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131 results for “mesoscale”
Data used in "BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean"
<div> <p>This repository contains the data used to generate the figures for the submitted manuscript "BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean".</p> </div> <h3>Contents</h3> <div> <ul> <li> <p>Model input:</p> <ul> <li> <p>INPUTS: ocean model input/grid files</p> </li> <li> <p>PISCES_INPUTS: BGC input files</p> </li> <li> <p>OBC: open boundary forcing </p> </li> <li> <p>WEIGHTS: weight files for ERA interim forcing</p> </li> </ul> </li> </ul> </div> <div> <ul> <li> <p>Manuscript files:</p> <ul> <li> <p>data: files used to generate manuscript images</p> </li> <li> <p>config, src, notebooks: Python code and Jupyter notebooks used to generate images</p> </li> <li> <p>figures, supplementary: manuscript figures and supplementary figures</p> </li> </ul> </li> </ul> </div> <div> </div> <div><strong>Abstract: </strong>"We present BIOPERIANT12, a regional model configuration of the Southern Ocean (SO) at a mesoscale-resolving 1/12 degree. This is a stable, ocean–ice–biogeochemical configuration derived from the Nucleus for European Modelling of the Ocean (NEMO) modelling platform. It is specifically designed to investigate questions related to the mean state, seasonal cycle variability and mesoscale processes in the mixed layer and within the upper ocean (<1000 m). In particular, the focus is on understanding processes behind carbon and heat exchange, systematic errors in biogeochemistry and assumptions underlying the parameters chosen to represent these SO processes. The dynamics of the ocean model play a large role in driving ocean biogeochemistry and we show that over the chosen period of analysis 2000–2009 that the simulated dynamics in the upper ocean provide a stable mean state, as compared to observation-based datasets (themselves subject to biases such as sparsity of data, cloud cover, etc.), and through which the characteristics of variability can be described. Using ocean biomes to delineate the major regions of the SO, the model demonstrates a useful representation of ocean biogeochemistry and partial pressure of carbon dioxide (pCO2). In addition to a reasonable model mean state performance, through model–data metrics BIOPERIANT12 highlights several pathways for improving Southern Ocean model simulations such as the representation of temporal variability and the overestimation of biological biomass."</div>
Mesoscale Low-Level Jet Climatology for the North and Baltic Seas
<p><strong>Mesoscale Low-Level Jet Climatology for the North and Baltic Seas</strong></p> <p>This dataset contains a mesoscale low-level jet (LLJ) climatology for the Baltic and North Seas.</p> <p>The dataset consists of many individual raster layers of LLJ characteristics zipped in the "llj_climatology.zip" file. Each layer is a netCDF4 file, which can be read directly by QGIS, Python, and many other tools. In the "figures" folder, plots showing most of the layers can be found. A more comprehensive description of the layers is given below.</p> <p>Some examples of layers contained:</p> <ul> <li>LLJ rate-of-occurrence</li> <li>LLJ height</li> <li>LLJ duration</li> <li>Wind speed and direction for at LLJ peak</li> <li>Max shear above and below the LLJ peak</li> <li>Wind speed and direction at 100, 150, 200 m</li> <li>Rotor-equivalent wind speed (REWS) for IEA 15 MW reference turbine</li> </ul> <p>Because of strong seasonality in offshore LLJ occurrences, most layers come as long-term means, including the full five years and seasonality-averaged layers. Several aggregate statistics are available for each layer, such as mean, median, and standard deviation. </p> <p>The data was created using the Weather Research and Forecasting model v4.2.1 running a five-year hindcast from 2019-06-26 to 2024-06-26. A two-domain setup was used to downscale ERA5 boundary data to 3 km horizontal grid spacing. See the associated paper for a full data generation process and validation description.</p> <p>Based on user feedback, future versions could be expanded to hold additional layers/variables, such as sector-wise Weibull parameters or time-series samples for representative points. Contact btol@dtu.dk for feedback and requests for future versions. </p> <p><strong>Full list of variables</strong></p> <ul> <li><strong>ws100, ws150, ws200</strong>: wind speed at 100, 150, and 200 meters</li> <li><strong>wd100, wd150, wd200</strong>: wind direction at 100, 150, 200 meters</li> <li><strong>rews_iea15mw</strong>: rotor-equivalent wind speed for the IEA 15 MW reference turbine</li> <li><strong>cf_iea15mw</strong>: capacity factor for the IEA 15 MW reference turbine</li> <li><strong>llj_rate</strong>: LLJ detection rate </li> <li><strong>height_of_llj_max</strong>: height of LLJ peak in meters</li> <li><strong>llj_ws_max</strong>: wind speed of LLJ peak in meters per second</li> <li><strong>llj_wind_direction</strong>: wind direction of LLJ peak in degree</li> <li><strong>llj_duration</strong>: LLJ duration in hours</li> <li><strong>llj_most_prevalent_hour</strong>: most prevalent hour-of-day during LLJ events as hour integers (0-23)</li> <li><strong>llj_most_prevalent_hour_freq</strong>: relative frequency of most prevalent hour-of-day during LLJ events</li> <li><strong>llj_most_prevalent_season</strong>: most prevalent month-of-year during LLJ events as 0-based month integers (0-11 JAN-DEC)</li> <li><strong>llj_most_prevalent_season_freq</strong>: relative frequency of most prevalent month-of-year during LLJ events </li> <li><strong>llj_max_shear_below</strong>: maximum shear between the LLJ peak and the minimum below</li> <li><strong>llj_min_shear_above</strong>: minimum (maximum negative) shear between the LLJ peak and the minimum above</li> <li><strong>height_of_max_shear_below_llj</strong>: height of maximum shear detected below the LLJ peak in meters</li> <li><strong>height_of_min_shear_above_llj</strong>: height of minimum shear detected above the LLJ peak in meters</li> <li><strong>llj_depth</strong>: the depth of the LLJ measured from "height_of_max_shear_below_llj" to "height_of_min_shear_above_llj" in meters</li> <li><strong>llj_rews_iea15mw</strong>: rotor-equivalent wind speed for the IEA 15 MW reference turbine during LLJ events</li> <li><strong>llj_cf_iea15mw</strong>: capacity factor for the IEA 15 MW reference turbine during LLJ events</li> <li><strong>llj_abs_falloff_above</strong>: absolute wind speed fall-off above the LLJ peak in meters per second</li> <li><strong>llj_rel_falloff_above</strong>: relative wind speed fall-off above the LLJ peak </li> <li><strong>llj_abs_falloff_below</strong>: absolute wind speed fall-off below the LLJ peak in meters per second</li> <li><strong>llj_rel_falloff_below</strong>: relative wind speed fall-off below the LLJ peak </li> </ul> <p><strong>Several layers exist for different aggregation and seasons for each variable. Suffixes describe the aggregation (_mean, _median, _std) and season (_DJF, _MAM, _JJA, _SON)</strong></p> <p>This work is part of the FLOW project and was supported by the European Union Horizon Europe Framework Programme (HORIZON-CL5-2021-D3-03-04) under grant agreement no. 101084205.</p>
Coweeta Synoptic Data from 49 sampling sites in the Upper Little Tennessee River Basin from 2009 to 2010 (mesoscale habitat data)
This data was generated as part of synoptic sampling conducted at the Coweeta LTER between June 2009 and May 2010. 49 wadeable streams with low levels of development were sampled throughout the Upper Little Tennessee River Basin in the Southern Appalachians. Effects of riparian vegetative conditions on a suite of channel morphological variables were investigated: active channel width, variability of width within a reach, large wood frequency, mesoscale habitat distributions, median particle size, and percent fines. Stream mesoscale habitat areas for each 150 m stream reach were recorded in this particular dataset. At each site, a uniform 150 meter section of stream was surveyed. Observers kept a running tally of the areas associated with various mesoscale habitat units including, cascades, riffles, pools, alcoves, pocket water, runs, glides, and obstructions.
Moisture-Precipitation Couplings for Mesoscale Convective Systems in Tracking Data and Idealized Simulations
<p>Morphological properties, collocated synoptic conditions, and collocated rainfall for mesoscale convective systems in 1) the ISCCP Convective Tracking (CT) dataset with coincident data from the ERA-Interim (ERA-I) reanalysis and the Multi-Source Weighted-Ensemble Precipitation (MSWEP) product and 2) long-channel radiative-convective equilibrium (RCE) simulations in the System for Atmospheric Modeling (SAM).</p> <p><strong>ISCCP_tracking_colloc.tar.gz </strong>- NetCDF files by year from 2000 to 2004 inclusive including ISCCP-CT morphological properties of MCSs, a series of collocated synoptic variables from ERA-5 (including specific humidity, temperature, vertical velocity, and cloud condensate profiles), and collocated precipitation intensity and accumulation from MSWEP.</p> <p><strong>RCE_colloc_execution1.tar.gz</strong> - NetCDF files including RCE-SAM extent of MCSs (RCE_COL_cluster-sizes_*.nc), as well as collocated synoptic variables. Collocation of synoptic variables is performed for these files by extracting and averaging the variables over grid cells where the precipitation is greater than either its mean (RCE_COL_MEAN_*.nc) or its 99th percentile (RCE_COL_99_*.nc).</p> <p><strong>RCE_colloc_execution2.tar.gz</strong> - NetCDF files including RCE-SAM extent of MCSs (RCE_COL_cluster-sizes_*.nc), as well as collocated synoptic variables. Collocation of synoptic variables is performed for these files by taking either the mean (RCE_COL_MEAN_*.nc) or the 99th percentile (RCE_COL_99_*.nc) value over all grid cells within the MCS.<br><br>For the NetCDF files from RCE output, the numeric value in the file name is the corresponding sea surface temperature from 280 to 310 K.</p>
Impact of Meteorological Factors on the Mesoscale Morphology of Cloud Streets during a Cold Air Outbreak over the western North Atlantic
<ul> <li>Supporting datasets for paper "Impact of Meteorological Factors on the Mesoscale Morphology of Cloud Streets during a Cold Air Outbreak over the western North Atlantic". </li> <li>Those are a subset of the (analyzed) datasets from WRF control simulation "ERA5" in netcdf format. See manuscript for more details. <ul> <li>cld_size.nc: cloud object size</li> <li>cld_ort_2020-03-01_15_00_00.nc: cloud object at 15:00 UTC</li> <li>hydro-02-2020-03-01_15/00/00.nc: water path sample data at 15:00 UTC</li> <li>wrfout_d02_2020-03-01_15/00/00: wrf output sample data at 15:00 UTC</li> </ul> </li> </ul>
Dataset for the ``Fast atmospheric response to a cold oceanic mesoscale patch in the north-western tropical Atlantic" publication
<p>The dataset presented here contains the files needed to produce the results presented in the publication "Fast atmospheric response to a SST mesoscale cold patch in the north-western subtropical Atlantic" submitted to the <em>Journal of Geophysical Research: Atmospheres</em>. The scripts that read and produce these files are publicly available at <a href="https://github.com/ClauClouds/SST-impact/">https://github.com/ClauClouds/SST-impact/</a> and can also be found in this repository (code_python.zip). This Zenodo data repository includes the following datasets:</p> <ul> <li> <p>Radiosonde data from 2-3 February 2020 (Stephan et al., 2021)</p> </li> <li> <p>Doppler lidar, and ARTHUS Raman lidar variables data from 2-3 February 2020,</p> </li> <li> <p>GOES-East (Geostationary Operational Environmental Satellite - East) Binary Cloud Mask (BCM) and Cloud Optical Depth (COD) products, provided at 2 km grid spacing every 10 minutes. They come from the GOES-R Advanced Baseline Imager (ABI) (Schmit et al., 2017), available at <a href="https://www.ncei.noaa.gov/products/satellite/goes-r-series.Data">https://www.ncei.noaa.gov/products/satellite/goes-r-series.Data</a> and they are provided for the 2-3 February 2020.</p> </li> <li> <p>Multi-scale Ultra-high Resolution (MUR) product (JPL MUR MEaSUREs Project, 2015,183 (Chin et al., 2017)) averaged between the 2nd and 3rdfor the 2nd of February 2020. The MUR product is an analysis product provided on a daily basis that combines different satellite (infrared at high and medium resolutions and microwave products) and in-situ data (Chin et al., 2017).</p> </li> <li> <p>W-band radar data post-processed for the purposes of the publication. The original W-band radar data used are publicly accessible at <a href="https://howto.eurec4a.eu/merian_cloudradar.html">https://howto.eurec4a.eu/merian_cloudradar.html</a> and can be downloaded via <a href="https://eurec4a.aeris-data.fr/">AERIS data portal</a>. See more details and specific DOI below.</p> </li> </ul> <p>The present dataset is structured as follows:</p> <ul> <li> <p>diurnal_cycle_removed_vars: files containing the time series of the variables without noise and diurnal cycle (filenames with extended dates 20200202 and 20200203)</p> </li> <li> <p>diurnal_cycle: files containing the diurnal cycle of each variable used in the publication</p> </li> <li> <p>binned_sst_vars: files containing variables binned in terms of SST, used to derive the plots in the paper.</p> </li> <li> <p>satellite_data: a folder containing all satellite data used in the publication</p> </li> </ul> <p>Additional data used in the publication, that are processed via the scripts contained in the link mentioned above, are available online at the following urls:</p> <ul> <li> <p>cloud radar observations can be directly obtained from the public dataset identifiable via DOI: <a href="https://doi.org/10.25326/235">https://doi.org/10.25326/235</a> (Acquistapace et al., 2022)</p> </li> <li> <p>ASCAT wind field data and corresponding MUR SST data are available from the NASA JPL PODAAC platform (<a href="https://podaac.jpl.nasa.gov/">https://podaac.jpl.nasa.gov/</a>)</p> </li> <li> <p>hourly ERA5 (Hersbach et al., 2020) gridded fields (available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=form, last accessed March 2022) of the following variables: SST, water vapor mixing ratio, air temperature, and horizontal wind components. </p> </li> </ul> <p><br> </p> <p>References;</p> <p>Acquistapace et al., 2022, ESSD, <a href="https://doi.org/10.25326/235">https://doi.org/10.25326/235</a>.</p> <p>Schmit, T. et al., 2017, QJRMS, <a href="https://doi.org/10.1175/BAMS-D-15-00230.1">https://doi.org/10.1175/BAMS-D-15-00230.1</a></p> <p>Hersbach et al., 2020, QJRMS, <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3803">https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3803</a></p> <p>Stephan et al., 2021, ESSD, <a href="https://doi.org/10.5194/essd-13-491-2021">https://doi.org/10.5194/essd-13-491-2021</a></p> <p>Chin, T. M. et al., (2017), RS, <a href="https://doi.org/10.1016/j.rse.2017.07.029">https://doi.org/10.1016/j.rse.2017.07.029</a></p>
Turbulent kinetic energy over large wind farms observed and simulated by the mesoscale model WRF (3.8.1)
<p>This repository contains the WRF configuration files necessary to reproduce the simulations <br> as described in Siedersleben et al. 2019 (https://doi.org/10.5194/gmd-2019-100)</p> <p>The file windturbines_GMD.txt contains the locations of <br> all windturbines implemented in the simulations. The corresponding attributes of each <br> wind turbine type is described in the wind-turbine-xx.tbl. Be aware that all windturbines use the same power and thrust coefficients only the different hub heights and rotor diameters are taken into account as described in Siedersleben et al. (2019).</p> <p>The namelist.input_nameOfSimulation files necessary to run the simulations are provided in this repository as well. You may notice that <br> there are less namelist files than simulations. The simulations not using a TKE source use the same namelists as the ones with a TKE a source. However, the WRF model needs to be recompiled using the manipolated module_wind_fitch.F (you find this file in this repository). The sensitivity studies investigating the impact of the uncertainties in the power and thrust coefficients use the namelist of the control simulation CNTRb, but with manipulated wind-turbine-x_modMin/Max.tbl wind turbine files.</p> <p>The two python files get_era5*.py can be used to retrieve the ERA5 data, driving the WRF model. <br> Note that the dates and pathes have to be adjusted in the python files. <br> After downloading the surface and model level data some postprocessing <br> is necessary as described nicely here: "http://valcap74.blogspot.com/2017/10/how-to-run-wrf-model-driven-by-era5-on.html". For this<br> purpose the simple script called postProcessERA5 (based on the blog entry mentioned above) can be used.</p>
Biogeochemical observations in adjacent mesoscale eddies of opposite polarity
<p>Datasets used for the analyses reported in the manuscript titled "Biogeochemical dynamics in adjacent mesoscale eddies of opposite polarity".</p> <p>For all files, the suffix HL4 indicates the expedition HOE-LEGACY 4, while the suffix MESOSCOPE indicates the MESO-SCOPE expedition.</p> <p>Temperature profiles measured underway across adjacent eddies are saved in files UnderwayTemperature*.csv. Station coordinates are saved in UnderwayCoordinates*.csv</p> <p>Shipboard vertical profiles of dissolved oxygen, chlorophyll fluorescence, and potential density anomaly are saved in files TransectOxygen*.csv, TransectFluorescence*.csv, and TransectSigma*.csv, respectively. Station coordinates are saved in TransectCoordinates*.csv</p> <p>Inorganic nutrient concentrations measured across adjacent eddies are saved in files EddyNutrients*.csv.</p> <p>Particulate carbon, chlorophyll a, beam attenuation, and chlorophyll fluorescence in the eddy centers are saved in file ParticlePigmentComparison_15m.csv and ParticlePigmentComparison_DCM.csv for the depth of 15 m and the depth of the DCM, respectively.</p> <p>Cell counts from flow cytometry in the eddy centers are saved in files FlowCytometry_cyclone_*.csv and FlowCytometry_anticyclone_*.csv. This files also report the depth of the DCM for each eddy center.</p> <p>Imaging FlowCytobot (IFCb) measurements during MESO-SCOPE are reported in files IFCB_Class_DCM&15m_MESOSCOPE.csv for classes and IFCB_Genera_DCM_MESOSCOPE.xlsx for genera.</p>
A High-Resolution Dataset of Global Urban Fraction for Mesoscale Urban Modelling
<p>Coupled urban-atmospheric models are extensively used to understand the urban environment and its impact on atmospheric processes. A common requirement of these models is information about the “urban fraction” (fraction of model grid covered by impervious surface area (ISA)). The European Space Agency (ESA) WorldCover product provides a global land cover map for the base year of 2020 and 2021 at a spatial resolution of 10 m. The dataset is based on Sentinel-1 and Sentinel-2 data with an overall accuracy of 74.4% (2020) and 76.7% (2021). In this study we process the WorldCover dataset and provide a ready-to-use “urban fraction” that can be incorporated in urban modelling systems. The dataset contains GeoTIFF and Weather Research and Forecasting Pre-processing System (WRF-WPS) format files for 1, 0.5, 0.25, 0.009 (~1 km), 0.0027 (~300 m), and 0.0009 (~100 m) degree spatial resolutions. The GeoTIFF files can be converted to other urban mesoscale modelling systems. Please check the README.txt for more information on using the dataset.</p> <p>Note: version 2.0.0 uses WorldCover 2021 v200 dataset for processing of urban fractions, while version 1.0.0 uses WorldCover 2020 v100 dataset.</p> <p>For more information please see here: <a href="https://1drv.ms/w/s!Ai5IcIuv5U4DioElr0E8CEq0DE3CSw?e=Wdp9i4">https://1drv.ms/w/s!Ai5IcIuv5U4DioElr0E8CEq0DE3CSw?e=Wdp9i4</a></p>
Supporting data for the manuscript: "The Role of Mesoscale Cloud Morphology in the Shortwave Cloud Feedback"
<p>This repository contains supporting data for the manuscript "The Role of Mesoscale Cloud Morphology in the Shortwave Cloud Feedback" in <em>Geophysical Research Letters</em>. Detailed descriptions of these datasets can be found in the manuscript text as well as in the file descriptions.</p>
Bridging length scales in organic mixed ionic-electronic conductors through internal strain and mesoscale dynamics
<p>Understanding structural and dynamic properties of inherently disordered systems at the mesoscale is crucial. This is particularly important in organic mixed ionic-electronic conductors (OMIECs), which undergo significant and complex structural changes when operated in electrolyte. In this study, we investigate the mesoscale strain, reversibility, and dynamics of a model OMIEC material under external electrochemical potential using operando X-ray photon correlation spectroscopy. Our results reveal mesoscale strain and structural hysteresis that depend on the sample's cycling history, establishing a comprehensive kinetic sequence bridging the macroscopic and microscopic behaviors of OMIECs. Furthermore, we uncover equilibrium and non-equilibrium dynamics of charge carriers and material doping states, highlighting the unexpected coupling between charge carrier dynamics and mesoscale order. These findings advance our understanding of the structure-dynamics-function relationships in OMIECs, opening pathways for designing and engineering materials with improved performance and functionality in non-equilibrium states during device operation.</p>
Data for "The role of ocean mesoscale variability in air-sea CO2 exchange: a global perspective"
<p>Processed model data for article "The role of ocean mesoscale variability in air-sea CO2 exchange: a global perspective"</p>
Scripts and datas for "A unified energy-constrained mesoscale parameterisation for ocean climate models".
<p>Scripts and datasets used for creating the results of a submitted work :</p> <p><strong>R. Torres, R. Waldman, G. Madec, C. de Lavergne, R. Séférian and J. Mak</strong>: <em>A unified energy-constrained mesoscale parameterisation for ocean climate models. </em>(submitted in JAMES).<em><br></em></p> <p>Datas include eORCA1 mesh files (directory "mesh") and simulations output (direcotories "runs/*/output"). However, to avoid heavy archive, only 2D simulations output are provided. The post-processed 3D variables are first pre-processed for each simulations (directories "runs/*/post/post/post_averag_1995-2017").</p> <p>The reference EKE of <a href="https://doi.org/10.1029/2023gl104688">Torres et al. (2023)</a> is provided (directory "obs/postprocessed_kinetic_energy") while other observational reference datasets have to be download by the user (e.g. <a href="https://www.ncei.noaa.gov/archive/accession/NCEI-WOA18">World Ocean Atlas 2018</a>, <a href="https://gmd.copernicus.org/articles/13/3643/2020/">Tsujino et al. (2020)</a> and <a href="https://www.bodc.ac.uk/data/published_data_library/catalogue/10.5285/04c79ece-3186-349a-e063-6c86abc0158c/">RAPID</a>)</p> <p>IPython notebooks for computing and plotting metrics are provided :</p> <ul> <li><em>james-eke-heat_budget.ipynb</em> : plots for heat transport and global heat storage (section 4.1)</li> <li><em>james-eke-southern_ocean.ipynb</em> : plots for Southern Ocean (section 4.2) analysis</li> <li><em>james-eke-north_atlantic.ipynb</em> : plots for North Atlantic and Labrador Sea (section 4.3) analysis</li> <li><em>james-eke-timeseries.ipynb</em> : plot 0D metric timeseries for simulations (including spin-up)</li> </ul> <p>Note however that these scripts use the author python library XOCE availbale on GitHub: https://github.com/torresr-cnrm/xoce. All the scripts have been runned using the version 0.2 of XOCE. Feel free to contact (romain.torres@meteo.fr) for any help in installing and using this library.</p>
Data accompanying manuscript: 'Antarctic subglacial topography mapped from space reveals complex mesoscale landscape dynamics'
<p>This upload contains the data which accompanies the manuscript: 'Antarctic subglacial topography mapped from space reveals complex mesoscale landscape dynamics'.</p> <p><strong>Metrics calculated for each of the 4269 50 km by 50 km regions</strong></p> <table> <tbody> <tr> <td>Filename (IFPA)</td> <td>Filename (Bedmachine)</td> <td>Filename (Bedmap3)</td> <td>Description</td> </tr> <tr> <td>x_ifpa.nc<br>y_ifpa.nc</td> <td> </td> <td> </td> <td>X and Y coordinates</td> </tr> <tr> <td>mean_ifpa.nc or ifpa_mean.nc</td> <td>bedmach_mean.nc</td> <td> </td> <td>Mean elevation (m)</td> </tr> <tr> <td> <p>ifpa_count.nc<br>ifpa_count_max_20.nc<br>ifpa_count_max_100.nc<br>ifpa_count_max_250.nc</p> </td> <td>bedmach_count.nc<br>bedmach_count_max_20.nc<br>bedmach_count_max_100.nc<br>bedmach_count_max_250.nc</td> <td> </td> <td> <p>The number of hills with a 50 m prominence within a 5 km neighbourhood <br>(or 20 m, 100 m, 250 m respectively)</p> </td> </tr> <tr> <td>ifpa_b1_5km.nc<br>ifpa_b1_thickness.nc</td> <td>bedmach_b1_5km.nc<br>bedmach_b1_thickness.nc</td> <td> </td> <td>The fourier fractal dimension for wavelengths greater than 5 km or the ice thickness respectively</td> </tr> <tr> <td>ifpa_std_deslope.nc<br>i_std_l.nc</td> <td>bedmach_std_deslope.nc<br>b_std_l.nc</td> <td> </td> <td>The standard deviation:<br>- with the best fit slope removed<br>- of some long wavelength components of the fourier spectrum</td> </tr> <tr> <td>ifpa_wav_max_power.nc</td> <td>bedmach_wav_max_power.nc</td> <td> </td> <td>The wavelength in the Fourier spectrum with the maximum power</td> </tr> <tr> <td>ifpa_rms_slope.nc<br>i_rms_slope_h.nc</td> <td>bedmach_rms_slope.nc<br>b_rms_slope_h.nc</td> <td> </td> <td>The RMS slope of:<br>- the bed elevation<br>- some short wavelength components of the fourier spectrum</td> </tr> <tr> <td>ifpa_rms_curvature.nc</td> <td>bedmach_rms_curvature.nc</td> <td> </td> <td>The RMS curvature of the bed elevation</td> </tr> <tr> <td> </td> <td>source.nc</td> <td> </td> <td>The method used to calculate the bed topography (Bedmachine only)</td> </tr> <tr> <td> </td> <td> </td> <td>mean_nearest.nc</td> <td>The mean distance from each IFPA grid point to the nearest Bedmap3 data point</td> </tr> <tr> <td> </td> <td> </td> <td>bedmap3_count.nc</td> <td>The number of Bedmap3 data points within the region</td> </tr> </tbody> </table> <p> </p> <p><strong>Datasets required for plotting</strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> </tr> <tr> <td>Groundingline_Antarctica_v2.shp</td> <td>Antarctic grounding line </td> </tr> <tr> <td>ECR_features.shp</td> <td>Outline of significant features within the example regions chosen</td> </tr> <tr> <td>IFPA_bed.nc</td> <td>OLD VERSION of IFPA bed topography map for Antarctica</td> </tr> <tr> <td> <p>IFPA_bed_C50.nc</p> </td> <td>IFPA bed topography map for Antarctica (without radar correction)</td> </tr> </tbody> </table> <p><strong>To plot the figures, you will either require the following datasets: </strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> </tr> <tr> <td>IFPA_figures_data.zip</td> <td>Additionally data to plot figures 1,6,8 and 9 </td> </tr> <tr> <td> <p>HA_data.csv<br>HB_data.csv<br>RSB_data.csv</p> </td> <td>For Highland A, Highland B and Recovery Subglacial Basin<br>IFPA (radar corrected), IFPA (not radar corrected), Bedmachine v3, and ice-penetrating radar profiles</td> </tr> <tr> <td> <p>HA_data_ifpa.csv<br>HB_data_ifpa.csv<br>RSB_data_ifpa.csv</p> </td> <td> <p>For Highland A, Highland B and Recovery Subglacial Basin<br>IFPA (radar corrected) map for the region crossed by the ice-penetrating radar profile</p> </td> </tr> </tbody> </table> <p><strong>or, the figures can be regenerated using the following datasets, which are available at the listed DOIs</strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> <td>Reference</td> <td>DOI</td> </tr> <tr> <td>GaplessREMA100.nc</td> <td>Gapless REMA Antarctica dataset at 100m resolution</td> <td>Dong et al. (2022)</td> <td>10.1016/j.isprsjprs.2022.01.024</td> </tr> <tr> <td>BedMachineAntarctica-v3.nc</td> <td>MEaSURES BedMachine Antarctica bed topography map version 3</td> <td>Morlighem et al. (2020)</td> <td>10.5067/FPSU0V1MWUB6</td> </tr> <tr> <td>antarctica_ice_velocity_450m_v2.nc</td> <td>ITSLIVE Antarctic velocity map</td> <td>Gardner et al. (2019)</td> <td>10.5067/6II6VW8LLWJ7</td> </tr> <tr> <td>antarctic_ice_vel_phase.nc</td> <td>MEaSURES Antarctic velocity map</td> <td>Mouginot et al. (2019)</td> <td>10.5067/PZ3NJ5RXRH10</td> </tr> <tr> <td>UTIG_2010_ICECAP_AIR_BM3.csv</td> <td>Bed elevation from airborne radar from the UTIG Icecap survey</td> <td>Wright et al. (2012)</td> <td>10.1029/2011JF002066</td> </tr> <tr> <td>BAS_2012_ICEGRAV_AIR_BM3.csv</td> <td>Bed elevation from airborne radar from the BAS Icegrav survey</td> <td>Forsberg et al. (2018)</td> <td>10.1144/SP461.17</td> </tr> </tbody> </table> <p><strong> </strong></p>
Output of simulations for "Atmosphere Response to an Oceanic Sub-mesoscale SST Front: A Coherent Structure Analysis": Part 1
<p>This dataset contains the output of the simulations for the paper "Atmosphere Response to an Oceanic Sub-mesoscale SST Front: A Coherent Structure Analysis" doi: [TO BE COMPLETED]. This is part 1. It contains data for the S1 simulation.</p>
Output of simulations for "Atmosphere Response to an Oceanic Sub-mesoscale SST Front: A Coherent Structure Analysis": Part 2
<p>This dataset contains the output of the simulations for the paper "Atmosphere Response to an Oceanic Sub-mesoscale SST Front: A Coherent Structure Analysis" doi: [TO BE COMPLETED]. This is part 2. It contains the remaining of data for the S1 simulation, the data of the reference simulations RefC and RefW, and the data for the sensitivity analysis of the supplementary material.</p>
Supplementary datasets and code for: Instability and mesoscale eddy fluxes in an idealized 3-layer Beaufort Gyre
<p>This dataset contains the configuration files and scripts used to initialise the Aronnax simulations described in the manuscript; processed output from the simulations; and code used for the linear stability analysis. </p>
Rain gauge data used in the study "Characteristics of Precipitation and Mesoscale Convective Systems over the Peruvian Central Andes in Multi 5-Year Convection-Permitting Simulations"
<p>The rain gauge data in Peru and Brazil used in the study,</p> <p>Yongjie Huang, Ming Xue, Xiao-Ming Hu, et al. Characteristics of Precipitation and Mesoscale Convective Systems over the Peruvian Central Andes in Multi 5-Year Convection-Permitting Simulations. <em>ESS Open Archive .</em> November 14, 2023.<br><span>DOI: <a href="https://doi.org/10.22541/essoar.170000370.07634797/v1" target="_blank" rel="noopener noreferrer">10.22541/essoar.170000370.07634797/v1</a></span></p> <p><span>The original data source:</span></p> <ul> <li>The rain gauge data in Peru are available at <a href="https://piscoprec.github.io/webPISCO/en/raingauges">https://piscoprec.github.io/webPISCO/en/raingauges</a> (last access: 18 July 2021).</li> <li>The rain gauge data in Brazil are available at <a href="https://bdmep.inmet.gov.br">https://bdmep.inmet.gov.br</a> (last access: 19 January 2023).</li> </ul>
Shock physics mesoscale modeling of shock stage 5 and 6 in ordinary and enstatite chondrites: modeling data
<p>These data are related to:</p> <p>Moreau, J., Kohout, T., Wünnemann K., Halodova, P., Haloda, J., 2019.<br> Shock physics mesoscale modeling of shock stage 5 and 6 in ordinary and enstatite chondrites.<br> Icarus, 332, 50-65. <a href="https://doi.org/10.1016/j.icarus.2019.06.004">https://doi.org/10.1016/j.icarus.2019.06.004</a></p> <p>Any use of these files, scripts (partial or complete) in research papers, please reference the paper above + Moreau et al. (2017, 2018) (references compiled in the above-mentioned paper).</p> <p>To use these files, you will need:<br> - authorized access to the iSALE shock physics code (iSALE-Dellen version) re-compiled with our modifications, with reference<br> to the manual in your work<br> - access to the pySALEPlot tool for iSALE users made by T. Davison acknowledged in your work<br> - running the iSALE models to generate the different jdata.dat files (average size of a jdata.file is 7 Go)<br> - python<br> - Ubuntu or macOS</p> <p> </p> <p>(more info in README.txt file)</p>
Cyclostrophic corrections of AVISO/DUACS surface velocities and its application to mesoscale eddies in the Mediterranean Sea
<p>We apply an optimised iterative method to retrieve with best accuracy the cyclogeostrophic corrections on fifteen years (2000-2015) of surface geostrophic velocity fields provided by AVISO/DUACS for the Mediterranean Sea. The initial gridded altimeter products were produced by SSALTO/DUACS and distributed by the Copernicus Marine Environment Monitoring Service (marine.copernicus.eu). </p> <p>Each netCFD file corresponds to the two cyclogeostrophic velocity components zonal u and meridional v. </p> <p>(ssu_adt_DYNED_MED_cyclo_2000_2015.nc & ssv_adt_DYNED_MED_cyclo_2000_2015.nc)</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.