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49 results for “sea surface height”
Estimate of the atmospherically-forced contribution to sea surface height variability based on altimetric observations
<p>This repository contains the estimate of the atmospherically-forced contribution to sea level variability described in <a href="http://doi.org/10.1016/j.pocean.2020.102314">Close et al, 2020</a>, and derived from the Ssalto/Duacs altimeter products produced and distributed by the Copernicus Marine and Environment Monitoring Service (CMEMS) (<a href="http://www.marine.copernicus.eu">http://www.marine.copernicus.eu</a>).</p> <p>The files contain successive 5-day averages of sea level anomaly, with the same global coverage and 0.25° grid as the Ssalto/Duacs altimeter products. The estimate is created using a spatial bandpass filter, with cutoff scales of ~1.5° and 10.5°. Zeros in the mask file indicate regions in which it has not been possible to evaluate the quality of the estimate.</p> <p>The cutoff scales applied to the altimetry data were determined through analysis of output from the OceaniC Chaos – ImPacts, strUcture, predicTability (Penduff et al, 2014) experiment, comprising a 50-member ensemble of ocean-sea ice model hindcasts with 0.25° horizontal resolution (<a href="http://doi.org/10.5194/gmd-10-1091-2017">Bessières et al., 2017</a>). The spatiotemporal coherence between the model-based estimates of the atmospherically-forced (ensemble mean) and total simulated sea surface height signals was analysed, and found to exhibit distinct partitioning between the atmospherically-forced and intrinsic contributions in a spatial (but not temporal) sense, thus suggesting that meaningful estimation of the two components can be achieved based on simple spatial filtering. Verification of the method using the model data indicates good accuracy, with a global mean correlation of 0.9 between the estimate based on spatial filtering and the ensemble mean sea surface height. Full details of the methodology and verification may be found in <a href="http://doi.org/10.1016/j.pocean.2020.102314">Close et al, 2020</a>.</p> <p>----</p> <p><strong>References</strong>:</p> <p>Bessières, L., Leroux, S., Brankart, J.-M., Molines, J.-M., Moine, M.-P., Bouttier, P.-A., Penduff, T., Terray, L., Barnier, B., and Sérazin, G., 2017. Development of a probabilistic ocean modelling system based on NEMO 3.5: application at eddying resolution, Geosci. Model Dev., 10, 1091–1106, <a href="https://doi.org/10.5194/gmd-10-1091-2017">doi: 10.5194/gmd-10-1091-2017</a>.</p> <p>Close, S., Penduff, T., Speich, S. and Molines J.-M., 2020. A means of estimating the intrinsic and atmospherically-forced contributions to sea surface height variability applied to altimetric observations. Progr. Oceanogr. <a href="https://doi.org/10.1016/j.pocean.2020.102314">doi: 10.1016/j.pocean.2020.102314</a></p> <p>Penduff, T., Barnier, B. , Terray, L., Bessières, L., Sérazin, G., Grégorio, S., Brankart, J., Moine, M., Molines, J., Brasseur, P., 2014. Ensembles of eddying ocean simulations for climate, CLIVAR Exchanges, Special Issue on High Resolution Ocean Climate Modelling, 19.</p>
ICESat-2 sea ice ancillary data - Mean Sea Surface Height Grids
<p>File format: NetCDF</p> <p>Mean Sea Surface (MSS) Height data grids used for the production of ICESat-2 sea ice data products (ATL07, ATL10, ATL20, ATL21). Blended data from CryoSat-2 and DTU13.</p>
Airborne radar observation dataset of sea surface height on 8 December 2016
<p>This dataset contains the results of time-series sea surface height (SSH) observation data of flight No.1, 2, 3, 4, 7, and 8 on 8 December 2016 by airborne altimeter measurement using a frequency modulated continuous wave (FM-CW) radar. The observation flight were carried out south of Japan passed over the Kuroshio Current. The data files are written in CSV format, the columns are UTC date, time, latitude, longitude, flight altitude, observed SSH, 1 min moving averaged SSH values, geoid height, and tide height. The geoid height and the tide height are derived by the EGM 2008 model (Pavlis et al. 2012) and the Nao.99Jb model (Matsumoto et al. 2000), respectively. The original data sampling rate of 800 microseconds is resampled by 80 milliseconds in each file. The data comes from a paper under review for Geophysical Research Letter.</p>
Sea Surface Height (SSH) maps for the California Current System, Jan-May 2018
<p>This is a dataset in netCDF format comprising daily sea surface height maps in the California Current system during between Jan-May 2018, produced by AVISO and 2DVAR, described and analyzed in the research paper:</p> <p>Archer, M., Li, Z., & Fu, L.‐L. (2020). Increasing the space‐time resolution of mapped sea surface height from altimetry. <em>Journal of Geophysical Research: Oceans</em>, 125, e2019JC015878. <a href="https://doi.org/10.1029/2019JC015878">https://doi.org/10.1029/2019JC015878</a></p> <p>Please see metadata and/or paper for more details. </p>
Turkish Straits System - Sea Surface Height
<p>Sea surface height daily mean estimates from a six-year simulation of Turkish Straits System (TSS) using high-resolution unstructured triangular mesh ocean model FESOM between 2008-2013. Other variables are provided separately.</p> <p>The mesh files are appended to the dataset for processing purposes.</p> <p>Aydogdu, A., Pinardi, N., Ozsoy, E., Danabasoglu, G., Gurses, O., and Karspeck, A.: Circulation of the Turkish Straits System under interannual atmospheric forcing, Ocean Sci., 14, 999-1019, doi:10.5194/os-14-999-2018, 2018.'</p>
Five day averaged sea surface height from MITgcm integration with climatological river discharge forcing
<p>The 5-day averaged sea surface height for experiment using climate river discharge, with file names like ssh_dayflux_YEAR.mat for each year.</p>
Five day averaged sea surface height from MITgcm integration with daily river discharge forcing
<p>The 5-day averaged sea surface height (SSH) for experiment using daily discharge, with file names like ssh_dayflux_YEAR.mat for each year</p>
A Deep Learning Approach to Extract Balanced Motions from Sea Surface Height Snapshot
<p>Related dataset for the paper "A Deep Learning Approach to Extract Balanced Motions from Sea Surface Height Snapshot" submitted to GRL</p>
The Seasonal Variability in the Semidiurnal Internal Tide; A Comparison between Sea Surface Height and Energetics
<p>This dataset contains data from global HYCOM simulations with realistic atmospheric and tidal forcings. The horizontal resolution is 8 km. Data is stored as netcdf4 classic.</p>
Data of publication "A simple diagnostic based on sea surface height with application to Central Pacific ENSO"
<p>Data used for the analysis presented in the publication "A simple diagnostic based on sea surface height with application to Central Pacific ENSO" by Lai et al.</p>
Surface feature height, spacing and form drag coefficients over Arctic sea ice from Operation IceBridge, 2009-2015
<p>Data used to estimate the neutral form drag coefficient over Arctic sea ice using high-resolution IceBridge laser (ATM) data, from Petty et al., (2017). The data here include the raw 1D linear profiling (along the edge of the ATM swath) data of surface feature data: e.g. 1km_xyres2m_20cm/1D/[year]/ and also 10 km mean along-track surface feature and form drag estimates: 1km_xyres2m_20cm/1D/ATMO/.</p> <p>The code to generate these data can be found here: https://github.com/akpetty/ibdrag2017. The Python pickle files have been converted to CSV here to aid data ingestion. 2D surface feature data using the same input data can be found here: https://zenodo.org/record/6617715</p> <p>The following raw IceBridge datasets are used to create these data:</p> <ul> <li>L1B ATM data: <a href="https://nsidc.org/data/docs/daac/icebridge/ilatm1b/">https://nsidc.org/data/docs/daac/icebridge/ilatm1b/</a>.</li> <li>L1B DMS imagery: <a href="http://nsidc.org/data/iodms1b%7D">http://nsidc.org/data/iodms1b}</a>.</li> <li>IceBridge IDCSI4 and quick-look sea ice thickness data: <a href="http://nsidcorg/data/docs/daac/icebridge/evaluation_products/sea">http://nsidcorg/data/docs/daac/icebridge/evaluation_products/sea</a>-ice-freeboard-snowdepth-thickness-quicklook-index.html and <a href="http://nsidc.org/data/idcsi4.html">http://nsidc.org/data/idcsi4.html</a>.</li> </ul> <p><strong>References</strong></p> <p>Petty, A. A., M. C. Tsamados, N. T. Kurtz, S. L. Farrell, T. Newman, J. P. Harbeck, D. L. Feltham, and J. A. Richter-Menge (2016), Characterizing Arctic sea ice topography using high-resolution IceBridge data, The Cryosphere, 10(3), 1161–1179, doi:10.5194/tc-10-1161-2016.</p> <p>Petty, A. A., M. C. Tsamados, N. T. Kurtz (2017), Atmospheric form drag over Arctic sea ice using remotely sensed ice topography observations, J. Geophys. Res. Earth Surf., 122, doi:10.1002/2017JF004209.</p>
Modeled wintertime sea ice drift, sea ice thickness, dynamic sea surface height and sea ice drift budget terms in the Arctic
<p>Modeled wintertime sea ice drift, sea ice thickness, dynamic sea surface height and sea ice drift budget terms in the Arctic between 1981 and 2020.</p>
Internal tides vertical structure and steric sea surface height signature south of New Caledonia revealed by glider observations
<p>Data to reproduce the figures of the preprint <em>Internal tides vertical structure and steric sea surface height signature south of New Caledonia revealed by glider observations</em>, submitted to <em>Ocean Science</em></p>
sea surface slope and significant wave height
<p>This zip file contains the band-pass filtered monthly sea surface slope and the monthly significant wave height from multi-mission satellite altimetry missions.</p>
Wave heights obtained from sea surface and bottom measurements
<p>Generally, the pressure response function of the most commonly used linear wave theory may not perfectly recover surface elevation signals. To solve this, field measurements were carried out to collect wave parameters at five observation stations, characterized by shallow to deep water environments. The data was uploaded to explain and solve this problem.</p>
ECCO Sea Surface Height - Monthly Mean 0.5 Degree (Version 4 Release 4b)
This dataset contains monthly-averaged dynamic sea surface height interpolated to a regular 0.5-degree grid from the ECCO Version 4b revision 4 (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
ECCO Sea Surface Height - Daily Mean 0.5 Degree (Version 4 Release 4b)
This dataset contains daily-averaged dynamic sea surface height interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
ECCO Sea Surface Height - Daily Mean llc90 Grid (Version 4 Release 4b)
This dataset provides daily-averaged dynamic sea surface height and model sea level anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
ECCO Sea Surface Height - Daily Mean 0.5 Degree (Version 4 Release 4)
This dataset contains daily-averaged dynamic sea surface height interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
ECCO Sea Surface Height - Snapshot llc90 Grid (Version 4 Release 4)
This dataset provides instantaneous dynamic sea surface height and model sea level anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include dynamic sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; dynamic sea surface temperature (SST) from satellite radiometers [AVHRR], dynamic sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
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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)
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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.