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Eddy covariance (EC) vertical carbon fluxes from a Georgia tidal salt marsh from 2014 to 2024
We present our methodology and data for science ready vertical carbon fluxes from a Spartina alterniflora tidal salt marsh as part of the Georgia Coastal Ecosystems Long Term Ecological Research (GCE-LTER) site on Sapelo Island, Georgia, USA. Vertical carbon fluxes were measured through the eddy covariance (EC) method from 2014 to 2024. The EC flux tower was located on the western side of Sapelo Island bounded by the Duplin River and Barn Creek. The proportional influence of marsh habitats on the flux measurements were 4% tall, 38% short, and 58% medium height form Spartina alterniflora. We present the net ecosystem exchange (NEE), ecosystem respiration (ER), and gross primary production (GPP) at 30-minute fluxes (μmol CO2 m-2 s-1), daily averages (μmol CO2 m-2 s-1) and totals (g C m-2 day-1), and annual (g C m-2 year-1) quantities. We provide estimated uncertainty for each flux at each integrated timescale as 95% confidence intervals. Providing open access to 10-year carbon flux datasets can facilitate collaboration for advancing regional and global blue carbon synthesis and scale-up studies.
Comparing vertical accretion, organic carbon (C) sequestration, and nitrogen burial between a natural, never diked tidal salt marsh and a hydrologically restored tidal salt marsh on Sapelo Island, Georgia.
Restoration of tidal marshes throughout the 20th century have attempted to bring back important functions of natural tidal systems. In this study, vertical accretion, organic carbon (C) sequestration, and nitrogen burial were compared between a natural, never diked tidal salt marsh and a hydrologically restored tidal salt marsh on Sapelo Island, Georgia to examine the impacts of restoration years later. On Sapelo Island there are two marshes near the University of Georgia Marine Institute, one of which is a natural marsh, and one of which is a restored marsh. The restored marsh had been diked in 1948, and the dike was breached, allowing for the marsh to be restored, in 1956. Soil cores were collected from both marshes, and the sediments were analysed for Nitrogen and Carbon concentrations and bulk density. This analysis was used to determine accretion rates for the two marshes as well as changes in the restored marsh since the dike was breached. Nitrogen burial, carbon sequestration, and soil accretion in the restored marsh as compared to the natural marsh were the focus of this study.
Dataset to Manuscript: Vertical mobility of pyrogenic organic matter in soils: A column experiment, Marcus Schiedung et al. (Biogeosciences)
<p>Dataset to manuscript: Schiedung, M., Bellè, S.-L., Sigmund, G., Kalbitz, K., and Abiven, S.: Vertical mobility of pyrogenic organic matter in soils: A column experiment, Biogeosciences, https://doi.org/10.5194/bg-17-6457-2020, 2020.</p> <p>All parameters and variables are described in "Var_names" files.</p>
A vertically-resolved atmospheric dust reanalysis for Mars Years 28-29 using Analysis Correction
<p>This is a dataset of meteorological variables for the atmosphere of Mars, obtained by assimilating measurements (retrievals) of atmospheric temperature and dust opacity into a 3-dimensional, time-dependent numerical model of the Martian atmospheric circulation (known as a “reanalysis”).</p> <p>The observations come from two spacecraft - the Mars Climate Sounder (MCS) instrument on board NASA’s Mars Reconnaissance Orbiter (e.g. Kleinboehl et al. 2009) and the Thermal Emission Imaging Spectrometer (THEMIS) on board NASA’s Mars Odyssey spacecraft, and cover the period from 21 September 2006 until 5 November 2009 (Mars Years 28:Ls=109.98 - 30:Ls=4.78). MCS observations include profiles of temperature and dust opacity from near the surface up to altitudes of around 80 km obtained from infrared limb-sounding (MCS version 3 retrievals, based on opacities at around 21.6 micron wavelengths), while THEMIS measurements are of column dust opacity in the infrared (centred around 9.3 micron wavelength). Further details can be found on the websites</p> <p>https://pds-geosciences.wustl.edu/missions/odyssey/themis.html,<br> https://atmos.nmsu.edu/data and services/atmospheres data/MARS/aerosols.html</p> <p>The model into which the observations are assimilated is the UK version of Laboratoire de Météorologie Dynamique Mars Global Circulation Model (LMDMGCM), a 3-dimensional, time-dependent numerical circulation model of the Martian atmosphere and near-surface environment, simulating the changing winds, temperature, pressure and dust content of the atmosphere across the whole planet. The model solves the equations of motion, mass and energy conservation using a spherical harmonic representation in the horizontal and finite difference formulation in the vertical direction, but outputs the data here on a regular longitude-latitude grid with 72 points in longitude, 36 points in latitude and 25 terrain-following sigma levels in the vertical direction (where sigma = pressure/surface pressure) on a stretched vertical grid that extends from the surface to an altitude of approximately 100 km. More details can be found in publications by Forget et al. (1999), Newman et al. (2001), Mulholland et al. (2013).</p> <p>The observations and model are linked by an assimilation scheme, based on the Analysis Correction (AC) algorithm developed by Lorenc et al. (1991) and adapted for Mars by Lewis et al. (2007). Previous reanalyses of Mars observations using this scheme include the MACDA dataset (Montabone et al. 2014) and OPENMars (Holmes et al. 2020). This new dataset, however, makes use of an extension of the AC scheme to enable assimilation of both column integrated dust opacity measurements and dust opacity profiles in the vertical direction (see Ruan et al. 2021). This new dataset therefore provides a more realistic representation of the distribution of dust loading in the Martian atmosphere than previous work, which may also result in improved representation of other meteorological variables, notably temperature.</p> <p>Data are provided as 2D and 3D fields of variables in netCDF format as generated by the numerical model on the (longitude, latitude, sigma) grid at 2-hourly intervals. Each file contains 360 time steps covering 30 Martian days or sols. The variables contained in each file are as follows:</p> <p> Variables and attributes<br> 0 lon: FLOAT(72) = FLOAT(lon)<br> 0 long_name: longitude<br> 1 units: degrees_east<br> 1 lat: FLOAT(36) = FLOAT(lat)<br> 0 long_name: latitude<br> 1 units: degrees_north<br> 2 sigma: FLOAT(25) = FLOAT(sigma)<br> 0 long_name: sigma<br> 1 units: sigma_level = p/ps<br> 3 soil: FLOAT(18) = FLOAT(soil)<br> 0 long_name: soil levels (i.e. levels below the surface to represent thermal variations)<br> 1 units: none<br> 4 time: FLOAT(360) = FLOAT(time)<br> 0 long_name: model time<br> 1 units: days since 00:00:00 (the beginning of the file)<br> 5 controle: FLOAT(100) = FLOAT(lentable)<br> 0 long_name: Table of run parameters<br> 1 description: MGCM run 5.000<br> 6 Ls: FLOAT(360) = FLOAT(time)<br> 0 Physics_diagnostic: Solar longitude (such that Ls=0 is northern Spring equinox)<br> 1 units: deg<br> 7 tsurf: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: Surface temperature<br> 1 units: K<br> 8 ps: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: surface pressure<br> 1 units: Pa<br> 9 co2ice: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: co2 ice thickness (column mass density)<br> 1 units: kg.m-2<br> 10 fluxsurf_lw: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: fluxsurf_lw (surface infrared radiative flux)<br> 1 units: W.m-2<br> 11 fluxsurf_sw: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: fluxsurf_sw (surface visible radiative flux)<br> 1 units: W.m-2<br> 12 temp: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: temperature<br> 1 units: K<br> 13 u: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: Zonal (east-west) wind<br> 1 units: m.s-1<br> 14 v: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: Meridional (north-south) wind<br> 1 units: m.s-1<br> 15 rho: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: density<br> 1 units: kg.m-3<br> 16 udrag: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: Drag velocity<br> 1 units: m/s<br> 17 udragt: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: Threshold velocity for dust lifting<br> 1 units: m/s<br> 18 aerosol: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: dust opacity considering layer thickness<br> 1 units: SI (opacity/m)<br> 19 taudustvis: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: Dust optical depth<br> 1 units: SI<br> 20 q01: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: mix. ratio<br> 1 units: kg/kg<br> 21 dqsdevtot: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: dust devil lift rate<br> 1 units: kg.m-2.s-1<br> 22 dqsstrtot: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: near surface wind stress dust lifting rate<br> 1 units: kg.m-2.s-1<br> 23 dqssedtot: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: dust sedimentation rate<br> 1 units: kg.m-2.s-1</p> <p> </p>
The Jefferson Project 2017 water quality data from two vertical profiler stations in Lake George, NY, USA.
The Jefferson Project at Lake George – a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association – combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake’s food web and overall water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2017, The Jefferson Project deployed two vertical profiler stations on the lake, collecting data on water quality and meteorology. Meteorological data have been included with the Jefferson Project Weather Station dataset for 2017. These vertical profiler stations are named VP_AnthonysNose and VP_TeaIsland. The water quality data are collected by EXO2 Multi-parameter sonde sensors. The sensors collect data at 1 meter depth increments, starting at 1 meter and proceeding to 2 meters off bottom. The data is transferred in near real-time to an off-site database for monitoring and review. The data provided have undergone data correction by Jefferson Project researchers.
The Jefferson Project 2018 water quality data from two vertical profiler stations in Lake George, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2018, The Jefferson Project deployed two vertical profiler stations on the lake, collecting data on water quality and weather. Meteorological data have been included with the Jefferson Project Weather Station dataset for 2018. These vertical profiler stations are named VP_AnthonysNose and VP_TeaIsland. The water quality data are collected by YSI EXO2 Multi-parameter sonde sensors. The sensors collect data at 1 meter depth increments, starting at 1 meter and proceeding to 2 meters off bottom. The data is transferred in near real-time to an off-site database for monitoring and review. The data provided have undergone data correction by Jefferson Project researchers.
The Jefferson Project 2019 water quality data from three vertical profiler stations in Lake George, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2019, The Jefferson Project deployed three vertical profiler stations on the lake, collecting data on water quality and weather. Meteorological data have been included with the Jefferson Project Weather Station dataset for 2019. These vertical profiler stations are named VP_AnthonysNose, VP_CalvesPen, and VP_TeaIsland. The water quality data are collected by a YSI EXO2 Multi-parameter sonde sensors. The sensors collect data at 1 meter depth increments, starting at 1 meter and proceeding to 2 meters off bottom. The data are transferred in near real-time to an off-site database for monitoring and review. The data provided here have undergone data correction by Jefferson Project researchers.
Landscape Position Project at North Temperate Lakes LTER: Vertical Lake Profiles 1998 - 1999
Parameters characterizing the chemical limnology and spatial attributes of 45 lakes were surveyed as part of the Landscape Position Project. Parameters are measured at or close to the deepest part of the lake. A vertical profile of temperature, dissolved oxygen, and conductivity are collected at 1 meter increments Sampling Frequency: generally monthly for one summer; for some lakes, one or two samples in one summer Number of sites: 45
Vertical fluxes of particulate carbon, nitrogen and phosphorus from a sediment trap deployed west of Palmer Station, Antarctica at a depth of 170 meters, 1992-2019.
Particulate organic matter is exported from the upper ocean euphotic zone in the form of large sinking particles and as dissolved material. Particle fluxes to depth link the surface and mesopelagic realm and supply food to the benthos. Sedimentation flux is typically measured with sediment traps of various designs. Palmer LTER has deployed a time-series trap near 64.5degrees S, 66.0degrees W since late 1992. The trap is moored in 300 m depth and collects sinking particles at 150 m. Deployments and analyses were performed by David Karl, University of Hawaii until 2002 when Hugh Ducklow took over the sediment trap operations.Sedimentation at the PAL site of the West Antarctic Peninsula demonstrates extreme seasonality, with a well-defined pulse in the Austral summer following sea ice retreat. Daily sedimentation rates during the summer flux event are among the highest recorded globally. During the Austral winter when the ocean is covered by sea ice and shrouded in darkness, fluxes are among the lowest observed anywhere. Sedimentation rates at PAL typically vary by 4 orders of magnitude. There is also order of magnitude variability in the total annual flux (area under the curve).
Lateral and vertical forest retreat rate in the mid-Atlantic sea-level rise hotspot
Ghost forests consisting of dead trees adjacent to marshes are striking indicators of climate change. Here we quantify both the lateral and vertical rate of coastal forest retreat between 1984 and 2020 along the US mid-Atlantic coast. The study region includes areas between 0-5 m above sea level across the Chesapeake Bay and the adjacent Delaware Bay. Specifically, the data package includes 2 shapefile datasets derived from four decades of Landsat satellite observations of coastal treeline dynamics. The two datasets are generated on the same spatial-scale and have the same spatial resolution (0.075 km2), both stored as hexagon grids with a side length 170 m. Here we define "forest retreat" (as shown in the datasets as positive values) as the migration of coastal treeline landwards (lateral retreat) or upslope (vertical retreat), whereas "forest advance" (negative values) refers to treeline migration seawards (lateral advance) or downslope (vertical advance). The number '999999' in both datasets indicates areas of stable coastal treelines (i.e. no change) between 1984 and 2020.
Radar measurements for the article "Dynamic differential reflectivity calibration using vertical profiles in rain and snow"
<p><strong>Dataset documentation</strong></p> <p>The archives in hdf5 format provided at this link contain the datasets used in the manuscript <em>Dynamic differential reflectivity calibration using vertical profiles in rain and snow</em>, submitted to <em>Remote Sensing</em> (MDPI) by Alfonso Ferrone and Alexis Berne in 2020.</p> <p> </p> <p><strong>File content</strong></p> <p>Each file is structured as a table, with each column referring to a specific variable and each row containing a different realization (in space or time). The set of available variables is campaign dependent, and the possibilities are:</p> <ul> <li> <p><strong>idx</strong>, and integer index that starts at 1 for the first scan of the dataset and increases by 1 for every successive scan;</p> </li> <li> <p><strong>t</strong>, the timestamp of the scan, in seconds since seconds since Jan 01, 1970;</p> </li> <li> <p><strong>r</strong> or <strong>rg</strong> (depending on the file), the distance from the radar in meters;</p> </li> <li> <p><strong>az</strong>, the azimuth angle in degrees;</p> </li> <li> <p><strong>el</strong>, the elevation angle in degrees;</p> </li> <li> <p><strong>zdr</strong>, the uncalibrated differential reflectivity, in dB;</p> </li> <li> <p><strong>zh</strong>, the horizontal reflectivity, in dBZ;</p> </li> <li> <p><strong>rhovh</strong> or <strong>rho</strong>, the co-polar correlation coefficient, unitless;</p> </li> <li> <p><strong>snr_h</strong> or <strong>snr</strong>, the signal to noise ratio for the horizontal channel in dB;</p> </li> <li> <p><strong>snrv</strong>, the signal to noise ratio for the vertical channel, in dB,</p> </li> <li> <p><strong>ngates</strong>, the number of unique range gates.</p> </li> </ul> <p>For the comparison of the data collected by MXPol and DX50 during the PAYERNE campaign, two auxiliary variables were added to the archives:</p> <ul> <li> <p><strong>x</strong> the horizontal distance from the current radar, computed on a line passing through the location of two radars;</p> </li> <li> <p><strong>z</strong> the vertical distance from the current radar.</p> </li> </ul> <p> </p> <p><strong>Usage</strong></p> <p>The dataset are provided in the the Hierarchical Data Format version 5 (HDF5), an open source file format, supported by several programming language.</p> <p>They archives were created using the <em>vaex</em> library for Python 3:</p> <p>https://github.com/vaexio/vaex</p> <p>The function <em>vaex.open</em> from the same library can be used for accessing the archives and converting them to <em>vaex.DataFrame</em>.</p> <p> </p> <p><strong>Campaign-specific information</strong></p> <p>Some of the parameters associated to the variables included in the archives may change depending on the campaign. The following subsection provide a summary of these information.</p> <p> </p> <p><strong>dataframe_HYMEX_2013_from_20130907-040344_to_20131105-175944.hdf5</strong></p> <p>Contains PPI scans at 90° elevation for the HYMEX campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 44.61° N</p> </li> <li> <p>Longitude: 4.55° E</p> </li> <li> <p>Altitude: 604 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.41 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.45°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 75 m</p> </li> <li> <p>Range to the first gate: 204.3 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.6</p> </li> <li> <p>Attenuation correction: Z-PHI method</p> </li> <li> <p>Note on reflectivity calibration: The original manufacturer calibration constant was 7.56 dBZ. The value used here derives from comparison with disdrometers during the HYMEX campaign.</p> </li> </ul> <p> </p> <p><strong>dataframe_PAYERNE_2014_from_20140321-160016_to_20140519-085808.hdf5</strong></p> <p>Contains PPI scans at 90° elevation performed by MXPol during the PAYERNE campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 46.81° N</p> </li> <li> <p>Longitude: 6.94° E</p> </li> <li> <p>Altitude: 496 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.41 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.45°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 75 m</p> </li> <li> <p>Range to the first gate: 204.3 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.6</p> </li> <li> <p>Attenuation correction: None</p> </li> </ul> <p> </p> <p><strong>dataframe_DAVOS_2014_from_20140704-090224_to_20141231-105720.hdf5</strong></p> <p>Contains PPI scans at 90° elevation for the DAVOS campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 46.82° N</p> </li> <li> <p>Longitude: 9.82° E</p> </li> <li> <p>Altitude: 2220 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.41 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.45°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 75 m</p> </li> <li> <p>Range to the first gate: 204.3 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.6</p> </li> <li> <p>Attenuation correction: None</p> </li> </ul> <p> </p> <p><strong>dataframe_APRES3_from_20151207-123944_to_20160129-125856.hdf5</strong></p> <p>Contains PPI scans at 90° elevation for the APRES3 campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 66.66 S</p> </li> <li> <p>Longitude: 140.00 E</p> </li> <li> <p>Altitude: 40 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.41 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.45°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 75 m</p> </li> <li> <p>Range to the first gate: 354.3 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.6</p> </li> <li> <p>Attenuation correction: None</p> </li> </ul> <p> </p> <p><strong>dataframe_VALAIS_2016_from_20161104-154312_to_20170306-195912.hdf5</strong></p> <p>Contains PPI scans at 90° elevation for the VALAIS campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 46.12 N</p> </li> <li> <p>Longitude: 7.10 E</p> </li> <li> <p>Altitude: 460 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.41 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.27°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 30 m</p> </li> <li> <p>Range to the first gate: 226.95 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.6</p> </li> <li> <p>Attenuation correction: None</p> </li> </ul> <p> </p> <p><strong>dataframe_DX50_from_20140501-020000_to_20140524-015752.hdf5</strong></p> <p>Contains PPI scans at 90° elevation performed by DX50 during the PAYERNE campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 46.84° N</p> </li> <li> <p>Longitude: 6.92° E</p> </li> <li> <p>Altitude: 450 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.459 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.273°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 75 m</p> </li> <li> <p>Range to the first gate: 0.0 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.0</p> </li> <li> <p>Attenuation correction: None</p> </li> </ul> <p> </p> <p><strong>dataframe_DX50_RHI_201405.hdf5</strong></p> <p>Contains RHI scans from DX50 the PAYERNE campaign.</p> <p>The remaining information equal to the ones listed for <em>dataframe_DX50_from_20140501-020000_to_20140524-015752.hdf5</em>.</p> <p> </p> <p><strong>dataframe_MXPol_RHI_201405.hdf5</strong></p> <p>Contains RHI scans from MXPol the PAYERNE campaign.</p> <p>The remaining information is equal to the ones listed for <em>dataframe_PAYERNE_2014_from_20140321-160016_to_20140519-085808.hdf5</em>.</p>
Vertical hydrography profiles from CTD rosette downcasts during PolarFront cruise 2023-08
<p>PolarFront 2023-08 CTD profiles</p><p>Pressure, temperature, salinity, and other physical properties of seawater from 31 vertical profiles sampled during the PolarFront 2023-08 cruise. Only downcast values are used to avoid errors caused by turbulence on the upcast. Basic data processing were done using Sea-Bird Scientific software SBE Data Processing (v7.26): converting to physical units, filtering for outliers, and bin-averaging over 1 m bins. The final data accuracy is ±0.5 dbar for pressure, ±0.002˚C for sea water temperature and ±0.003 for sea water salinity.</p><p>Example final data `head -n3 stnr1085.xls`:</p><blockquote><p>scan: Scan Count depSM: Depth [salt water, m] prDM: Pressure, Digiquartz [db] t090C: Temperature [ITS-90, deg C] c0S/m: Conductivity [S/m] sal00: Salinity, Practical [PSU] sigma-t00: Density [sigma-t, kg/m^3 ] svCM: Sound Velocity [Chen-Millero, m/s] flSP: Fluorescence, Seapoint sbeox0PS: Oxygen, SBE 43 [% saturation] seaTurbMtr: Turbidity, Seapoint [FTU] par/sat/log: PAR/Logarithmic, Satlantic [umol photons/m^2/sec] sbeox0ML/L: Oxygen, SBE 43 [ml/l] depSM: Depth [salt water, m], lat = 74.9995 potemp090C: Potential Temperature [ITS-90, deg C] sal00: Salinity, Practical [PSU] sigma-é00: Density [sigma-theta, kg/m^3] svCM: Sound Velocity [Chen-Millero, m/s] oxsolML/L: Oxygen Saturation, Garcia & Gordon [ml/l] flag: flag 243 3.955 4.000 8.9775 3.708699 34.9575 27.0886 1486.13 7.9014e-01 105.842 1.003 2.0359e+01 6.8367 3.958 8.9771 34.9576 27.0886 1486.13 6.45936 0.0000e+00 327 4.945 5.000 8.9787 3.708880 34.9577 27.0885 1486.15 7.4764e-01 99.487 0.991 1.4706e+01 6.4261 4.948 8.9782 34.9578 27.0886 1486.15 6.45918 0.0000e+00</p></blockquote><p> File types</p><p>Each station number has multiple data files from various steps of processing: `ls stnr1085*`: </p><p>`stnr1085_bin.cnv stnr1085_bin.wmf stnr1085.bl stnr1085.btl stnr1085.btx stnr1085.cnv stnr1085.hdr stnr1085.hex stnr1085_RAW.cnv stnr1085.xls`</p><p>Notice that the *.xls are plain ascii text files with tab-separated values. These may be converted to utf-8 using `iconv -f iso8859-1 -t utf8`</p><ul><li>*.bl = Bottle log information. Output bottle file, containing bottle firing sequence number and position, data, time, and beginning and ending scan numbers for each bottle closure. Beginning and ending scan numbers correspond to approximately 1.5-second duration for each bottle.</li><li>*.btl = Bottle files. Averaged data for each bottle.</li><li>*.cnv = Data converted to engineering units.</li><li>*hdr = Header information.</li><li>.hex = Hexadecimal data file.</li><li>*.xls = Converted data binned to 1 m as ascii text (tsv).</li><li>*.xlmcon = Instrument configuration.</li><li>*_bin.cnv = Converted data binned to 1 m as ascii data.</li><li>*_bin.wmf = Graphic of converted and 1m-binned data. *_RAW.cnv = Raw data as text file.</li></ul>
BNNOz - Infilled vertically resolved ozone dataset
<p>This vertical ozone dataset is a fusion of an existing ozone dataset (<a href="http://www.bodekerscientific.com/data/monthly-mean-global-vertically-resolved-ozone">Bodeker Scientific</a>) with chemistry-climate model output from the Chemistry-Climate modelling initiative.</p> <p>The vertically and latitudinally resolved ozone dataset (zmo3_BNNOz.nc) has been produced by fusing the above data within a <a href="https://proceedings.neurips.cc/paper/2020/file/0d5501edb21a59a43435efa67f200828-Paper.pdf">Bayesian neural network</a>.</p> <p>More information about this processing and the data can be found <a href="https://github.com/mattramos/VertOzone-BNN">here</a>.</p> <p>In addition to the output product we include the training dataset of observed and modelled ozone as a python pickled dataframe. The code to use this training dataset can be found <a href="https://github.com/mattramos/VertOzone-BNN">here</a>.</p> <p>This data submission supports a manuscript submission to ESSD.</p>
Dataset for the study Late development of audio-visual integration in the vertical plane
<p>It is not clear how multisensory skills develop and how visual experience impacts on multisensory spatial development. Conflicting results show that visual calibration precedes multisensory integration for the audio-visual spatial bisection task (Gori et al., 2012a, 2012b) while in other tasks such as spatial localization, visual calibration occurs after multisensory development (Rohlf et al., 2020). Results in blind individuals can say something about the role of vision on perceptual development. Scientific evidences show that blind individuals have impairments in bisecting the auditory space (Gori et al., 2014) but not in localizing auditory sources (Lessard et al., 1998). Such results suggest that sensory calibration and impairment are linked. We studied the development of audio-visual multisensory localization in the vertical plane in sighted individuals from 5 years to adulthood to address this hypothesis. We hypothesize that typical children would show late audio-visual integration for the vertical plane, preceded by visual dominance. Unimodal and bimodal audio-visual thresholds and PSEs were measured and compared with the Bayesian optimal-integration model (maximum likelihood estimation). Results show that the development of multisensory integration in the vertical plane is not evident at 5 years, suggesting visual dominance for vertical audio-visual localization. These results support the idea that multisensory perception in the vertical domain depends on sensory calibration. We discuss these scientific results proposing that the process of cross-sensory calibration is task-specific and highlighting the importance of linking the impairment and development to better determine how our brain works.</p> <p>Data are in textual tab delimited format. Columns report for each subject: age, age_bin, condition, jnd.</p> <p> </p>
OMPS-NPP L2 LP USask Ozone (O3) Vertical Profile swath daily V1.1
<p>The USask OMPS-LP L2 2D Ozone v1.1 product provides ozone profile retrievals performed at the University of Saskatchewan for the central slit of the Ozone Mapping and Profiler Suite Limb Profiler (OMPS-LP) instrument on the Suomi-NPP satellite. The two-dimensional retrieval algorithm accounts for variation in the along orbital track dimension, retrieving an entire orbit simultaneously instead of treating each image independently. Ozone is retrieved from the thermal tropopause to 59 km on a 1 km grid with a vertical resolution of approximately 2 km.</p> <p>Each granule contains data from the daylight portion of each orbit measured for a full month. Spatial coverage is global (-82 to +82 degrees latitude), and there are about 14.5 orbits per day, each has typically 160 profiles with an along orbital track sampling of 125 km. The files are written using NetCDF4.</p>
Vertical profiles of urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland
<p><strong>Vertical Profiles of Urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland</strong></p> <p>=================================</p> <p>README version 1.3, 21/07/2022</p> <p>==================================</p> <p>Contact info:</p> <p>Paul Leahy, University College Cork</p> <p>paul.leahy@ucc.ie | +353 21 4902017</p> <p>================================</p> <p> </p> <p><strong>Contents</strong></p> <p><strong>1. Measurement location and time period</strong></p> <p><strong>2. What is measured (brief description)</strong></p> <p><strong>3. Instrumentation</strong></p> <p><strong>4. CSV file detailed descriptions</strong></p> <p>================================</p> <p> </p> <p><strong>1. Measurement location and time period: </strong></p> <p>North roof of Kane Building, University College Cork (UCC), Ireland.</p> <p>Lat 51 d 53 m 34 s N.</p> <p>Long 8 d 29 m 39 s W.</p> <p>Roof is c. 39 m above sea level, and c. 26 m above ground level (ground level reference point is the car park West of the UCC Kane Building).</p> <p>The measurements were taken over a time period of several months in the years 2013 / 2014.</p> <p>=================================</p> <p><strong>2. What is measured (brief description):</strong></p> <p>* LiDAR Wind speed (horizontal and vertical), wind direction, turbulence intensity at 5 altitudes; reference point (0 m) for these altitudes is the top of the LiDAR instrument c. 1.2 m above roof level.</p> <p>* Air temperature, atmospheric pressure, relative humidity.</p> <p>* Wind speed and direction from an ultrasonic anemometer mounted on top of the instrument (c. 1.2 m above roof level).</p> <p>* 10-minute average values (2 files) and high-resolution (c. 23 sec) data (1 file) are provided.</p> <p>See 'CSV file detailed description' below for detailed information.</p> <p>* Diagnostic information.</p> <p>=================================</p> <p><strong>2.1 Surrounding terrain:</strong></p> <p>Surrounding area is urban/suburban. The aspect is northerly.</p> <p>To the West: 2-5 storey buildings, open spaces, suburban.</p> <p>To the South: 2-3 storey buildings, open spaces, trees, river.</p> <p>To the East: 2-3 storey buildings, open spaces.</p> <p>To the North: A higher section of the Kane Building roof (47 m asl), 1-3 storey buildings, suburban.</p> <p>=================================</p> <p><strong>3. Instrumentation:</strong></p> <p>ZephIR 175 continuous wave wind profiling LiDAR with integrated sonic anemometer, temperature, humidity, air temperature pressure sensors and GPS.</p> <p>=================================</p> <p><strong>4. CSV files detailed description:</strong></p> <p><strong>4.1 Data on 10-minute averages:</strong></p> <p>Filename 05092013-03122013_10min_res.csv contains:</p> <p>10 minute averaged data from 05/09/2013 to 03/12/2013.</p> <p>Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m above instrument level.</p> <p> </p> <p>Filename 03122013-07082014_10min_res.csv contains:</p> <p>10 minute averaged data from: 03/12/2013 to 07/08/2014.</p> <p>Measurement altitudes: 148 m, 90 m, 50 m, 35 m, 15 m above instrument level.</p> <p>Note: from 19/06/2014 onwards, LiDAR data missing (MET data continues).</p> <p> </p> <p>The first two rows contain header information.</p> <p>Row 1 contains location information (GPS record)) and the measurement altitudes for wind speeds.</p> <p>Sample GPS record: N51535775W8296590 = 51 d 53.5775 m North; 8 d 29.6590 m West.</p> <p>Row 2 contains the data column headers including units.</p> <p> </p> <p>Wind speeds at each altitude are recorded:</p> <p>No of Packets (= number of scan units averaged over) []</p> <p>Wind direction (mean) [deg]</p> <p>Horizontal wind speed (mean) & standard deviation [m/s]</p> <p>Vertical wind speed (mean) & standard deviation [m/s]</p> <p>Horizontal variance [m^2/s^2] </p> <p>Horizontal min [m/s] </p> <p>Horizontal max [m/s] </p> <p>TI (turbulence intensity) []</p> <p> </p> <p>Other meteorological data:</p> <p>Air temperature [<sup>o</sup>C]</p> <p>Pressure [mbar]</p> <p>Rel. Humidity [%]</p> <p>Rain indicator [unitless] Higher values indicate more rain during the averaging interval.</p> <p>Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer)</p> <p>Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer).</p> <p> </p> <p>Other housekeeping and diagnostic data:</p> <p>Instrument tilt [deg]</p> <p>Instrument bearing [deg]</p> <p>GPS data [degrees N, degrees W]</p> <p>Battery voltage [V] </p> <p>Optics, electronics and battery temperature [<sup>o</sup>C]</p> <p> </p> <p>=====================================================</p> <p> </p> <p><strong>4.2 Data with high time resolution (~23 s):</strong></p> <p> </p> <p>Filename 05092013-11112013_23s_res.csv contains:</p> <p>High resolution data from 05/09/2013 to 11/11/2013</p> <p>Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m.</p> <p> </p> <p>Note on time resolution:</p> <p>The time resolution of processed wind measurements is c. 3 seconds per wind level, and around 8 seconds to reset to the first level. A full wind profile measurement at 5 altitudes therefore takes around (5 x 3) + 8 = 23 s to complete.</p> <p>The raw scanning resolution of the instrument is higher than this, as each wind measurement is an average of several values.</p> <p> </p> <p>Row 1 contains location information (lat, long) and the vertical measurement levels for wind speeds.</p> <p>Row 2 contains the data column headers including units.</p> <p> </p> <p>Wind speeds at each altitude are recorded:</p> <p>No of Packets (= scan units averaged over) []</p> <p>Wind direction (mean) [deg]</p> <p>Horizontal wind speed (mean) & standard deviation [m/s]</p> <p>Vertical wind speed (mean) & standard deviation [m/s]</p> <p>Horizontal variance [m^2/s^2] not defined as measurement interval is too short.</p> <p>Horizontal min [m/s] not defined as measurement interval is too short. </p> <p>Horizontal max [m/s] not defined as measurement interval is too short. </p> <p>TI (turbulence intensity) [] not defined as measurement interval is too short.</p> <p> </p> <p>Other meteorological data:</p> <p>Air temperature [<sup>o</sup>C]</p> <p>Pressure [mbar]</p> <p>Rel. Humidity [%]</p> <p>Rain indicator [unitless] Higher values indicate more rain during the scanning interval.</p> <p>Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer)</p> <p>Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer.</p> <p> </p> <p>Other housekeeping and diagnostic data:</p> <p>Instrument tilt [deg]</p> <p>Instrument bearing [deg]</p> <p>GPS data [degrees N, degrees W]</p> <p>Battery voltage [V] </p> <p>Optics, electronics and battery temperature [<sup>o</sup>C]</p> <p> </p> <p>=====================================================</p> <p><strong>4.3 Quality control indicators:</strong></p> <p> </p> <p>9998 atmospheric conditions which adversely affect LiDAR wind speed measurements e.g. fog</p> <p>9999 high quality wind speed measurement not possible e.g. very low wind speed or obscuration of optical path</p> <p>Status Flag 'Green' => good</p> <p>=======================================================</p> <p> </p>
Data for: Impact of SO2 injection profiles on simulated volcanic forcing for the Sarychev 2009 eruptions - investigating the importance of using high vertical resolution methods when compiling SO2 data
<p>The files are data assosicated with the study High-resolution stratospheric volcanic SO2 injections in WACCM. The files are associated with four differnt simulaions described in the paper: M16, S21-1D, S21-3D and No-Volc. The files with "input" in the name are the SO2 input files used in the WACCM (Whole Atmosphere Community Climate Model) simulations in the paper. The files with "monthly_averages" in the filenames are monthly averages of model output data the variables used in the paper. </p> <p>The CALIOP_monthly_averages.nc file is monthly average of the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) satellite data used in the study to evaluate the WACCM simulations. </p> <p> </p>
Supplementary files for Vertical Displacements and Sea-Level Changes in Eastern North America Driven by Glacial Isostatic Adjustment: an Ensemble Modeling Approach
<p>Model input and output files associated with the manuscript entitled "Vertical Displacements and Sea-Level Changes in Eastern North America Driven by Glacial Isostatic Adjustment: an Ensemble Modeling Approach" that will be submitted to Journal of Geophysical Research.</p>
Vertical velocity field from the JCOPE-T-NEDO simulation
<p>Vertical velocity field from the "NEDO" version of the "JCOPE-T" ocean general circulation model (Varlamov et al 2015; Wang et al 2024).</p> <p><a href="../api/records/13132471/draft/files/wzm-2014.11.01-2014.11.11.nc.gz/content">wzm-2014.11.01-2014.11.11.nc.gz</a> contains the hourly-mean vertical velocity "wzm" from 133.3°E to 148°E, from 24°N to 28.3°N, and from 2014-11-01T00:00:00Z to 2014-11-11T00:00:00Z. The model uses the sigma coordinates and the data file contains the variable zed(x,y,sigma) that indicates the depth at which "wzm" is defined for each x and y.</p>
Data and Analysis for Kaplanis, Denny, and Raimondi 2024, "Vertical distribution of rocky intertidal organisms shifts with sea-level variability on the Northeast Pacific Coast".
<p>This repository contains all the data and R scripts used to produce all analyses and figures for Kaplanis, Denny, and Raimondi 2024, as well as all intermediate outputs and final figures. To access this content, download and unzip the intertidalvertdist folder (for intertidal vertical distribution). The R Project is titled "intertidalvertdist". All pertinent information needed to access data, replicate the analyses, and produce figures is contained within the README file, but a brief desciption is below.</p> <p><br>Directory Architecture:</p> <p>Data:<br>Contains all data. Within this folder are two subdirectories - Raw Data, and Processed Data. Raw Data are unmanipulated, straight from the data source. Processed Data are outputs from scripted data wrangling and transformations. </p> <p>Within each of these folders are two more subdirectories: Tide Gauge Data, and MARINe Data. These are the two data sources used in this manuscript - monthly sea-level data from The National Oceanic and Atmospheric Administration Center for Operational Oceanographic Products and Services (NOAA CO-OPS) tide gauge stations, and long-term rocky intertidal biological monitoring data from Multi-Agency Rocky Intertidal Network (MARINe) survey sites.</p> <p>Scripts:<br>All R scripts are contained within the Scripts folder. The scripts have the prefix IVD (for intertidal vertical distribution), then a name that indicates the major function of the code. The scripts either downloads data, manipulates data, conducts analyses, and/or produces a figure.</p> <p>Outputs:<br>Any figures and tables from preliminary analyses, but that are not used in the final manuscript, are saved in Outputs.</p> <p>Figures:<br>All final figures and tables are contained in the Figures folder. All figures are produced by scripts, except Figs. 1 and 2, which are schematics produced manually in a graphics editor. This folder contains two other folders: Supplemenatary Figures, and Partial Regression Plots. Partial Regression plots are the same as the final Figures 8-12, except they are grouped by taxa rather than by explanatory variable.</p> <p>Data Processing Workflow - Overview: <br>Tide Gauge Data (Data/Raw Data/Tide Gauge Data/individual stations) were downloaded using the NOAA Co-Ops API URL Builder (https://tidesandcurrents.noaa.gov/api-helper/url-generator.html), merged, then analyzed. Three MARINe data sets from the Coastal Biodiversity Survey (CBS) were accessed via data requests (https://marine.ucsc.edu/explore-the-data/contact/data-request-form.html). The first MARINe dataset (Data/Raw Data/MARINe Data/CBS_Percent Cover Data, both First Sample and Full Sample) was used to determine the top ten most abundant taxa (hereafter termed “dominant taxa”) across CBS survey sites during the monitoring period of 2001-01-01 to 2021-09-30. The second MARINe dataset (Data/Raw Data/MARINe Data/CBS_Elevation Data) was used to describe the upper limits of vertical distribution of dominant taxa through time. The third MARINe dataset (Data/Raw Data/MARINe Data/CBS_Presence Data) was used to visualize latitudinal distribution of taxa.</p> <p>Location information for Tide Gauge Stations and CBS Survey Sites were assembled into a table (Data/Raw Data/CBS_Tide Gauge_Data.csv)</p> <p>Tide Gauge Data were processed first, then MARINe Data. To replicate this workflow follow the steps described in the README file, in order.</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.