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88 results for “Argos”
BGC-Argo matchups with Ocean Color Satellite Sensors and MERRA-2 updated for 2023
<p>Updated matchup dataset as described in "Begouen Demeaux et al., Algorithms to Retrieve the Spectral Diffuse Attenuation Coefficient of Light in the Ocean from Remote Sensing, Optics Express, 2023".</p> <p>Composed of Satellites matchup from the MODIS, VIIRS and OLCI sensors with BGC-Argo floats, including Kds derived from float measurements (Kd_WV_Xing), Rrs at all wavelengths from each sensor, solar zenith angle and information on the atmospheric composition from Merra-2 matchups. </p> <p>New recomputed Kds using the Lee et al., 2005 algorithm with individual sensor coefficients are also listed (new_kd_WV_Lee_indiv), as well as recomputed Kds for a new global m2 coefficient (new_kd_WV_Lee_global). Recomputed Kds for the new coefficients of the NASA/ESA algorithm are also available (new_Kd_Aus). Lastly, Kds for the new GF algorithm depending on the MERRA inputs and IOPs is listed : (kd_WV_f). </p> <p>For any questions, do not hesitate to be in touch. </p>
BGC-Argo Satellite matchup to compute variability in the Chl:C ratio of phytoplankton.
<p>This dataset provides matchups between BGC-Argo and MODIS satellites (both atmospheric and ocean color products). This dataset allows users to compare the variability of the Chlorophyll (Chl) to Phytoplankton Carbon ratio from BGC-Argo floats depending on the light in the mixed layer and link to information obtained from satellites about cloud coverage. </p> <p>Quality control previously performed on this dataset and matchup criteria are described in the associated publication.</p> <p>Here are some of the column headers detailed for clarity:</p> <p>Columns 1-25 represent data from the BGC-Argo floats:</p> <ul> <li>ID: Float WMO ID number</li> <li>dt: Datetime in datenum format.</li> <li>biomes: Biomes according to Fay & McKinley, 2014 (with West Mediterranean biome 18 and East Mediterranean biome 19)</li> <li>zenith: Sun zenith angle at time of surfacing.</li> <li>kd_490_Xing: Downwelling diffuse attenuation coefficient at 490nm from Xing et al.,2021 method. </li> <li>kd_PAR_Xing: Downwelling diffuse attenuation coefficient of PAR from Xing et al.,2021 method. </li> <li>chla: Median chlorophyll from fluorescence in the mixed layer (corrected for Non-Photochemical Quenching following Xing et al., 2012)</li> <li>F_indiv: Calibration factor for chla (chlorophyll from fluorescence) according to the method described in Xing et al., 2011. </li> <li>F_median: Median Correction factor (F) for all the floats in a biome</li> <li>F_median_season: Median Correction factor (F) for all the floats in a biome in a given season</li> <li>Chl_cor: Chla from floats corrected using the F_median factor according to Xing et al., 2011. </li> <li>PAR_0_Argo: PAR(0-) right below the surface also from Xing et al., 2021.</li> <li>Z_iso : Depth of the 0.415 mol/quanta/m-2/d-1 isolume. </li> <li>Zeu: Euphotic depth, 1% of surface light.</li> <li>Eg_Argo: Median light level in the mixed layer during a float's profile, bounded by the surface and the MLD (in mol quanta m^-2 h^-1).</li> <li>MLD: Mixed layer depth, determined using the 0.03 density criteria from de Boyer Montégut, et al.,2004.</li> <li>bbp_XXX: Backscattering at a specific wavelength</li> <li>Cphyto: Median Phytoplankton Carbon in the mixed layer computed from Bbp following Graff et al., 2015.</li> <li>Cphyto_B: Median Phytoplankton Carbon in the mixed layer computed from Bbp following Behrenfeld et al., 2005.</li> <li>Cphyto_M: Median Phytoplankton Carbon in the mixed layer computed from Bbp following Martinez-Vincente et al., 2013.</li> <li>ratio_cor: Chl_cor /Cphyto.</li> </ul> <p>Columns 26-45 have products from matchups with ocean-color MODIS files:</p> <ul> <li>sat_dt: Datetime of satellite overpass in datenum format. </li> <li>chlor_a: Satellite chl obtained from NASA's OBPG hybrid algorithm.</li> <li>sat_IPAR: Instantaneous PAR at time of overpass.</li> <li>sat_PAR: MODIS Daily PAR product above the surface.</li> <li>sat_Daily_PARminus: MODIS Daily PAR product propagated right below the surface (0-)</li> <li>sat_Daily_Eg: Daily median light in the mixed layer computed as sat_DailyPAR_minus * exp(-Kd_PAR*MLD/2) ( in mol quanta m^-2 d^-1).</li> </ul> <p>Columns 46-49 have products from matchups with atmospheric MODIS files: </p> <ul> <li>a_lat, a_lon, a_dt: Same as above but for the atmospheric file</li> <li>Confident Cloudy: Number of pixels (Out of 25) with the Confident Cloudy flag. </li> </ul> <p> </p>
Atlantic Meridional Overturning Circulation Near 41N from Altimetry and Argo Observations
<p>Updated Jan 17, 2024 to include estimates through calendar year 2024.</p> <p>These files contain an estimate of the Atlantic Meridional Overturning Circulation (AMOC) volume and heat transports, computed using observations of temperature, salinity and subsurface velocity from the Argo array of profiling floats (DOI: 10.17882/42182#116315), and satellite-based observations of sea level from altimetry (DOI: 10.48670/moi-00148 and DOI: 10.48670/moi-00149). The estimates are computed using the techniques of Willis (2010) and Hobbs and Willis (2012). In addition, estimates of wind stress at the surface were estimated from European Center for Medium Range Weather Forecast, ERA5 analysis (DOI: 10.24381/cds.143582cf).</p> <p>Note that in all files, although there are 12 time-steps per year, each time step represents a 3-month average, so the time series is over sampled.</p> <p>The .txt file contains comma separated values of the time series, with 1 header line and the following columns, estimated as in Willis (2010) and Hobbs and Willis (2012): </p> <p>Column 1: Decimal year</p> <p>Column 2: Ekman Volume Transport (Sverdrups)</p> <p>Column 3: Northward Geostrophic Transport (Sverdrups)</p> <p>Column 4: Meridional Overturning Volume Transport (Sverdrups)</p> <p>Column 5: Meridional Overturning Heat Transport (PetaWatts)</p> <p>The file called “trans_Argo_ERA5.nc” contains an estimate of the geostrophic transport as a function of latitude, longitude, depth and time, for the upper 2000 m for latitudes near 41 N in the Atlantic Ocean, estimated as described in Willis (2010). Also included are Ekman Transport and Overturning Transport as functions of time and latitude for this region.</p> <p>The file called “Q_ARGO_obs_dens_2000depth_ERA5.nc” contains estimates of heat transport for these regions based on various assumptions about the temperature of the ocean at depths unmeasured by the Core Argo array (depths below 2000m), estimated as described in Hobbs and Willis (2012). These assumptions are described in the variable “Hpar”.</p> <p> </p> <p>If you use these data please cite:</p> <p>Willis, J. K., and Hobbs, W. R., Atlantic Meridional Overturning Circulation Near 41N from Altimetry and Argo Observations. Dataset access [YYYY-MM-DD] at 10.5281/zenodo.8170366.</p> <p> </p> <p>References & Acknowledgements:</p> <p>Hobbs, W. R., and J. K. Willis (2012), Midlatitude North Atlantic heat transport: A time series based on satellite and drifter data. J. Geophys. Res., 117, C01008, doi:10.1029/2011JC007039.</p> <p>Willis, J. K. (2010), Can in situ floats and satellite altimeters detect long-term changes in Atlantic Ocean overturning?, Geophys. Res. Lett., 37, L06602, doi:10.1029/2010GL042372. http://www.agu.org/pubs/crossref/2010/2010GL042372.shtml</p> <p>This study has been conducted using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00149">https://doi.org/10.48670/moi-00149</a> and <a href="https://doi.org/10.48670/moi-00148">https://doi.org/10.48670/moi-00148</a></p> <p> </p> <p>These data were collected and made freely available by the International Argo Program and the national programs that contribute to it. (https://argo.ucsd.edu, https://www.ocean-ops.org). The Argo Program is part of the Global Ocean Observing System. “</p> <p>Argo (2000). Argo float data and metadata from Global Data Assembly Centre (Argo GDAC). SEANOE. <a href="https://doi.org/10.17882/42182#116315">https://doi.org/10.17882/42182#116315</a><a name="_Hlk188024500"></a></p> <p>Hersbach, H., et al. (2017): Complete ERA5 from 1940: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service (C3S) Data Store (CDS). DOI: 10.24381/cds.143582cf (Accessed on 24-Dec-2022)</p> <p> </p>
Global Ocean Heat Content Anomalies and Ocean Heat Uptake based on mapping Argo data using local Gaussian processes
<p>Monthly Ocean Heat Content Anomalies (OHCA) in the top 2000 dbar of the ocean are calculated (during 2004-2024, equatorward of 65 degree latitude) subtracting the mean over the period 2004-2024 from the monthly time series of OHC. Yearly OHCA time series are then calculated that include 1. one point per year, i.e., from averaging Jan to Dec (see files ending in “yearly.nc”), and 2. two points per year, i.e., from averaging Jan to Dec and Jul to Jun, respectively (see files ending in “yearly2.nc”). OHC fields are mapped using locally stationary Gaussian processes (defined over space and time) with data-driven decorrelation scales (Kuusela and Stein, 2018). A linear time trend was included in the estimate of the mean field (along with spatial terms and harmonics for the annual cycle). Mapping is done separately for different vertical sections: 15-20 dbar, 15-300 dbar, 300-700 dbar, 700-1850 dbar, 1800-1850 dbar. The 15-20 dbar (1800-1850 dbar) section is used to estimate OHCA for 0-15 dbar (1850-2000 dbar), where observations are sparser. Different vertical sections are combined to estimate global OHCA time series for 0-2000 dbar, 0-700 dbar, 700-2000 dbar (as indicated in the file names). The attribute "area" is included in the netcdf files and it tells the corresponding surface area for the estimates. Regions of the ocean that are shallower than 300 m or are not sufficiently well sampled by the Argo array are not included. Maps of the ocean masks used for the different vertical sections can be found in the .png files (blue shading indicates the area used for the horizontal integral); the bathymetry mask by Roemmich and Gilson (included in the file RG_ArgoClim_Temperature_2019.nc at https://sio-argo.ucsd.edu/RG_Climatology.html) is also used to define the ocean mask. Ocean Heat Uptake is calculated from the monthly OHCA and then averaged as described above to produce yearly time series included in the files for the different layers.</p> <p>For the uncertainty at each time point, the standard deviation of each OHCA/OHU value in the time series is included. When plotting a time series, the user may consider, e.g., shading plus/minus 1* or 1.96*standard deviation (corresponding to a confidence level of 68% or 95% respectively). These standard deviations in the files are estimated using spatially and temporally dependent conditional simulations of monthly gridded anomalies. When combining different layers, the standard deviation of the sum is conservatively estimated as the sum of the standard deviations. </p> <p>Finally, OHCA/OHU trends are estimated via a least-squares fit and reported in the variable metadata with uncertainties (confidence level of 68%). Trend uncertainties are estimated by repeating the fit for each member of the conditional simulation ensemble described above.</p> <p> </p>
Mean current velocity sections along 11°S, 5°S, 35°W, and 23°W from shipboard measurements used in "Transports and pathways of the tropical AMOC return flow from Argo data and shipboard velocity measurements"
<p>This data set contains current velocity measurements used in the study "Transports and pathways of the tropical AMOC return flow from Argo data and shipboard velocity measurements“ by <em>Tuchen et al. (2022)</em> published at <em>Journal of Geophysical Research: Oceans</em>.</p> <p>For the meridional mean sections along 35°W and 23°W, and for the quasi-zonal sections along 11°S and 5°S, one ".mat" file is provided for each of the sections. Please note that the section along 11°S consists of a zonal part (east of 34.2°W) and a cross-shore part closer to the coast. The meridional velocities along the cross-shore part of the 11°S-section are rotated clockwise by 36° in order to derive along-shore velocities.</p> <ul> <li>11°S: meridional velocity / alongshore velocity (V), neutral density (gamma_n), longitude (LON), depth (Z)</li> <li>5°S: meridional velocity (V), neutral density (gamma_n), longitude (LON), depth (Z)</li> <li>35°W: zonal velocity (U), neutral density (gamma_n), latitude (LAT), depth (Z)</li> <li>23°W: zonal velocity (U), neutral density (gamma_n), latitude (LAT), depth (Z)</li> </ul> <p>Mean velocity data in the upper 10 m are replaced by the gridded mean surface current velocities at 1/4° horizontal resolution derived from satellite-tracked surface drifting buoys (<em>Laurindo et al. 2017</em>) that were horizontally interpolated to the resolution of the individual ship sections.</p>
Data in support of 'The deep western boundary current of the Southwest Pacific Basin: insights from Deep Argo'
<p>Data in support of 'Chandler M, Zilberman NV, Sprintall J. (2024). The deep western boundary current of the Southwest Pacific Basin: insights from Deep Argo. <em>Journal of Geophysical Research: Oceans</em>. <a href="https://doi.org/10.1029/2024JC021098" target="_blank" rel="noopener">https://doi.org/10.1029/2024JC021098</a>'</p> <p>There are 4 netCDF files:</p> <ol> <li>swpb_dwbc_deep_argo_profiles_chandler2024.nc</li> <li>swpb_dwbc_deep_argo_trajectories_chandler2024.nc</li> <li>kt_dwbc_deep_argo_time_series_chandler2024.nc</li> <li>kt_dwbc_deep_argo_seasonal_cycles_chandler2024.nc</li> </ol> <p><strong>swpb_dwbc_deep_argo_profiles_chandler2024.nc </strong>contains the delayed-mode profiles of potential temperature and salinity on a 10-dbar pressure grid from the Deep Argo floats profiling within the deep western boundary current of the Southwest Pacific Basin. <em>[pressure; latitude; longitude; time; wmo_id; theta; salinity]</em></p> <p><strong>swpb_dwbc_deep_argo_trajectories_chandler2024.nc </strong>contains delayed-mode trajectories from the Deep Argo floats profiling within the deep western boundary current of the Southwest Pacific Basin. <em>[latitude; longitude; u; v; pressure; wmo_id; time]</em></p> <p><strong>kt_dwbc_deep_argo_time_series_chandler2024.nc</strong> contains the 2021--2022 monthly time series of dynamic height, salinity, and potential temperature between 2000--4000-dbar computed from the spatially-averaged Deep Argo profiles within the deep western boundary current as it travels along the western side of the Kermadec Trench. <em>[time; pressure; theta; salinity; dh; region_long; region_lat]</em></p> <p><strong>kt_dwbc_deep_argo_seasonal_cycles_chandler2024.nc</strong> contains seasonal cycles of dynamic height, salinity, and potential temperature (including the decomposition into heave/spice) between 2000--4000-dbar from the Deep Argo profiles within the deep western boundary current as it travels along the western side of the Kermadec Trench. <em>[pressure; theta; theta_heave; theta_spice; salinity; dh; region_long; region_lat]</em></p> <p>Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (<a href="https://argo.ucsd.edu/" target="_blank" rel="noopener">https://argo.ucsd.edu/</a>). The Argo Program is part of the Global Ocean Observing System. A full list of acknowledgements can be found in the affiliated <a href="https://doi.org/10.1029/2024JC021098">publication</a>.</p> <p><code>Version history:</code><br><code>v1.0 First created (06-March-2024)</code><br><code>v1.1 Updated to include accepted publication reference (15-October-2024)</code></p>
Biogeochemical Argo Adjusted Oxygen Data
<p>This archive contains two netCDF files used in the work reported by Johnson and Bif (2021). These files contain vertical profiles of corrected dissolved oxygen (Argo variable DOXY_ADJ), temperature, salinity, pressure, location, and time data obtained by profiling floats in the Biogeochemical-Argo array. The files were created by downloading all Argo .Sprof files directly from the Argo Global Data Assembly Center (ftp://usgodae.org/pub/outgoing/argo or ftp://ftp.ifremer.fr/ifremer/argo) in December 2020. The corresponding monthly snapshot (December 2020, https://doi.org/10.17882/42182#79118) of the Argo database will contain these .Sprof files, in addition to data for floats that do not have biogeochemical sensors.</p> <p><br> Merged netCDF (.nc) data files were then created by importing all of the .Sprof files into Ocean Data View (ODV, odv.awi.de) using the List File Generator tool in ODV. After eliminating 9 floats with anomalous adjusted oxygen data, which are listed in Johnson and Bif, the vertical profiles for all floats with adjusted oxygen data were exported as two netCDF files using ODV. One file contains data in the northern hemisphere (90N to 10S), which the second contains data from 0N to 90S). The files overlap from 0 to 10S, which enabled all equatorial data (10N to 10S) to be processed with one file.</p> <p><br> The netCDF files contain the adjusted dissolved oxygen concentration in µmol/kg (DOXY_ADJ), oxygen anomaly (oxygen minus oxygen saturation at one atmosphere pressure), temperature, salinity, pressure (dbar), profile position and date, quality flags for each observation, and float WMO number and type.</p> <p><br> Johnson, K. S. &amp; Bif, M. B. (2021) Constraint on net primary productivity of the global ocean by Argo oxygen measurements. Nature Geoscience. In press.</p> <p> </p>
Argo-based ocean surface mixed layer depths using the buoyancy gradient definition of Whitt Nicholson and Carranza (2019)
<p>Argo-based mixed layer depth profiles derived from the CORA product as described in Whitt Nicholson Carranza. A binned 2-degree climatology was published previously:</p> <p>https://github.com/danielwhitt/globalimpacts_2019_whittetal/blob/master/MonthlyClimatology_ARGO_MLDbmax_TEOS10_Copernicus_PF_2000-2017_all_jun252019_nc.nc</p> <p>with:</p> <p>Whitt, D. B., Nicholson, S. A., & Carranza, M. M. (2019). Global Impacts of Subseasonal (< 60 Day) Wind Variability on Ocean Surface Stress, Buoyancy Flux, and Mixed Layer Depth. <em>Journal of Geophysical Research: Oceans</em>, <em>124</em>(12), 8798-8831</p> <p>Contact the authors with questions. </p> <p>The chosen mixed layer depth definition is the same as "HMXL", a standard output of the Community Earth System Model (CESM) ocean component.</p>
profiles of chlorophyll and photosynthetically available radiation (PAR) from Bio-Argo float measurements for 2013-2020 interpolated on regular 2 m grid for the World Ocean
<p>The dataset includes profiles of chlorophyll and photosynthetically available radiation (PAR) from Bio-Argo float measurements for 2013-2020 interpolated on regular 2 m grid for the World Ocean </p> <p>Data was collected from open archive (<em>Argo float data and metadata from Global Data Assembly Centre (Argo GDAC)) </em><a href="https://doi.org/10.17882/42182">https://doi.org/10.17882/42182</a></p> <p>Global array of Bio-Argo floats equipped with Chl (mg m−3) and PAR(μmol photons m-2 s-1) sensors at -60°S..60°N was used in this study. Data for 2013-2020 was downloaded from the IFREMER data archive (ftp://ftp.ifremer.fr/, <a href="https://doi.org/10.17882/42182">https://doi.org/10.17882/42182</a>). It includes 464 floats measuring Chl (~ 70000 profiles), and 167 floats measuring both PAR (~26000 profiles) and Chl. Before the analysis, the measurements of each Bio-Argo buoy were visually checked to filter the outliers in Chl or PAR data. After visual analysis about 1600 profiles of PAR and 2800 profiles of Chl were excluded from the dataset.</p> <p>Chl (mg m−3) was retrieved from a Chl fluorometer (excitation at 470 nm; emission at 695 nm) sensors of three types (FLBB, ECO-Triplet, or MCOMS). We use the raw fluorescence-based estimates of Chl (product “non-adjusted Chl”) derived directly from the measurements of fluorescence with factory calibration coefficients without the corrections on non-photochemical quenching, CDOM fluorescence, and other effects (see (<a href="http://www.argodatamgt.org/Documentation">http://www.argodatamgt.org/Documentation</a>)).</p> <p>A multispectral ocean color radiometer (OCR-504, SATLANTIC Inc.) was used to measure PAR. Only instantaneous PAR measurements made within ± 1.5 hours from noon (10:30-13:30 hours) were used.</p> <p>Then the data from all buoys were interpolated on regular 2-m grid and included in one dataset.</p>
Data and Videos for Argos: a toolkit for tracking multiple animals in complex visual environments
<p>Original videos used and data generated for the article "Argos: a toolkit for tracking multiple animals in complex visual environments".</p> <p>The data contains original videos used as input to the Argos Tracking tool, the generated raw tracks in Pandas-HDF5 format, and the corrected tracks after processing with Argos Review tool.</p> <p>It also includes a zip archive with ground truth tracks along with tracks detected from two videos by Argos and several other tracking tools for comparison using the HOTA metric organized in a folder structure suitable for the TrackEval tool.</p>
Data supplement for 'Global Dataset of Thermohaline Staircases obtained from Argo Floats and Ice-Tethered Profilers'
<p>This is the data supplement for 'Global Dataset of Thermohaline Staircases obtained from Argo Floats and Ice-Tethered Profilers'. Both algorithm and dataset described in this publication can be found in this folder.</p> <p>Please cite 'Global dataset of thermohaline staircases obtained from Argo floats and Ice-Tethered Profilers' when using this data set (doi: 10.5194/essd-2020-197).</p> <p>The newest/most updated version of the code can be found on GitHub: https://github.com/cvanderboog/Staircase-detection-algorithm.</p> <p> </p> <p> </p> <p> </p>
Mapping Across OpenAIRE And Argos Data Models And The DMP Common Standard
<p>This is the outcome of the mapping activity across the data models of Argos and the OpenAIRE Research Graph and the RDA DMP Common Standard.</p>
Global Ocean Heat Content Anomalies based on Argo data
<p><strong>NOTE for users: please use the latest version of the product at https://zenodo.org/doi/10.5281/zenodo.10182972. </strong>Ocean Heat Content Anomalies (OHCA) are calculated (during 2005-2022) subtracting the mean over the period 2005-2021 from the monthly time series. Yearly OHCA time series are then calculated. OHC fields are mapped using locally stationary Gaussian processes with data-driven decorrelation scales (Kuusela and Stein, 2018). A linear time trend was included in the estimate of the mean field (along with spatial terms and harmonics for the annual cycle). In the present version, mapping is done in latitude and longitude with monthly subsets of data (a future version will add time to the mapping). Mapping is done separately for different vertical sections. Different vertical sections are combined to estimate: 1. Global OHC timeseries (e.g., for level 0-2000m: GCOS_0000_2000_OHCA_J_m2_oc, for OHC in J/m2; GCOS_0000_2000_OHCA_ZJ, for OHC in ZJ; the attribute “GCOS_area” is included for both variable types in the netcdf file and it tells the corresponding surface area); 2. Volume averaged temperature anomaly (global) timeseries (e.g., for level 0-2000m: GCOS_0000_2000_vol_ave_temp_anom, in degC; the attribute “GCOS_volume'' is included for this variable type in the netcdf file and it tells the corresponding volume). Regions of the ocean that are shallower than 300 m or are not sufficiently well sampled by the Argo array are not included.</p>
Argos Platform Transmitter Terminal data for 3 Southern Giant Petrel (Macronectes giganteus) during breeding season in Fildes Peninsula.
<p>Argos Platform Transmitter Terminal raw data for 3 Southern Giant Petrel (Macronectes giganteus) during breeding season in Fildes Peninsula, more specifically in the Islet known as Diomedea or Albatross Islet. All location classes are included. Locations are in EPSG 4326 (WGS 84). All animals had an active nest during data collection.</p> <p> </p> <p>Data generates with funding from INACH Programa Areas Marinhas Protegidas (24 04 052) and Agencia Nacional de Investigación y Desarrollo, Instituto Milénio Base ICN2021_002</p>
Argo Trajectories under ice (Southern Hemisphere, version 2022.05)
<p>Argo floats sometimes sample under ice, and do not return a measured position. Here, we provide estimates of positions using the multiple-constraint method described by Oke et al. (2022). </p> <p>Each file includes longitude and latitude from GPS measurements when floats are not under ice. When floats are under ice, positions are estimated by linearly interpolating between known locations (the traditional approach used by the Argo community), and using constraints: potential vorticity, f/H; mean sea-level, and density at 1000 m. A merged trajectory is also included, but users might select their preferred estimate based on their understanding of the ocean circulation at the time of measurement.</p> <p>Oke, P. R., T. Rykova, G. S. Pilo, J. L. Lovell, 2022: Estimating Argo float trajectories under ice, Journal of Geophysical Research - Earth and Space Science, under review.</p>
Global characterization of the ocean's internal gravity wave vertical wavenumber spectrum from Argo float profiles
<p>Oceanic internal gravity wave energy levels E (m^2/s^2), vertical wavenumber spectral slopes s, and vertical wavenumber scale m* (1/m) estimated by fitting the Garrett Munk model vertical wavenumber shape function to strain spectra obtained from Argo float hydrographic profiles based on the finestructure method, as discussed in Pollmann (2020): "Global Characterization of the Ocean’s Internal Wave Spectrum" (<em>Journal of Physical Oceanography</em> 50.7: 1871-1891). The paper and hence this dataset are a contribution to the Collaborative Research Centre TRR181 ‘Energy Transfers in Atmosphere and Ocean’ funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Projektnummer 274762653. The hydrographic profiles used in this study were collected and made freely available by the International Argo Program and the national programs that contribute to it (http://www.argo.ucsd.edu, http://argo.jcommops.org). The Argo Program is part of the Global Ocean Observing System.</p> <p>Please cite Pollmann (2020) when using this dataset.</p> <p>This dataset includes:</p> <p>a) energy density (m^2/s^2) binned into 1°x1° horizontal bins and averaged into 3 depth bins (300-500 m, 500-1000 m, 1000-2000 m)</p> <p>b) vertical wavenumber spectral slopes binned into 1°x1° horizontal bins and averaged into 3 depth bins (300-500 m, 500-1000 m, 1000-2000 m)</p> <p>c) vertical wavenumber scale m* (1/m) binned into 1°x1° horizontal bins and averaged into 3 depth bins (300-500 m, 500-1000 m, 1000-2000 m)</p> <p>d) latitude and longitude, defined such that, e.g., E(10,10) represents energy levels in the bin bounded by lat(10), lat(11) as well as lon(10), lon(11)</p>
Figure 33. Repaired Argonauta argo shell from Monterey, California. Repaired A in Recognising variability in the shells of argonauts (Cephalopoda: Argonautidae): the key to resolving the taxonomy of the family
Figure 33. Repaired Argonauta argo shell from Monterey, California. Repaired A. argo shell from Monterey, California (81.9 mm shell length, USNM 61374): a, left lateral view; b, oblique left lateral view; c, oblique anterior aperture view. Note change in direction of lateral ribs along repair line. Scale bar = 1 cm.
Dataset: Argo Blockchain plc 8.75% Senior Notes due 2026 (ARBKL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Argo Blockchain plc (ARBK) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Fig. 11 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 11: RMSE profiles for temperature (A) and salinity (C). All associated profiles differences (Argo-model) for temperature (B) and salinity (D) in 6 discrete depths (10 m dark blue, 20 m light blue, 30 m red, 40 m pink, 50 m green, 60 m yellow).
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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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.