the development of emerging science and technologies. Sukun Cheng and Laurent Bertino were co-funded by the FOCUS project from the Research Council of Norway (grant no. 301450), and Alberto Carrassi and Yumeng Chen are also supported by the UK National Centre for Earth Observation (grant no. NCEO02004). Computations were carried out on the Norwegian Supercomputing InfrastructureSigma2 (grants nn2993k for computing and NS2993K for data storage)</p>
Ariane outputs and Python scripts used in the GRL publication titled "A Lagrangian estimate of the Mediterranean outflow's origin"
<p>Outputs of quantitative experiments performed with the particle tracking software Ariane, and Python scripts to analyze and plot them. </p>
Data from: Population genomics meet Lagrangian simulations: oceanographic patterns and long larval duration ensure connectivity among Paracentrotus lividus populations in the Adriatic and Ionian seas
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Lagrangian Atmospheric Model output for the Control, Onset and Development experiments
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Lagrangian particle dataset (2 years) for Agulhas region surface flow
<p>This is a .npz file containing the longitudes and latitudes of approximately 24,000 virtual Lagrangian particles (0.2 degree in longitude and latitude initial spacing) released on January 5, 2000 in the eastern South Atlantic Ocean and advected for 2 years. The data was used for the detection of finite-time coherent sets (Agulhas rings), see this <a href="https://github.com/OceanParcels/coherent_vortices_OPTICS">github repository</a> for the usage and this <a href="https://github.com/OceanParcels/near_surface_microplastic">github repository</a> for simulation details. Trajectories were simulated using OceanParcels (https://oceanparcels.org/), with surface currents derived from a NEMO-ORCA 006 run, available at http://opendap4gws.jasmin.ac.uk/thredds/nemo/root/catalog.html.</p>
Dataset for "Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control"
<p>Dataset to reproduce results of "Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control"</p> <p> </p> <p><strong>Abstract</strong>: Recent approaches for modelling dynamics of physical systems with neural networks enforce Lagrangian or Hamiltonian structure to improve prediction and generalization. However, when coordinates are embedded in high-dimensional data such as images, these approaches either lose interpretability or can only be applied to one particular example. We introduce a new unsupervised neural network model that learns Lagrangian dynamics from images, with interpretability that benefits prediction and control. The model infers Lagrangian dynamics on generalized coordinates that are simultaneously learned with a coordinate-aware variational autoencoder (VAE). The VAE is designed to account for the geometry of physical systems composed of multiple rigid bodies in the plane. By inferring interpretable Lagrangian dynamics, the model learns physical system properties, such as kinetic and potential energy, which enables long-term prediction of dynamics in the image space and synthesis of energy-based controllers.</p>
Lagrangian and Eulerian droplets in DNS
The dataset contains some of the data from numerical simulations investigating the impact of turbulence on growth of cloud droplets by the diffusion of water vapor. The dataset includes a dozen of .dat files (standard write from fortran) and two README files that explain how the data was written and how to read it. The entire dataset is about 70 Gb.
Model hydrodynamic outputs and example codes for running different Lagrangian particle tracking packages
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Data presented in the paper "Impact of Giant Sea Salt Aerosol Particles on Precipitation in Marine Cumuli and Stratocumuli: Lagrangian Cloud Model Simulations"
<p>View Readme.</p>
Lagrangian trajectories representing surface drift from the Cape Verde islands
<p><strong>Example trajectories from a biophysical Lagrangian simulation</strong></p> <p>This is 25x 10.000 example trajectories from biophysical experiments performed with Parcels.</p> <p>The trajectories are a tiny subset of a much bigger collection of trajectories that have been simulated with the aim of learning about the fate of particles drifting away from the Cape Verde islands.</p> <p><em>Note, that these trajectories should not be used for biological or physical science but merely serve as study objects for developing, testing, or benchmarking (stasticical) methods and algorithms.</em></p> <p><strong>Details of the experiments</strong></p> <p>The trajectories are taken from 25 sets of biophysical simulations which differ in the year they represent. Particles are seeded between mid of August and start of December of the years 1993 to 2017. They are subject to Ocean surface currents simulated by a high-resolution ocean model and subject to Stokes Drift estimated by a wave simulation data provided by the Copernicus Marine Service (<a href="https://marine.copernicus.eu/">https://marine.copernicus.eu/</a>).</p> <p><strong>Data</strong></p> <p>The full data come as a Zarr store inside of a ZIP file <code>cape_verde_drift_trajectories_1993-2017.zarr.zip</code> which you need to download and unzip to be able to read it, e.g., with Xarray's <code>open_zarr</code> method. There are subsets of 10000 trajectories provided as compressed csv files split into years called <code>cape_verde_drift_trajectories_1-10000_1993.csv.gz</code> ... <code>cape_verde_drift_trajectories_1-10000_2017.csv.gz</code>. And there are subsets of 100 trajectories provided as un-compressed csv files split into years <code>called cape_verde_drift_trajectories_1-100_1993.csv</code> ... <code>cape_verde_drift_trajectories_1-100_2017.csv</code>.</p> <p><strong>Variables and their meaning</strong></p> <ul> <li><code>"obs"</code> contains the time step since the larva started to exist. Each trajectory covers up to 881 daily positions.</li> <li><code>"traj"</code> indicates the trajectory ID.</li> <li><code>"lat"</code> and <code>"lon"</code> contain the horizontal positions in degrees Latitude and Longitude.</li> <li><code>"temp"</code> contains the ambient temperature in degrees Celsius the simulated larva would have felt.</li> <li><code>"time"</code> contains time stamps for each position.</li> <li><code>"z"</code> contains the vertical positions of the simulated larva in meters counted downwards. As <code>"z</code>" does not vary (all particles are at the surface), it is omitted from the CSV files.</li> </ul> <p><strong>Details on the subsetting</strong></p> <p>See <a href="https://nbviewer.org/urls/zenodo.org/record/6826071/files/cape_verde_turtle_subsetting.ipynb">cape_verde_turtle_subsetting.ipynb</a>.</p> <p><strong>Using the data</strong></p> <p>This data set is licensed under a <em>Creative Commons Attribution 4.0 International License</em>.</p> <p>If you use the data, we'd love to get a notice to <a href="mailto:wrath@geomar.de">wrath@geomar.de</a>. This is, however, not required.</p>
North Atlantic EXtratropical CYCLONE TRAcks and Lagrangian-Derived MOisture Uptake Dataset: ExCyclone-TRAMO (Part III)
<h3>Abstract:</h3> <p>This dataset focuses on <strong>extratropical cyclones (ETCs)</strong> over the <strong>North Atlantic Ocean (NATL)</strong> during the extended winter seasons, spanning from October to April, between 1985 and 2022. The relationship between moisture uptake and precipitation in various mesoscale ETC structures remains an active research area. The provision of moisture parameters through <strong>tracer dispersion models</strong> enhances our understanding of moisture dynamics, the behaviour of ETCs, and the associated meteorological fields.</p> <h3>Description:</h3> <p>The dataset was constructed through <strong>dynamic downscaling</strong> of ERA5 reanalysis data using the Weather Research and Forecasting (WRF) model, in conjunction with the <strong>Lagrangian dispersion model FELXPART-WRF</strong>. It consists of <strong>6-hourly </strong>time intervals with a horizontal resolution of <strong>0.18°</strong>. This database is completely described in <em>Coll-Hidalgo, P., Gimeno-Sotelo, L., Fernández-Alvarez, J.C. et al. North Atlantic Extratropical Cyclone Tracks and Lagrangian-Derived Moisture Uptake Dataset. Sci Data 11, 1258 (2024). <a href="https://doi.org/10.1038/s41597-024-04091-5" target="_blank" rel="noopener">https://doi.org/10.1038/s41597-024-04091-5</a></em></p> <p>The dataset includes:</p> <ul> <li>Detailed tracking of ETCs across the North Atlantic: <ul> <li>Date in year-month-day format</li> <li>hh (in UTC): Hour in Coordinated Universal Time</li> <li>latitude (degrees north): Latitude in degrees north</li> <li>longitude (degrees west): Longitude in degrees west</li> <li>MSLP (hPa): Mean sea level pressure in hectopascals (hPa)</li> <li>Radius (km): Cyclone radius in kilometres (km)</li> <li>Last closed isobar (hPa): Pressure at the last closed isobar in hectopascals (hPa)</li> <li>Low-level thermal wind parameter (VTL)</li> <li>High-level thermal wind parameter (VTU)</li> <li>The thermal asymmetry parameter (B)</li> </ul> </li> </ul> <ul> <li>Two-Dimensional Masks Shaping ETCs: <ul> <li>Radius</li> <li>Warm Conveyor Belt (WCB) footprint Related Areas</li> <li>A theoretical boundary encompassing a broader extent of the ETC circulation within a spiral shape </li> </ul> </li> </ul> <ul> <li>Cyclone Moisture Uptake-Related Variables by ETCs life timesteps: <ul> <li>Total moisture uptake</li> <li>Discrete Moisture Uptake (10-Day Backward)</li> <li>Moisture uptake by layers from the surface to 100 hPa.</li> </ul> </li> </ul> <h3>Dataset Repository Information</h3> <p>This repository contains the dataset for the years 1985-1999. For users interested in datasets from subsequent periods, please refer to the following links:</p> <p>Years 1985-1999: <a href="https://zenodo.org/records/13844378" target="_blank" rel="noopener">Access the dataset here</a><br>Years 2000-2014: <a href="https://zenodo.org/records/13844450" target="_blank" rel="noopener">Access the dataset here</a></p> <p>A <a href="https://github.com/ECMOISTDATABASE/North-Atlantic-Extratropical-Cyclones-database.git">GitHub repository</a> is available, providing example code that demonstrates how to use and handle the dataset effectively. </p>
Data for publication "Chasing ice crystals: interlinking cloud microphysics and dynamics in cloud seeding plumes with Lagrangian trajectories"
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Data from: Does perspective matter? A case study comparing Eulerian and Lagrangian estimates of common murre (Uria aalge) distributions
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Lagrangian Snow Distributions for Sea-Ice Applications, Version 1
This data set provides daily estimates of snow depth and snow density for snow-on-sea-ice in the Arctic Ocean over a 41-year period using a Lagrangian snow-evolution model forced with NASA’s Modern Era Retrospective-Analysis for Research Applications Version 2 (MERRA-2) and the European Centre for Medium Range Weather Forecasts (ECMWF) Reanalysis, generation 5 (ERA5).
Supplementary material to `Accelerating Lagrangian transport simulations on graphics processing units: performance optimizations of MPTRAC v2.5'
<p>This repository contains the supplementary material to the following paper: Hoffmann, L., Haghighi Mood, K., Herten, A., Hrywniak, M., Kraus, J., Clemens, J., and Liu, M., Accelerating Lagrangian transport simulations on graphics processing units: performance optimizations of MPTRAC v2.5, Geoscientific Model Development, submitted, 2023.</p><p> </p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 2000m Y5 & Y6
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 2000m particles releases for the years 2000 (Y5) and 2001 (Y6).</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 2000m Y19 & Y20
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 2000m particles releases for the years 2014 (Y19) and 2015 (Y20).</p>
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