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131 results for “mesoscale”
Model intercomparison of medicane Ianos using ten mesoscale numerical frameworks
<p>The dataset provides numerical simulations of the high-impact medicane Ianos of September 2020. It is based on a collective effort with five mesoscale models to look for a robust response among ten numerical frameworks used in the community involved in the networking activity of the EU COST Action "MedCyclones" <a href="https://medcyclones.eu/">https://medcyclones.eu/</a></p> <p>The five mesoscale models are:</p> <ul> <li>The BOLAM hydrostatic model and the MOLOCH non-hydrostatic, fully compressible model developed at CNR-ISAC available upon request to <a href="mailto:dinamica@isac.cnr.it">dinamica@isac.cnr.it</a></li> <li>The Met Office Unified Model (MetUM) available for use under a closed licence agreement, further information at <a href="http://www.metoffice.gov.uk/research/modelling-systems/unified-model">http://www.metoffice.gov.uk/research/modelling-systems/unified-model</a></li> <li>The Meso-NH mesoscale non-hydrostatic model of the French research community freely available under CeCILL-C license agreement on <a href="http://mesonh.aero.obs-mip.fr">http://mesonh.aero.obs-mip.fr</a> with two variants included: <ul> <li>one run at Centre National de Recherches Météorologiques (MESONH-CNRM)</li> <li>one run at Laboratoire d’Aérologie (MESONH-LAERO)</li> </ul> </li> <li>The WRF (Weather Research and Forecasting) non-hydrostatic, fully compressible model freely available at <a href="https://github.com/wrf-model/WRF/releases">https://github.com/wrf-model/WRF/releases</a> with five variants included: <ul> <li>one run at the Aristotle University of Thessaloniki (WRF-AUTH)</li> <li>two run at CNR-ISAC (WRF-ISAC and WRF-ISAC-2)</li> <li>one run at the National Observatory of Athens (WRF-NOA)</li> <li>one run at the University of the Balearic Islands (WRF-UIB)</li> </ul> </li> </ul> <p>Four sets of simulations are provided:</p> <ul> <li>Control simulations obtained by initialising the models at 00 UTC on 15 September 2020 and using 6-h operational analyses from the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) as initial and lateral boundary conditions. The horizontal grid spacing is set to 10 km, which approximately matches the resolution of IFS analyses and requires parameterization of deep convection.</li> <li>A first sensitivity test obtained by initialising the models 12 h earlier at 12 UTC on 14 September 2020.</li> <li>A second sensitivity test obtained by using ECMWF Reanalysis v5 (ERA5), which provides higher frequency (hourly) but lower spatial resolution (about 30 km), as initial and lateral boundary conditions.</li> <li>A third sensitivity test obtained by setting the horizontal grid spacing to 2 km, which allows explicit representation of deep convection.</li> </ul> <p>The model output is stored every 3 h until 00 UTC 20 September 2020 and interpolated onto the same regular 0.1°×0.1° horizontal grid and pressure levels. The data files are formatted in Network Common Data Form (NetCDF) and named <strong>runs_ILBC_DDHH_RES.nc</strong> where</p> <ul> <li><strong>ILBC</strong> describes the initial and lateral boundary conditions (IFS or ERA5) </li> <li><strong>DDHH</strong> describes the initial day and hour (1500 or 1412) </li> <li><strong>RES</strong> describes the horizontal grid spacing (10 or 2 km)</li> <li>simulated infrared brightness temperatures are provided in extra files with <strong>RTTOV</strong> suffix for five of the models and variants</li> </ul>
Evaluating mesoscale model predictions of diurnal speedup events in the Altamont Pass Wind Resource Area of California
<p>This dataset contains input files for the Weather Research and Forecasting (WRF) model related to the manuscript "Evaluating mesoscale model predictions of diurnal speedup events in the Altamont Pass Wind Resource Area of California," to be submitted to the <em>Journal of Applied Meteorology and Climatology</em> by Arthur, et al. Included are:</p> <ul> <li><strong>namelist.wps</strong>: used by the WRF preprocessing system (WPS) to configure the model domain and initial/boundary conditions</li> <li><strong>Vtable.HRRR</strong>: used by WPS to process data from the High-Resolution Rapid Refresh (HRRR) model for WRF initial/boundary conditions</li> <li><strong>namelist.input.mynn</strong>: used to run the MYNN PBL simulation</li> <li><strong>namelist.input.3dpbl</strong>: used to run the 3D PBL simulation</li> <li><strong>windturbines.txt</strong>: used to define the location and type of wind turbines included in the simulations</li> <li><strong>wind-turbine-*.tbl</strong>: used to define the parameters of each turbine type (see Table 1 in Arthur et al.) <ul> <li><strong>1</strong>: NREL 1.7MW, H=80m, D=103m</li> <li><strong>2</strong>: NREL 2.3MW, H=80m, D=107m</li> <li><strong>3</strong>: NREL 2.3MW, H=80m, D=116m</li> <li><strong>4</strong>: Vestas V47 0.66MW, H=60m, D=47m</li> <li><strong>5</strong>: Bonus B54 1.0MW, H=55m, D=54m</li> </ul> </li> </ul> <p>This work was prepared by LLNL under Contract DE-AC52-07NA27344.</p>
The Mixed Layer Depth in the Ocean Model Intercomparison Project (OMIP): Impact of Resolving Mesoscale Eddies: supporting data
<p>This file contains a jupyter notebook (python language) used to produce the figures of a manuscript submitted to the journal Geoscientific Model Development, and the data necessary to reproduce the figures.</p> <p>Abstract of the manuscript:</p> <p>The ocean mixed layer is the interface between the ocean interior and the atmosphere or sea ice, and plays a key role in climate variability. It is thus critical that numerical models used in climate studies are capable of a good representation of the mixed layer, especially its depth. Here we evaluate the mixed layer depth (MLD) in six pairs of non-eddying (1° resolution) and eddy-rich (up to 1/16°) models from the Ocean Model Intercomparison Project (OMIP), forced by a common atmospheric state. For model validation, we use an updated MLD dataset computed from observations using the OMIP protocol (a constant density threshold). In winter, low resolution models exhibit large biases in the deep water formation regions. These biases are reduced in eddy-rich models but not uniformly across models and regions. The improvement is most noticeable in the mode water formation regions of the northern hemisphere. Results in the Southern Ocean are more contrasted, with biases of either sign remaining at high resolution. In eddy-rich models, mesoscale eddies control the spatial variability of MLD in winter. Contrary to a hypothesis that the deepening of the mixed layer in anticyclones would make the MLD larger globally, eddy-rich models tend to have a shallower mixed layer at most latitudes than coarser models do. In addition, our study highlights the sensitivity of the MLD computation to the choice of a reference level and the spatio-temporal sampling, which motivates new recommendations for MLD computation in future model intercomparison projects.</p>
Transition of the mesoscale eddy in the Kuroshio Extension re-circulation gyre in 2019
<p>Transition of the mesoscale cyclonic eddy occurred in the Kuroshio Extension re-circulation gyre (KERG) from June to December 2019. Color shades denote sea surface height (SSH) map (color contours, m). The Kuroshio and Kuroshio Extension (KE) are shown by a sharp southward increase in SSH and the recirculation gyre is illustrated by a high-SSH (>1.6 m) region. The SSH map was generated using E.U. Copernicus Marine Service Information.</p>
Impact of Convection-permitting and Model Resolution on the Simulation of Mesoscale Convective System Properties over East Asia: companion dataset
<p>This folder includes the intermediate data for the following manuscript:</p><p>Ding et al., Impact of Convection-permitting and Model Resolution on the Simulation of Mesoscale Convective System Properties over East Asia</p><p>The simulations were done using ICON-NWP (ICON Numerical Weather Prediction) model, version 2.6.1, over Asian monsoon region (62E–150E, 5.5N–54.5N) for 2020 summer. At the moment, we upload the intermediate data for MCS tracking. For more data, please contact the authors.</p>
Bridging length scales in organic mixed ionic-electronic conductors through internal strain and mesoscale dynamics
Open the record for dataset details and reuse information.
Data from: Depletion of lamins B1 and B2 promotes chromatin mobility and induces differential gene expression by a mesoscale-motion dependent mechanism
Open the record for dataset details and reuse information.
Mesoscale variation in fish populations in two small Appalachian streams (fish population data) at the Coweeta Hydrologic Laboratory in 1996
We examined the reach-scale distributions of three fish species to determine which biotic and abiotic factors are influential in the fishes distributions.
Mesoscale variation in fish populations in two small Appalachian streams (light data) at the Coweeta Hydrologic Laboratory in 1996
We examined the reach-scale distribution of macroinvertebrates to determine if photosynthetically active radiation (PAR) is influential in the distribution of the macroinvertebrates and macroinvertebrate functional feeding groups.
The simulated dataset associated with the paper "Mesoscale modelling of optical turbulence in the atmosphere: The need for ultrahigh vertical grid resolution"
<p>The WRF model-generated meteorological profiles are available in netcdf format. More information will be provided shortly. </p>
Data for the manuscript 'Biological response to the interaction of a mesoscale eddy and the river plume in the northern South China Sea'
<p>Satellite data includes daily sea-level anomaly from AVISO, daily surface chlorophyll a data from OC-CCI and sea surface salinity obtained from SMAP. Model simulated daily current, temperature, salinity, chlorophyll and nitrate concentration for three experiments (control, case1 and case2) from our coupled physical-biological model of the South China Sea . For chlorophyll and nitrate concentration, the dataset also includes the 16-day averaged budget terms along section S1. The modeled lagrangian particl tracking data is also avaiable. And all data and matlab scripts used to draw Fig. 1 to Fig. 10 are in Figure_data.rar file.</p>
Data and Scripts for "Bridging the gap to mesoscale radiation materials science with transient grating spectroscopy"
<p>This release contains all the files required to reproduce the data, figures, and tables in this paper's initial submission as a manuscript. All input files, output files, LAMMPS scripts and atomic configurations, MATLAB data processing scripts, raw plot databases, and plotting scripts can be found here.</p>
Flash propagation and inferred charge structure relative to radar-observed ice alignment signatures in a small Florida Mesoscale Convective System
<p>Data for paper of above title, submitted to <em>Geophysical Research Letters</em>, June 2017. Manuscript number: 2017GL072767</p>
Dataset for "Mesoscale Eddy-Induced Sharpening of Oceanic Tracer Front"
<p>Data of the tracer experiments and the associated diagnostics in the shallow water model for the ocean front study.</p><ul><li><strong>eforc.tar.gz</strong>: diagnosed eddy forcing fields for different tracers;</li><li><strong>exps_trs.tar.gz</strong>: solutions in offline tracer experiments on the coarse grid;</li><li><strong>forc_uvh.tar.gz</strong>: mass fluxes and layer thicknesses used to advect tracers;</li><li><strong>params.tar.gz</strong>: parameters used for tracer experiments</li></ul><p>Please contact Yueyang Lu via <strong>yueyang.lu@miami.edu</strong> if there are any questions.</p>
The Heat and Carbon Characteristics of Modelled Mesoscale Eddies in the South Atlantic Ocean
<p>Datasets in this repository are generated from BIOPERIANT12-CNCLNG01 model and are part of the manuscript entitled: "The Heat and Carbon Characteristics of Modelled Mesoscale Eddies in the South Atlantic Ocean". </p>
Data for the paper: The impact of Mediterranean mesoscale eddies on precipitations on land
<p>This dataset contains some raw model output, configuration files, and plotting scripts used to generate the results of the paper "The impact of Mediterranean mesoscale eddies on precipitations on land"</p>
Evaluation of Mesoscale Convective Systems in High Resolution E3SMv2
<p>This is the open data resource for the paper "Mesoscale Convective Systems Represented in High Resolution E3SMv2 and Impact of New Cloud and Convection Parameterizations" that submitted to the Journal of Geophysical Research: Atmospheres. </p>
Presentation in 46th ARO: Mesoscale Selective Plane-Illumination Microscopy for Thickness Map of Sub-Layers of the Human Tympanic Membrane
<p>This upload is for the poster presentation of Merlin Schär at the 46th Annual Midwinter Conference of the Association for Research in Otolaryngology on the development and application of mesoscale selective plane-illumination microscopy for imaging of the sublayers of the human tympanic membrane. The upload contains the poster, the related imaging data with corresponding metadata for scanning and image processing, as well as supplementary figures illustrating the stitching of subvolume scans and the merged 3D rendering.</p>
Global Classification Dataset of Daytime and Nighttime Marine Low-cloud Mesoscale Morphology
<p>The global classification dataset of daytime and nighttime marine low-cloud mesoscale morphology with six cloud types (Solid stratus, Closed MCC, Open MCC, Disorganized MCC, Clustered Cu and Suppressed Cu). The spatial resolution is 1<sup>o</sup> × 1<sup>o </sup>and the temporal resolution is 5 minutes for the years 2018-2022. They were established based on a deep learning model ResNet-50. Trained on daytime radiance data from MODIS (Moderate Resolution Imaging Spectroradiometer) and daytime retrieved COT (Cloud Optical Thickness), this model achieved a high prediction accuracy and can be applied to nighttime cloud classification. For a detailed introduction to the model, please refer to our article.</p> <p> </p> <h2>Technical info</h2> <p><strong>Product information</strong></p> <ul> <li><strong>File ‘day_xxxx_all.h5’:</strong> Daytime classification of global marine low-cloud morphology for the year xxxx, with a spatial resolution of 1°×1° and a temporal resolution of 5 minutes <ul> <li>date: time of the 1°×1° box, format: 'YYYYDDD.HHHH'</li> <li>lon: central longitude (-180, 180)</li> <li>lat: central latitude (-60, 60)</li> <li>cat: category of the cloud morphology. The numbers 0-5 represent each of the six categories: 0-Solid stratus, 1-Closed MCC, 2-Open MCC, 3-Disorganized MCC, 4-Clustered Cu, 5-Suppressed Cu</li> <li>cert: model certainty, the probability that this cloud morphology belongs to the assigned category</li> <li>low_cf: the cloud fraction of low clouds</li> <li>COT_CNN: average cloud optical thickness (COT), retrieved using TIR-CNN model from Wang et al. (2022)</li> <li>CER_CNN: average cloud effective radius (CER), retrieved using TIR-CNN model from Wang et al. (2022), in the unit of μm</li> <li>LWP_CNN: average cloud liquid path (LWP), calculated from COT_CNN and CER_CNN, in the unit of g/㎡</li> <li>Sensor_zenith: scene average sensor zenith angle, from MODIS MYD021, in the unit of degree (°)</li> </ul> </li> </ul> <div> <ul> <li><strong>File 'night_xxxx_all.h5':</strong> Nighttime classification of global marine low-cloud morphology for the year xxxx, with a spatial resolution of 1°×1° and a temporal resolution of 5 minutes <ul> <li>same variables as daytime</li> </ul> </li> </ul> <ul> <li><strong>File 'example.xlsx':</strong> A sample of the variable data from our cloud classification dataset, showcasing the classification results of a MODIS granule captured on January 1, 2018, at 00:25 UTC. This sample is provided to help users better understand the content of our dataset.</li> </ul> <p> </p> <p><strong>Training, Validation, Test dataset</strong></p> <ul> <li>Originating from the same classification dataset, same variables, only differ in sample size</li> <li><strong>Files 'training_dataset.h5', 'validation_dataset.h5', and 'test_dataset.h5'</strong><strong>:</strong> <ul> <li>date: time of the 128×128 pixels, format: 'YYYYDDD.HHHH'</li> <li>lat: central latitude of the 128×128 scene</li> <li>lon: central longitude of the 128×128 scene</li> <li>cat: category of the cloud morphology. The numbers 0-5 represent each of the six categories: 0-Solid stratus, 1-Closed MCC, 2-Open MCC, 3-Disorganized MCC, 4-Clustered Cu, 5-Suppressed Cu</li> <li>CTH: cloud top height, in-cloud average value, in the unit of km</li> <li>COT_retrieved: cloud optical thickness (COT), retrieved using TIR-CNN model from Wang et al. (2022), 128×128 pixels</li> <li>LWP: cloud liquid path (LWP) from MODIS MYD06, in-cloud average value, in the unit of g/㎡</li> <li>Sensor_zenith: scene average sensor zenith angle, from MODIS MYD021, in the unit of degree (°)</li> <li>emis_29: radiance data from thermal infrared channel 29 (8.7μm), 128×128 pixels</li> <li>emis_31: radiance data from thermal infrared channel 31 (10.8 μm), 128×128 pixels</li> <li>emis_32: radiance data from thermal infrared channel 32 (12.0 μm), 128×128 pixels</li> <li>i: the row number of the top-left pixel of 128 ×128 scene in the MODIS granule</li> <li>j: the column number of the top-left pixel of 128 ×128 scene in the MODIS granule</li> </ul> </li> </ul> </div>
Data for preprint: "Non-Telecentric 2P microscopy for 3D random access mesoscale imaging "
<p>Numerical data used in latest version of preprint: "Non-Telecentric 2P microscopy for 3D random access mesoscale imaging ", https://www.researchsquare.com/article/rs-121292/v1</p>
ScienceDex guides
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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.