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131 results for “mesoscale”
Summary wind statistics from NEWA WRF mesoscale ensemble
<p>These files contain summary statistics from the NEWA WRF mesoscale ensemble for a Northern European domain.</p> <p>Each netCDF file includes:<br> - Mean wind speed (50, 75, 100 and 150 m AGL)<br> - Surface mean air density<br> - Surface static fields (latitude, longitude, surface elevation and surface roughness)<br> - Wind speed and direction frequency distribution (100 m AGL)</p> <p>The WRF simulation setup and parameterizations correspond to those in the README.ensemble file.</p>
Data from: Mesoscale activity facilitates energy gain in a top predator
How animal movement decisions interact with the distribution of resources to shape individual performance is a key question in ecology. However, links between spatial and behavioural ecology and fitness consequences are poorly understood because the outcomes of individual resource selection decisions, such as energy intake, are rarely measured. In the open ocean, mesoscale features (~10-100 km) such as fronts and eddies can aggregate prey and thereby drive the distribution of foraging vertebrates through bottom-up biophysical coupling. These productive features are known to attract predators, yet their role in facilitating energy transfer to top predators is opaque. We investigated the use of mesoscale features by migrating northern elephant seals and quantified the corresponding energetic gains from the seals' foraging patterns at a daily resolution. Migrating elephant seals modified their diving behaviour and selected for mesoscale features when foraging. Daily energy gain increased significantly with increasing mesoscale activity, indicating that the physical environment can influence predator fitness at fine temporal scales. Results show that areas of high mesoscale activity not only attract top predators as foraging hotspots, but also lead to increased energy transfer across trophic levels. Our study provides evidence that the physical environment is an important factor in controlling energy flow to top predators by setting the stage for variation in resource availability. Such understanding is critical for assessing how changes in the environment and resource distribution will affect individual fitness and food web dynamics.
Experimental data for the publication: "Probing the early stages of shock- induced chondritic meteorite formation at the mesoscale"
<p>In accordance with the expectations outlined in <em><strong>Clarifications of EPSRC expectations on research data management </strong></em>(09/10/14) this data has been made publicly available to complement the open access publication "Probing the early staged of chondritic meteorite formation at the mesoscale using single-bunch X-ray phase-contrast radiography at ESRF". </p> <p>There are three data sets. The folder entitled 'DarkFrames' contains the fitted median dark frame radiographs recorded on frames 1 and 2 of the camera. The folder entitled 'Flatfields' contains the mean flatfield radiographs recorded on frames 1 and 2 of the camera. The folder entitled 'Shots' contains the radiographs recorded on static and shock-compressed samples on frames 1 and 2 of the camera. </p>
Glider and satellite high resolution monitoring of a mesoscale eddy in the algerian basin: Effects on the mixed layer depth and biochemistry
<p>Despite an extensive bibliography for the circulation of the Mediterranean Sea and its sub-basins, the debate on mesoscale dynamics and their impacts on bio-chemical processes is still open because of their intrinsic time scales and of the difficulties in their sampling. In order to clarify some of these processes, the “Algerian BAsin Circulation Unmanned Survey-ABACUS” project was proposed and realized through access to the JERICO Trans National Access (TNA) infrastructure between September and December 2014. In this framework, a deep glider cruise was carried out in the area between the Balearic Islands and the Algerian coast to establish a repeat line for monitoring of the basin circulation. During the mission a mesoscale eddy, identified on satellite altimetry maps, was sampled at high-spatial horizontal resolution (4 km) along its main axes and from the surface to 1000 m depth. Data were collected by a Slocum glider equipped with a pumped CTD and biochemical sensors that collected about 100 complete casts inside the eddy. In order to describe the structure of the eddy, in situ data were merged with next generation remotely sensed data: daily synoptic sea surface temperature (SST) and chlorophyll concentration (Chl-a) images from the MODIS satellites, as well as sea surface height and geostrophic velocities from AVISO. From its origin along the Algerian coast in the eastern part of the basin, the eddy propagated northwest at a mean speed of about 4 km/day, with a mean diameter of 112–130 km, mean amplitude of 15.7 cm; the eddy was clearly distinguished from the surrounding waters thanks to its higher SST and Chl-a values. Temperature and salinity values over the water column confirm the origin of the eddy from the Algerian Current (AC) showing the presence of recent Atlantic water in the surface layer and Levantine Intermediate Water (LIW) in the deeper layer. The eddy footprint is clearly evident in the multiparametric vertical sections conducted along its main axis.</p> <p>Deepening of temperature, salinity and density isolines at the center of the eddy is associated with variations in Chl-a, oxygen concentration and turbidity patterns. In particular, at 50 m depth along the eddy borders, Chl-a values are higher (1.1–5.2 μg/l) in comparison with the eddy center (0.5–0.7 μg/l) with maximum values found in the southeastern sector of the eddy.</p> <p>Calculation of geostrophic velocities along transects and vertical quasi-geostrophic velocities (QG-w) over a regular 5 km grid from the glider data helped to describe the mechanisms and functioning of the eddy. QG-w presents an asymmetric pattern, with relatively strong downwelling in the western part of the eddy and upwelling in the southeastern part. This asymmetry in the vertical velocity pattern, which brings LIW into the euphotic layer as well as advection from the northeastern sector of the eddy, may explain the observed increases in Chl-a values</p>
CFD Modeling Results and Related Data and Codes for Plotting of "A Mesoscale-to-LES Modeling of Tornado-like Vortex and Associated Local Strong Winds in Urban Area"
<p>The CFD modeling outputs, derived maximum wind fields in the analysis area, the topography data, the Python codes used to produce the figures, as we as the namelist of WRF simulation are available. The CFD modeling outputs are in binary format. The ctl. files of corresponding binary data (or dataset if ordered chronologically) are available in each directory (named after each experiment in our study).</p>
Evaluation of Precipitation Forecast by the Operational China Meteorological Administration Mesoscale Model during the 2020 Meiyu Period
Open the record for dataset details and reuse information.
Supporting data for "Shallow convective heating in weak temperature gradient balance explains mesoscale vertical motions in the trades" (previously for ch. 5 of "Mesoscale Cloud Patterns in the Trade-Wind Boundary Layer")
<p>This contains both the data and scripts required to produce the figures in the preprint "Shallow convective heating in weak temperature gradient balance explains mesoscale vertical motions in the trades". The scripts labeled 1-5 produce the main figures; the other scripts produce supporting data or figures.</p> <p>Earlier versions of this dataset contained the scripts and data supporting Ch. 5 of the PhD thesis "Mesoscale Cloud Patterns in the Trade-Wind Boundary Layer". The scripts labeled 1-5 produce the main figures; the other scripts produce either the underlying data, or supporting figures (prefix S). </p>
Sub-Mesoscale Ocean Dynamics Experiment: Surface currents and Sea Surface Temperature
<p>Airborne observations of surface currents from Doppler Scatterometer and sea surface temperature from infrared camera.</p> <p><span>This data set is derived from the S-MODE project <span> </span><span><a title="https://urldefense.us/v3/__https:/github.com/podaac/2022-SMODE-Open-Data-Workshop__;!!PvBDto6Hs4WbVuu7!KGw6WDLV5oVZZv5MSJfZzLeZUUOOanKvBtRTARLrqCGRTLJl1FNP0nby2uQNCVA7GcoulOIL0eeT0ifzTu9iHii22cisZMe8$" href="https://urldefense.us/v3/__https:/github.com/podaac/2022-SMODE-Open-Data-Workshop__;!!PvBDto6Hs4WbVuu7!KGw6WDLV5oVZZv5MSJfZzLeZUUOOanKvBtRTARLrqCGRTLJl1FNP0nby2uQNCVA7GcoulOIL0eeT0ifzTu9iHii22cisZMe8$">https://github.com/podaac/2022-SMODE-Open-Data-Workshop</a></span>.<br></span></p> <p> </p> <p>Description of the data:</p> <p>DopplerScatt surface current spatial resolution of 200 m</p> <p>MOSES SST nominal spatial resolution of 10 m, but interpolated to DopplerScatt grid at 200 m resolution.</p> <p>Description of the name: SMODE_DScatt_and_MOSES_20221023_120546_ver_L2E_Smoothing_1km_Submesoscale_Frontogenesis.nc</p> <p>Date = 20221023_120546 (YYYY/mm/dd HH:MM:SS)</p> <p>Level of smoothing = 1 kilometer</p> <p>Submesoscale_Frontogenesis = The variables included in the file are necessary to compute frontogenesis</p> <p> </p> <p>Variables:</p> <p>SST_moses = sea surface temperature</p> <p>U_hp = high-pass east-west velocity component</p> <p>V_hp = high-pass north-south velocity component</p> <p>Tx = SST gradients in east-west direction</p> <p>Ty = SST gradients in north-south direction</p> <p>U_hp_(rot,div) = high-pass east-west velocity component rotational and divergent component</p> <p>V_hp_(rot,div) = high-pass north-south velocity component rotational and divergent component</p> <p>Fs_rot = Frontogenetic tendency due to the rotational component of the velocity field</p> <p>Fs_div = Frontogenetic tendency due to the divergent component of the velocity field</p> <p>Fs_tot = Frontogenetic tendency</p> <p>Strain_rot = Strain deformation due to the rotational component of the velocity field smoothed at 1 km</p> <p>Strain_div = Strain deformation due to the divergent component of the velocity field smoothed at 1 km</p> <p>rv_lp = relative vorticity smoothed at 1 km</p> <p>dv_lp = divergence smoothed at 1 km</p> <p>Strain_lp = strain deformation smoothed at 1 km</p> <p>Strain_normal = normal strain deformation smoothed at 1 km</p> <p>Strain_shear = shear component of the strain deformation smoothed at 1 km</p> <p> </p> <p> </p>
Chen et al (2021) Mesoscale and Submesoscale Shelf-Ocean Exchanges Initialize an Advective Marine Heatwave
<p>data and software for Chen et al (2021) "Mesoscale and Submesoscale Shelf-Ocean Exchanges Initialize an Advective Marine Heatwave" in Journal of Geophysical Research: Oceans</p>
Mesoscale cortex-wide neural dynamics predict self-initiated actions in mice several seconds prior to movement
<p>Volition - the sense of control or agency over one's voluntary actions - is widely recognized as the basis of both human subjective experience and natural behavior in non-human animals. To date, several human studies have found peaks in neural activity preceding voluntary actions, e.g. the readiness potential (RP), and some have shown upcoming actions could be decoded even before awareness. While these findings may pose a challenge to traditional accounts of human volition, some have proposed that random processes underlie and explain pre-movement neural activity. Here we seek to address part of this controversy by evaluating whether pre-movement neural activity in mice contains structure beyond that present in random neural activity. Implementing a self-initiated water-rewarded lever pull paradigm in mice while recording widefield [Ca++] neural activity we find that cortical activity changes in variance seconds prior to movement and that upcoming lever pulls or spontaneous body movements could be predicted between 1 second to more than 10 seconds prior to movement, similar to but even earlier than in human studies. We show that mice, like humans, are biased towards initiation of voluntary actions during specific phases of neural activity oscillations but that the pre-movement neural code in mice changes over time and is widely distributed as behavior prediction improved when using all vs single cortical areas. These findings support the presence of structured multi-second neural dynamics preceding voluntary action beyond that expected from random processes. Our results also suggest that neural mechanisms underlying self-initiated voluntary action could be preserved between mice and humans.</p>
Cross-correlation coefficient maps generated in the paper "Correlation of Venusian Mesoscale Cloud Morphology Between Images Acquired at Various Wavelengths" by Narita et al. published in Journal of Geophysical Research - Planets
<p>This data archive contains the cross-correlation coefficient maps. Unzipping the compressed file, the following directories corresponding to different wavelength pairs appear. </p> <p> IR1_IR2/ : 0.9 micron & 2.02 micron<br> UVI283_UVI365/ : 283 nm & 365 nm<br> IR2_UVI365/ : 2.02 micron &. 365 nm<br> IR2_UVI283/ : 2.02 micron & 283 nm<br> IR2_LIR/ : 2.02 micron & 10 micron</p> <p>If usual unzip tools do not work, the use of 7zip is recommended:<br> https://www.7-zip.org/download.html</p> <p>Each directory contains CSV files for the longitude-latitude distribution of the correlation coefficient. The 2880 longitude grids cover the longitude range of 0 - 360 degrees, and the 1440 latitude grids cover the latitude range of -90 - +90 degrees, with a pixel resolution of 0.125 degree/pixel. Invalid regions are filled with the value of 1.1.</p> <p>Each filename is composed of the date, the instrument (wavelength), and the time. For example, for the file "20160720_ir2_150821_hp6_IR1_150209_hp6_sb24.csv":</p> <p> 20160720 : July 20, 2016<br> ir2 : 2.02 micron filter of IR2 camera<br> 150821 : IR2 exposure at 15:08:21<br> hp6: High-pass filtering size is 6 deg x 6 deg<br> IR1 : 0.9 micron filter of IR1 camera<br> 150209 : IR1 exposure at 15:02:09<br> sb24 : Sliding box size is 24 deg x 24 deg</p>
Script and data of "Role of Frictional Processes in Mesoscale Eddy Available Potential Energy Budget in the Global Ocean"
<p>% File description:</p> <p>1. Cal_conversions.m: a set of functions calculating the EAPE-EKE and EAPE-EKE conversion terms with CESM output data in B-grid</p> <p>2. smooth2a.m: function of boxcar filtering</p> <p>3. CONV_u100_2d.mat: data of the global distribution of upper 100 m averaged conversion terms used in Figure 2 of the manuscript<br> % Variables inside the file:<br> CONVa_H_u100: MAPE-EAPE conversion driven by frictional process<br> CONVo_H_u100: MAPE-EAPE conversion driven by non-frictional process<br> CONVa_V_u100: EAPE-EKE conversion driven by frictional process<br> CONVo_V_u100: EAPE-EKE conversion driven by non-frictional process</p> <p>4. CONV_profile.mat: data of the vertical profiles of global and regional averaged EAPE-EKE conversion terms used in Figure 3 of the manuscript<br> % Variables inside the file:<br> % Vertical profiles of quasi-global-averaged EAPE-EKE conversion <br> CONVa_V_GLO_profile: driven by frictional process<br> CONVo_V_GLO_profile: driven by non-frictional process<br> CONVttw_V_GLO_profile: reproduced by TTW balance <br> <br> % Vertical profiles of EAPE-EKE conversion averaged in western boundary current regions<br> CONVa_V_WBCE_profile: driven by frictional process<br> CONVo_V_WBCE_profile: driven by non-frictional process<br> CONVttw_V_WBCE_profile: reproduced by TTW balance </p> <p> % Vertical profiles of EAPE-EKE conversion averaged in subtropical gyres<br> CONVa_V_STG_profile: driven by frictional process<br> CONVo_V_STG_profile: driven by non-frictional process<br> CONVttw_V_STG_profile: reproduced by TTW balance <br> <br> % Vertical profiles of EAPE-EKE conversion averaged in subpolar gyres<br> CONVa_V_SPG_profile: driven by frictional process<br> CONVo_V_SPG_profile: driven by non-frictional process<br> CONVttw_V_SPG_profile: reproduced by TTW balance </p> <p> % Vertical profiles of EAPE-EKE conversion averaged in the Southern Ocean<br> CONVa_V_SO_profile: driven by frictional process<br> CONVo_V_SO_profile: driven by non-frictional process<br> CONVttw_V_SO_profile: reproduced by TTW balance </p> <p>5. CONV_SeasDiff.mat: data of the seasonal difference (winter minus summer) of global and regional averaged conversion terms used in Figure 3 of the manuscript<br> % Variables inside the file:<br> % Vertical profiles of the seasonal difference of quasi-global-averaged EAPE-EKE conversion <br> CONVa_V_GLO_SeasDiff: driven by frictional process<br> CONVo_V_GLO_SeasDiff: driven by non-frictional process<br> CONVttw_V_GLO_SeasDiff: reproduced by TTW balance <br> <br> % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in western boundary current regions<br> CONVa_V_WBCE_SeasDiff: driven by frictional process<br> CONVo_V_WBCE_SeasDiff: driven by non-frictional process<br> CONVttw_V_WBCE_SeasDiff: reproduced by TTW balance </p> <p> % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in subtropical gyres<br> CONVa_V_STG_SeasDiff: driven by frictional process<br> CONVo_V_STG_SeasDiff: driven by non-frictional process<br> CONVttw_V_STG_SeasDiff: reproduced by TTW balance <br> <br> % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in subpolar gyres<br> CONVa_V_SPG_SeasDiff: driven by frictional process<br> CONVo_V_SPG_SeasDiff: driven by non-frictional process<br> CONVttw_V_SPG_SeasDiff: reproduced by TTW balance </p> <p> % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in the Southern Ocean<br> CONVa_V_SO_SeasDiff: driven by frictional process<br> CONVo_V_SO_SeasDiff: driven by non-frictional process<br> CONVttw_V_SO_SeasDiff: reproduced by TTW balance </p> <p>6. Coord_lon_lat_zw.mat: coordinate information for the variables in "CONV_u100_2d.mat", "CONV_profile.mat"and "CONV_SeasDiff.mat"<br> % Variables inside the file:<br> lon: longitude for the global distributions of the conversion terms<br> lat: latitude for the global distributions of the conversion terms<br> z_w: depth of each vertical level for vertical profiles of conversion terms</p>
Data Supporting "Mesoscale Convective Clustering Enhances Tropical Precipitation"
<p>These data are in support of "Mesoscale Convective Clustering Enhances Tropical Precipitation" by P. Angulo-Umana and D. Kim. </p> <p>In the tropics, extreme precipitation events are often caused by mesoscale systems of organized, spatially clustered deep cumulonimbi, posing a substantial risk to life and property. While the clustering of convective clouds has been thought to strengthen precipitation intensity, no quantitative estimates of this hypothesized enhancement exist. In this study, after isolating the effects of mesoscale convective clustering on precipitation, we find that strongly clustered convection precipitates more intensely than weakly clustered convection. We further show that this enhancement is primarily attributable to an increase in convective precipitation intensity when the environment is less than 70% saturated, with increases in stratiform cloud cover being of equal or greater importance when the environment is closer to saturation. Our results suggest that a correct representation of mesoscale organized convective systems in numerical weather and climate models is needed for accurate predictions of extreme precipitation events.</p>
Tracked IMERG Mesoscale Precipitation Systems (TIMPS)
<p>TIMPS is a dataset of tracked precipitation systems in the tropics derived from NASA's Integrated Multi-satellitE Retrievals for GPM (IMERG) v06B precipitation fields. TIMPS uses the Forward in Time (FiT)Tracking Algorithm and is optimized to track precipitation systems representative of Mesoscale Convective Systems (MCSs). The TIMPS domain is 30N-30S and systems are tracked across 10 years (2011-2020). </p>
Additional Supporting Information to 'Quantifying the Contribution of Ocean Mesoscale Eddies to Low Oxygen Extreme Events.'
<p>Additional Supporting Information to 'Quantifying the Contribution of Ocean Mesoscale Eddies to Low Oxygen Extreme Events.'. Submitted to Geophysical Research Letters for publication. 2022.</p> <p>NetCDF files and Python NumPy arrays of data used to create all figures in the manuscript main text and supporting information.</p>
Data for figures in the manuscript "Refraction of the M2 internal tides induced by mesoscale eddies in the South China Sea"
<p>Data for figures in the manuscript "Refraction of the M2 internal tides induced by mesoscale eddies in the South China Sea"</p>
Modulation of Cyclones with Tropical and Extratropical Origins by Mesoscale SSTs in the Kuroshio Extension Region
<p>Datasets for <strong>Modulation of Cyclones with T</strong><strong>ropical and Extratropical </strong><strong>Origins </strong><strong>by Mesoscale SSTs in the Kuroshio Extension Region.</strong></p>
Data for: A persistent submesoscale frontal slick: A novel marker of the mesoscale flow field in a large lake (Lake Geneva)
<p><strong>This study provides the first evidence of the evolution of a frontal slick in Lake Geneva, a large, deep lake in western Europe. Our results rely on field measurement, remote sensing data, and three-dimensional numerical modeling. The data presented here comprise:</strong></p> <p><strong>(1) measurements from an autonomous catamaran, including GNSS data, near-surface temperature profiles, weather station, near-surface Acoustic Doppler Current Profilers (ADCP), and RGB images from two onboard cameras,</strong></p> <p><strong>(2) infrared images from a FLIR camera attached to a helium-filled balloon, </strong></p> <p><strong>(3) time-lapse images from a shore-based imaging package provided slanted (non-perpendicular) field of views of the lake surface water,</strong></p> <p><strong>(4) Excitation Emission Matrix (EEM) using a Fluorescence Spectrometer for the Fluorescent Dissolved Organic Matter (FDOM) inside slick/non-slick samples, and</strong></p> <p><strong>(5) results of a 3D numerical simulation provided details of the lake hydrodynamics during the field measurement campaign.</strong></p> <p><strong>The three-dimensional model used in this study is based on the MIT General Circulation Model (MITgcm,<a href="http://mitgcm.org/"> http://mitgcm.org/</a>,<a href="https://doi.org/10.1029/96JC02775"> https://doi.org/10.1029/96JC02775</a>).</strong></p>
Baroclinic instability induced mesoscale and submesoscale processes in the river plumes: A laboratory investigation on a rotating tank
<p>This dataset studied the baroclinic instability (BI) induced mesoscale and submesoscale processes by conducting laboratory rotating flume experiments. We acquired the high-resolution velocity data by Parrticle Image Velocity (PIV). The mesoscale and submesoscale vortices were identified and tracked, and the variations of the instabilities and kinetic energy of the plume system under different inflow conditions and slopes were summarized. Number '1' to '33' mean cases number; 'gs', 'ss', and 'ns' stand for 'gentle slope', 'steep slope', and 'no slope'; 'T20' to 'T60' correspond to the rotation period from 20 to 60 s; 'g4' to 'g10' correspond to the reduced gravity between the buoyant plume and environmental fluid from 4 to 10 cm/s^2. 'vor_char' means the vortices characteristics including the time series of the vortex center and vortex contour.</p>
Summer mesoscale convective systems in convection-permitting simulation using WRF over East China
<p>Mesoscale convective systems (MCSs) are active in East China during the summer, causing significant precipitation and extreme weather. Increasing MCS frequency and intensity highlight the need for better simulation and forecasting. Traditional global and regional models with coarse resolution unable to explicitly resolve convection fail to represent MCSs and their precipitation accurately. This study conducted a 22-year (2000--2021) JJA simulation at a convection-permitting resolution (4km) using the WRF model (WRF-CPM) over East China. The data generated and the code used to analyze are uploaded here.</p>
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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.