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81 results for “Altimetry”
Sentinel-3 Altimetry satellite imagery for Inland Water Altimetry Monitoring
<p>Sentinel-3 Altimetry satellite imagery for Inland Water Altimetry Monitoring</p>
High-Resolution Water Surface Slopes from Multi-Mission Satellite Altimetry
<p><strong>1. Summary</strong>:</p> <p>This dataset contains water surface slopes (WSS) every kilometer along 11 Polish rivers derived from cross-calibrated multi-mission satellite altimetry (<em>Schwatke et al. 2023a</em> (in review). ). The approach to derive WSS is based on a weighted least-squares approach, which is described in detail in <em>Schwatke et al. 2023b</em> (in review).</p> <p><strong>2. Data Formats</strong>:</p> <p>This dataset is provided in netCDF and shapefile formats. Each netCDF file contains the data of a single river and parameters such as river chainage, WSS, WSS error, location, and nearest centerline information from the SWORD database (v1.1, <em>Altenau et al., 2021</em>). The shapefile consists of five files (.cpg, .dbf, .prj, .shp, .shx) containing the data of the 11 Polish rivers. The attributes are identical to the netCDF, but the river name has been added.</p> <p><strong>3. Attribute Description</strong>:</p> <p>The attributes of netCDFs and shapefiles are described in the following list:</p> <ul> <li> <p><strong>river_chainage</strong>: The <em>river chainage</em> describes the distance from the river mouth to the location of each bin along the river (units: km)</p> </li> <li> <p><strong>wss</strong>: Water surface slopes (WSS) at each bin along the river. WSS are set to NaN/NULL for unprocessed lakes/reservoirs or short river segments (units: mm/km).</p> </li> <li> <p><strong>wss_error</strong>: Errors of WSS at each bin along the river. WSS errors are set to NaN/NULL for unprocessed lakes/reservoirs or short river segments (units: mm/km).</p> </li> <li> <p><strong>longitude</strong>: Longitude of the 1 km bins along the river (units: degree).</p> </li> <li> <p><strong>latitude</strong>: Latitude of the 1 km bins along the river (units: degree).</p> </li> <li> <p><strong>centerline_id</strong>: Nearest <em>centerline id </em>extracted from the SWORD database (v1.1, <em>Altenau et al., 2021</em>).</p> </li> <li> <p><strong>node_id</strong>: <em>Node id</em> from the SWORD database (v1.1, <em>Altenau et al., 2021</em>) for the corresponding <em>centerline id</em>.</p> </li> <li> <p><strong>reach_id</strong>: <em>Reach id</em> from the SWORD database (v1.1,<em> Altenau et al., 2021</em>) for the corresponding <em>centerline id</em>.</p> </li> <li> <p><strong>river_name</strong>: The name of the river is only available in the Shapefile.</p> </li> </ul> <p><strong>4. References</strong>:</p> <p><em>Schwatke C., Dettmering D., Passaro M., Hart-Davis M., Scherer D., Müller F. L., Bosch W., Seitz F.: </em><strong>OpenADB: DGFI-TUM`s Open Altimeter Database</strong>. Geoscience Data Journal, 2023a (in Review)</p> <p><em>Schwatke C., Halicki M., Scherer D</em>.: <strong>Generation of high-resolution water surface slopes from multi-mission satellite altimetry</strong>. Water Resources Research, 2023b (in Review)</p> <p><em>Altenau E.H., Pavelsky T.M., Durand M.T., Yang X., Frasson R.P.d.M., Bendezu L.</em>: <strong>SWOT River Database (SWORD) (Version v1)</strong> [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.4917236">https://doi.org/10.5281/zenodo.4917236</a>, 2021</p>
10-year glacier mass changes from CryoSat-2 radar altimetry
<p>We generate a record of ice loss across glaciers from CryoSat-2 swath interferometric radar altimetry. We resolve changes at 500m resolution for Arctic Canada North and South, Greenland periphery glaciers, Iceland, Svalbard, Franz Josef Land, Novaya Zemlya, Severnaya Zemlya, Southern Andes and Antarctic periphery glaciers between 2010 and 2020.</p>
Data from: Cyclone-anticyclone asymmetry of eddy detection on gridded altimetry product in the Mediterranean Sea
<p>We perform an Observing System Simulation Experiment that simulates the satellite sampling and the mapping procedure on the sea surface of the high-resolution model CROCO-MED60v40, to investigate the reliability and the accuracy of the eddy detection. The main result of this study is a strong cyclone-anticyclone asymmetry of the eddy detection on the altimetry products AVISO/CMEMS in the Mediterranean Sea. Large-scale cyclones having a characteristic radius larger than the local deformation radius are much less reliable than large-scale anticyclones. We estimate that less than 60% of these cyclones detected on gridded altimetry product are reliable, while more than 85% of mesoscale anticyclones are reliable. Besides, both the barycenter and the size of these mesoscale anticyclones are relatively accurate. This asymmetry comes from the difference of stability between cyclonic and anticyclonic eddies. Large mesoscale cyclones often split into smaller sub-mesoscale structures having a rapid dynamical evolution. The numerical model CROCO-MED60v40 shows that this complex dynamic is too fast and too small to be accurately captured by the gridded altimetry products. The spatio-temporal interpolation smoothes out this sub-mesoscale dynamics and tends to generate an excessive number of unrealistic mesoscale cyclones in comparison with the reference field. On the other hand, large mesoscale anticyclones, which are more robust and which evolve more slowly, can be accurately tracked by standard altimetry products. We also confirm that the AVISO/CMEMS products induce a bias on the eddy intensity. The azimuthal geostrophic velocities are always underestimated for large mesoscale anticyclones.</p>
Greenland mass trends from airborne and satellite altimetry during 2011–2020
<p><span>We use satellite and airborne altimetry to estimate </span><span>annual</span><span> mass changes of the Greenland Ice Sheet. We estimate ice loss corresponding to a sea-level rise of 6.9±0.4 millimeters from April 2011 to April 2020, with the highest annual </span><span>ice loss rate of 1.4 mm/yr </span><span>sea-level equivalent</span><span> from </span><span>April 2019 to April 2020</span><span>. On a regional scale, our annual mass loss timeseries reveals 10-15 m/yr dynamic thickening at the terminus of Jakobshavn Isbræ from April 2016 to April 2018, followed by a return to dynamic </span><span>thinning. </span><span>We observe contrasting patterns of mass loss acceleration in different basins across the ice sheet. Our gridded satellite altimetry data and surface mass balance (SMB), along with corrections due to firn compaction are available for download. Here, we provide:</span></p> <p><span>(1) Annual (April to April) elevation change rates of the Greenland Ice Sheet from April 2011 to April 2020 from CryoSat-2, ICESat-2 and NASA's ATM flights. 1x1 km grid.</span></p> <p><span>(2) Annual (April to April) elevation change rates due to SMB anomalies. 1x1 km grid.</span></p> <p><span>(3) Ice-sheet wide annual corrections due to firn compaction.</span></p>
Figure 1 in Satellite Altimetry of Sea Level and Ice Cover in the Barents Sea
Figure 1. Map of the Barents Sea. The red dashed line shows the boundaries of the Barents Sea.
Dataset used in Kittel et al., 2017 (https://doi.org/10.5194/hess-2017-549), including model files and processed remote sensing observations (CryoSat-2 radar altimetry and GRACE total water storage)
<p>Dataset used in</p> <p>Kittel, C. M. M., Nielsen, K., Tøttrup, C., Bauer-Gottwein, P., 2017.Informing a hydrological model of the Ogooué with multi-mission remote sensing data. Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-549</p> <p>The dataset contains</p> <p>Model files:</p> <ul> <li>River delineation of the Ogooué river based on the SRTM 3 arc-second DEM </li> <li>Climate input data for the Ogooué model subbasins (TRMM and FEWS-RFE precipitation and ECMWF temperature)</li> <li>Parameter files</li> </ul> <p>Processed remote sensing data:</p> <ul> <li>CryoSat-2 satellite altimetry data over the Ogooué River from July 2010 to February 2015</li> <li><strong> </strong>Water mask derived from Sentinel-1 SAR, used to filter CryoSat-2 data</li> <li>GRACE TWS time series for the Ogooué basin</li> </ul> <p>The data is provided in a .zip file with a README.txt file providing additional information and details on the data, including where to obtain similar/original datasets.</p> <p>(c) Author(s) and Technical University of Denmark (DTU) 2018</p>
DGFI-TUM DSO1 orbits of altimetry satellites TOPEX/Poseidon, Jason-1, Jason-2 and Jason-3 derived from SLR data in the SLRF2014 reference frame
<p>The data set provides DSO1 orbits of altimetry satellites TOPEX/Poseidon (27 September 1992 to 9 October 2005), Jason-1 (13 January 2002 to 30 June 2013), Jason-2 (20 July 2008 to 2 October 2019) and Jason-3 (17 February 2016 to 24 October 2021) computed at the Deutsches Geodätisches Forschungsinstitut of the Technical University of Munich (DGFI-TUM). The orbits are derived using the DGFI-TUM Orbit and Geodetic parameter estimation Software (DOGS). The orbits were computed from SLR data in the SLRF2014 (an extended version of ITRF2014) reference frame using common for all satellites, most precise models and standards available and described in the following paper that serves as a citation of these orbits:</p> <p>Sergei Rudenko, Denise Dettmering, Julian Zeitlhöfler, Riva Alkahal, Dhruv Upadhyay and Mathis Bloßfeld (2023) Radial orbit errors of contemporary altimetry satellite orbits. Surveys in Geophysics, https://doi.org/10.1007/s10712-022-09758-5.</p> <p>For each satellite, a tar file is given comprising compressed files. File names are given as satgpswd.sp3.gz, where “sat” is the abbreviation of the satellite name (JA1, JA2, JA3, TPX), “gpsw” is the 4-digit GPS week, and “d” indicates the day of the GPS week containing the first time instant of the file (0 = Sunday, 6 = Saturday). The orbit files are available in the Extended Standard Product 3 Orbit Format, Version c (SP3-c).</p> <p>The orbits were derived within the project “Mitigation of the current errors in precise orbit determination of altimetry satellites (MEPODAS)” funded by Deutsche Forschungsgemeinschaft (DFG).</p>
Synthetic along-track altimetry data over 1993-2018 from a NEMO-based simulation of the IMHOTEP project
<p>"Synthetic observations" of along-track SSH have been extracted online during the production of the global, NEMO-based experiment ** IMHOTEP-GAIc**, at every single time and locations where a true SLA observation exists in the AVISO database for the along-track altimetry from the TOPEX, Jason-1, Jason-2 and Jason-3 satellite continuous series over the period 1993-2018. This global ocean/sea-ice/iceberg simulation uses the NEMO model, and has a horizontal resolution of 1/4°. The atmospheric forcing applied at the surface is based on the JRA reanalysis (Kobayashi et al., 2015) and varies over the full range of time-scales from 6 hours to multi-decadal. The freshwater runoff forcing applied to the experiment is fully-variable (daily to multi-decadal) based on the ISBA hydrographic reanalysis for rivers (Decharme et al., 2019) and from altimeter data and regional GCM simulations for the liquid and solid discharges from the Greenland ice-sheet (Mouginot et al 2019). These runoffs are only climatological around Antarctica.<br>This synthetic along-track SSH dataset from the model is available over the altimetry period (1993-2018). It is provided there along with a time-mean model SSH (gridded model field) over the same period that can be used as a proxy for mean dynamic topography ("MDT").</p><p>See the README file for more information. And online documentation is also available here: https://doc-imhotep.readthedocs.io/en/latest/6-Synthetic-Obs.html</p>
Greenland mass trends from airborne and satellite altimetry during 2011–2020
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Data from: Cyclone-anticyclone asymmetry of eddy detection on gridded altimetry product in the Mediterranean Sea
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Global Marine Gravity Gradient Tensor Inverted from Altimetry-derived Deflections of the Vertical: CUGB2023GRAD
<p>CUGB2023GRAD is a dataset consisting of all six components of Earth's gravity gradient tensor over the oceans. The gravity gradient tensor is inverted from 1 arc-minute grid of altimetry-derived north-south and east-west components of deflection of the vertical. CUGB2023GRAD has a longitudinal extent of 180° W ~180° E and a latitudinal extent of 80° S ~ 80° N.</p> <p>This version used a merge of deflections of the vertical (north_32.1.nc and east_32.1.nc) developed by Scripps Institution of Oceanography, and DTU21GRA-derived deflections of the vertical. DTU21GRA is a highly accurate gravity anomaly model developed by Technical University of Denmark. Both sets of deflections of the vertical were developed from multiple satellite altimetry observations which include: Jason-1, Jason-2, Cryosat-2, SARAL/AltiKa, and Sentinel-3A/B.</p>
The impact of Natural Variability on Regional Sea-Level Rise During the Satellite-Altimetry Era
<p>We share the datasets for each figure. Anyone can use the datasets under proper citation.</p>
Input data for manuscript "SNR-based GNSS reflectometry for coastal sea-level altimetry – Results from the first IAG inter-comparison campaign"
<p>GNSS data collected at station GTGU for one year (2015.5-2016.5) and nearby tide gauge data.</p> <p>Note: the antenna position in the header file, expressed in global Cartesian coordinates, corresponds inadvertently to integer values of latitude, longitude, and altitude (57.0°, 11.0°, 0.0 m); non-truncated values are as follows: 57.3929549°, 11.9134886°, 40.420 m.</p>
Output data for manuscript "SNR-based GNSS reflectometry for coastal sea-level altimetry – Results from the first IAG inter-comparison campaign"
<p>GNSS-R sea level time series and ancillary information about periods with incomplete GNSS data.</p>
Water level of Qinghai Lake based on multi-source satellite altimetry data
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Datasets for "Scalable interpolation of satellite altimetry data with probabilistic machine learning"
<p>Elevation (radar freeboard and sea-level anomaly) fields from CryoSat-2, Sentinel-3A, and Sentinel-3B, over the period December 1st 2018 - April 30th 2019. These data were processed for the Arctic domain using the European Space Agency's Grid Processing on Demand (GPOD) service. Processing follows the steps outlined in Lawrence et al., 2021 (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.asr.2019.10.011" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.asr.2019.10.011</a>). These data are provided at along-track, 5 km and 50 km resolution, where gridded data follow the EASE grid definition (<a href="https://doi.org/10.3390/ijgi1010032">https://doi.org/10.3390/ijgi1010032</a>).</p> <p>These data were used to develop the open-source Python programming library GPSat (https://github.com/CPOMUCL/GPSat), which uses local Gaussian Process models to perform scalable interpolation of non-stationary satellite altimetry data. The 'Source_data.xlsx' file contains the data corresponding to figures in the published Nature Communications article 'Scalable interpolation of satellite altimetry data with probabilistic machine learning'.</p>
Data from: A daily global mesoscale ocean eddy dataset from satellite altimetry
Mesoscale ocean eddies are ubiquitous coherent rotating structures of water with radial scales on the order of 100 kilometers. Eddies play a key role in the transport and mixing of momentum and tracers across the World Ocean. We present a global daily mesoscale ocean eddy dataset that contains ~45 million mesoscale features and 3.3 million eddy trajectories that persist at least two days as identified in the AVISO dataset over a period of 1993–2014. This dataset, along with the open-source eddy identification software, extract eddies with any parameters (minimum size, lifetime, etc.), to study global eddy properties and dynamics, and to empirically estimate the impact eddies have on mass or heat transport. Furthermore, our open-source software may be used to identify mesoscale features in model simulations and compare them to observed features. Finally, this dataset can be used to study the interaction between mesoscale ocean eddies and other components of the Earth System.
The Effects of Differential Tropospheric Error on the Measurement of Wide-swath Interferometric Altimetry
<p>Related dataset and codes for the paper "The Effects of Differential Tropospheric Error on the Measurement of Wide-swath Interferometric Altimetry" submitted to IEEE TGRS.</p>
Data from: A daily global mesoscale ocean eddy dataset from satellite altimetry
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