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129 results for “lagrangian”
Lagrangian moisture sources for an arid region in northeast Greenland
<p>dataset of lagrangian moisture sources (February 1979-May 2017) that was used in a research article with the title "Lagrangian detection of precipitation moisture sources for an arid region in northeast Greenland: relations to the North Atlantic Oscillation, sea ice cover and temporal trends from 1979 to 2017" published in the Weather and Climate Dynamics journal (<a href="https://wcd.copernicus.org/articles/2/1/2021/">https://wcd.copernicus.org/articles/2/1/2021/</a>, more information in readme.md)</p> <p><strong>When using this dataset, please refer to the original publication in addition to this Zenodo repository:</strong></p> <p>- Schuster, L., Maussion, F., Langhamer, L., Moseley, G.E.: Lagrangian detection of precipitation moisture sources for an arid region in northeast Greenland: relations to the North Atlantic Oscillation, sea ice cover, and temporal trends from 1979 to 2017, Weather and Climate Dynamics, 2, 1-17, https://doi.org/10.5194/wcd-2-1-2021, 2021</p>
Lagrangian Atmospheric Model output for the Control, Onset and Development experiments
<p><span><span><span><span><span><span><span><span><span><span><span>This dataset consists of model outputs from the Control, Onset and Development experiments. Each experiment has six ensembles which were integrated for three years. 6-hourly instantaneous surface (1000hPa) zonal and meridional wind speed (m/s) between 31°S-31°N is recorded in uv1000hPa_{Control, Onset, Development}_ens{1, 2, 3, 4, 5, 6}.dat. 6-hourly accumulated precipitation (mm) between 32°S-32°N is stored in precip_{ Control, Onset, Development}_ens{1, 2, 3, 4, 5, 6}.dat. Note the precip.*dat records accumulated rainfall for the past 6 hours. The missing data is recorded as nan.</span></span></span></span></span></span></span></span></span></span></span></p>
Trajectories for 'A Lagrangian view of the atmospheric river related to the heavy rainfall of July 2020 in Japan: Importance of moisture gain during transport''
<p>This is the trajectories output for the article 'A Lagrangian view of the atmospheric river related to the heavy rainfall of July 2020 in Japan: Importance of moisture gain during transport''.</p> <p>The settings for FLEXPART-WRF is also included.</p>
Trajectory Movie for the article 'A Lagrangian view of the atmospheric river related to the heavy rainfall of July 2020 in Japan: Importance of moisture gain during transport'
<p>The supplement movie for the article 'A Lagrangian view of the atmospheric river related to the heavy rainfall of July 2020 in Japan: Importance of moisture gain during transport'.</p>
Data set - Lagrangian observations and modelling of turbulence along a tidally influenced river - Refined model
<p>The 'Dataset_Kaipara_Lagrangian_refined.mat' file contains Lagrangian observations collected in the Kaipara river and corresponding model predictions, after refinement of the model. </p> <p> </p> <p>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. </p>
Tracing the origin of the South Asian summer monsoon precipitation and its variability using a novel Lagrangian framework
<p>moc.zip: This dataset was used to calculate the meridional overturning water-mass stream function (Fig.2) and net evaporation which are responsible for net precipitation over the South Asian landmass during June to September months (Fig.3).</p> <p>track_amp.zip: The vertically integrated horizontal water-mass flux was computed from this data (Fig.4).</p> <p>bob_path.zip: Net precipitation (Fig.5) and net evaporation (Fig.6) calculated from Lagrangian water-mass trajectories that have crossed over the Bay of Bengal at least once and fallen down over the South Asian landmass. Also, Table 2 was prepared using this data.</p> <p>evap_precip_basins.zip: This file contain datasets that were used to compute the contribution of separate basins to the South Asian summer monsoon precipitation (Table 1) and also how the spatial distribution related to each basin (Fig.7)</p> <p>interannual.zip: Interannual precipitation variability was obtained from this dataset.</p>
Supplementary material to `Accelerating Lagrangian transport simulations on graphics processing units: performance optimizations of MPTRAC v2.6'
<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 v2.6, Geoscientific Model Development, submitted, 2023.</p>
Dataset for "McSnow 2.0: Explicit habit-prediction in a Lagrangian super-particle ice microphysics model"
<p>Data and plot scripts to create figures included in the paper draft "McSnow 2.0: Explicit habit-prediction in a Lagrangian super-particle ice microphysics model" (submitted to JAMES).</p> <p>This work has been funded by the German Science Foundation (DFG) under grant SE 1784/3-1, project ID 408011764 as part of the DFG priority program SPP 2115 on radar polarimetry.</p>
Parcels-WAOM model data: Lagrangian particle trajectories
<p>This data set includes the results of Lagrangian particle tracking experiments with Parsels in Weddell and Ross seas. </p> <p>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).</p> <p>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. </p>
NetCDF data matrix with Lagrangian dispersal simulation output
<p>zipped NetCDF file (unzipped ~444GB)</p> <p>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.<br>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.)</p> <p>data variable: <br>particle_number [111,007,858,176 values, float32]</p> <p>dimensions: <br>lon_bin [length 1468, float32]<br>lat_bin [length 1601, float32]<br>period [length 3, object '0-2','14-28','0-28']<br>station [length 32, object 'DK_044','FR_0206'....(all station names)]<br>year [length 4, object '2019'...'2022']<br>offset [length 123, object '000'...'122']</p>
Daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions (Part 1)
<p>A database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions to study the fate of sea ice in the ‘New Arctic’.</p> <p>Files are multi-part zip files containing trajectory and ancillary data on an annual basis over a sea ice year.</p>
Daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions (Part 3)
<p>A database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions to study the fate of sea ice in the ‘New Arctic’.</p> <p>Files are multi-part zip files containing trajectory and ancillary data on an annual basis over a sea ice year.</p>
Daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions (Part 2)
<p>A database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions to study the fate of sea ice in the ‘New Arctic’.</p> <p>Files are multi-part zip files containing trajectory and ancillary data on an annual basis over a sea ice year.</p>
Daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions (Part 2a)
<p>A database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions to study the fate of sea ice in the ‘New Arctic’.</p> <p>Files are multi-part zip files containing trajectory and ancillary data on an annual basis over a sea ice year.</p>
The first COVID-19 related Lockdown Paris Lagrangian model Footprints - Publication dataset
<p>This repository contains the Lagrangian model (LPDM) footprints and concentrations time-series modelled over Paris and the surrounding region of Ile-de-France for the period between March 1 and June 1 of 2019 or the year prior to the first COVID-19 related lockdown, and 2020 the year during which the lockdown occured.</p>
Pollution Tracks in Lagrangian Framework Dataset
<p>This dataset contains hand-logged positions of pollution tracks containing within MODIS granule at six locations (Come By Chance Oil Refinery, Norilsk Nickel Refinery, and effusive volcanoes at Kilauea, Ambrym, Yasur, and Mt. Michael) from 2015 -2017. Also included are PartMC input data for simualtions of Kilauea on day 6/13/2015 which can be obtained from <a title="Original URL: https://github.com/compdyn/partmc. Click or tap if you trust this link." href="https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fcompdyn%2Fpartmc&data=05%7C02%7Cmatt.christensen%40pnnl.gov%7C4eba9070676b4d1db19b08dc4edc0dc5%7Cd6faa5f90ae240338c0130048a38deeb%7C0%7C0%7C638471954798843224%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=NQfVqCm887UHkqB5t7Ou3MYoF3NVNdE52iGuZuiM9r0%3D&reserved=0" target="_blank" rel="noopener noreferrer">https://github.com/compdyn/partmc.</a></p>
A 40-year moisture source data for Tibetan Plateau precipitation using a 3D Lagrangian approach
<p>This repository contains the dataset that reproduces the work by Cheng et al. (2024). The zip files contain data in netCDF format. They consist of</p> <ol> <li>Data of Figures 1-5 in the article (Cheng et al. 2024)</li> <li>Moisture sources of precipitation in the Tibetan Plateau (TP) <ul> <li><code>TP_moisture_source_1971-2010.nc</code>: Global map (lon,lat,time) of 40 years of moisture source (mm/day) contributing to the TP precipitation based on the FLEXPART-WaterSip approach</li> <li><code>SR_1971-2010_TP_grids_1x1_XXX.nc</code>: Fractional contributions of different circulation regimes to each of 302 1˚x1˚ grids on the TP</li> </ul> </li> <li>Multi-product ensemble mean precipitation and evapotranspiration</li> <li>Boundary data of TP river basins used in the study</li> </ol> <p>For any enquiries, feel free to contact Dr. Tat Fan (Franklin) Cheng at <a href="mailto:franklin.cheng@ust.hk">franklin.cheng@ust.hk</a>. Please cite our two recent articles if you found the dataset useful. Thank you!</p> <p><strong>References</strong></p> <blockquote> <p>Cheng, T. F., Chen, D., Wang, B., Ou, T. & Lu, M. (2024). Human-induced warming accelerates local evapotranspiration and precipitation recycling over the Tibetan Plateau. <em>Commun Earth Environ </em>5, 388. <a href="https://doi.org/10.1038/s43247-024-01563-9">https://doi.org/10.1038/s43247-024-01563-9</a> </p> <p>Cheng, T. F., & Lu, M. (2023). Global Lagrangian Tracking of Continental Precipitation Recycling, Footprints, and Cascades. <em>Journal of Climate</em>, 36, 1923–1941. <a href="https://doi.org/10.1175/JCLI-D-22-0185.1">https://doi.org/10.1175/JCLI-D-22-0185.1 </a></p> </blockquote>
Exploring water accumulation dynamics in the Pearl River estuary from a Lagrangian perspective.
<p>The data for the study in the PRE, paper named 'Exploring water accumulation dynamics in the Pearl River estuary from a Lagrangian perspective'.</p>
Setup and Dataset for the Validation of an Eulerian-Lagrangian Coupling Method in the m-AIA framework
<p>This repository holds data files, code information and property files which are used to conduct performance analyses of a <br>Parallel Eulerian-Lagrangian Coupling Method. </p>
Data for "Investigating temporary capture in the Sun-Jupiter three-body system via Lagrangian coherent structures"
<p>The supplementary online materials for the paper "Investigating temporary capture in the Sun-Jupiter three-body system via Lagrangian coherent structures" consist of two parts.</p> <h3>Part 1: Coordinates of the LCS Surfaces in Figure 8</h3> <p>This part contains the coordinates of the local maxima of FTLE (Finite-Time Lyapunov Exponent) values that compose the Lagrangian Coherent Structures (LCS) surface presented in Figure 8 of the paper. These coordinates are crucial for understanding the formation and characteristics of the LCS in the three-body system.</p> <h3>Part 2: Orbital States of Asteroid 2002 GV28 Based on the High-Fidelity Ephemeris Model from Section 2.2 of the Paper</h3> <p>This part includes the orbital states of asteroid 2002 GV28, propagated using a high-fidelity ephemeris model as described in Section 2.2 of the paper. The data provides information on the asteroid's state in two different coordinate systems:</p> <ol> <li> <p><strong>Heliocentric J2000.0 Ecliptic Coordinate System:</strong></p> <ul> <li>This dataset includes the position and velocity of the asteroid relative to the Sun at various time steps.</li> <li>The columns typically include: Time (Julian Date), X (km), Y (km), Z (km), Vx (km/s), Vy (km/s), Vz (km/s).</li> </ul> </li> <li> <p><strong>Sun-Jupiter Rotating Coordinate System:</strong></p> <ul> <li>This dataset provides the position and velocity of the asteroid relative to the Sun-Jupiter rotating frame.</li> <li>The columns typically include: Time (Julian Date), X (normlized), Y (normlized), Z (normlized), Vx (normlized), Vy (normlized), Vz (normlized).</li> </ul> </li> </ol> <p>Each entry in these datasets corresponds to specific time steps during the propagation, providing a comprehensive view of the asteroid's trajectory in both coordinate systems.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.