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Rating curves based on satellite altimetry and in-situ discharge data
<h1>Context: </h1> <p>The ESA river discharge Climate Change Initiative (CCI) project is a precursor study. It aims to derive long term climate data records (at least over 20-years) of river discharge for some selected river basins (and some locations in the river network) using satellite remote sensing observations (altimetry and multispectral images) and ancillary data. It aims to provide a proof-of-concept for the feasibility for a potential River Discharge ECV product to meet the requirements for the <a href="https://gcos.wmo.int/en/essential-climate-variables/rivers/" target="_blank" rel="noopener">Global Climate Observing System</a>. This project covers precursor activities towards the production of data products that address the GCOS-defined requirements for the River Discharge ECV.</p> <h1>Data description :</h1> <p>Just as in-situ stage measurements can be used to gauge river discharge, altimetry-derived water surface elevation (WSE) can serve as an alternative means of estimating river discharge when discharge time series data is available. Several methodologies have been documented for deriving discharge time series from multimission altimetry observations and supplementary data (Biancamaria et al., 2024). At least two approaches will be used, depending on the available in situ discharge and altimetry water surface elevation (WSE) time series:</p> <p>⋅ <strong><em>Method 1</em>: </strong>The preferred approach relies on the altimetry water surface elevation time series and in situ discharge time series to create a rating curve (RC) characterized by a power relationship between these two variables following a Bayesian approach (Rantz et al., 1982). However, this method necessitates a significant overlap period between discharge data and radar altimetry measurements (e.g., Biancamaria et al., 2011; Papa et al., 2012), or it requires the assumption that the rating curve remains valid and consistent when discharge data is only available prior to the altimetry observation period.</p> <p>⋅ <em><strong>Method 2:</strong></em> The final option, in cases where there is no temporal overlap between in-situ or simulated discharge and water surface elevation data, assumes that the validity and stability of the rating curve persist across the various time periods covered by the two datasets. Both of these time periods should be sufficiently long to encompass a wide range of events. With this assumption, Tourian et al. (2013, 2017) introduced a method for calculating the rating curve, not based on the time series of discharge and water surface elevation, but on the distribution of their quantiles. This method has been adopted by a limited number of recent studies (e.g., Belloni et al., 2021). However, it’s important to note that this methodology naturally introduces higher errors when compared to the preferred approach. For this reason, this methodology will be validated over some stations with various hydrological dynamics and satisfying previous methods (overlap period exists between WSE and Q).</p> <h1>Approaches to derive Rating Curve (RC) :</h1> <h2>Bayesian Approach :</h2> <p>The Bayesian method is a robust statistical approach used for constructing a rating curve, frequently applied in the field of hydrology when the goal is to estimate unknown parameters from observed data, while taking into consideration the associated uncertainty in these estimates. </p> <p>According to this, the estimation of the rating curve using the Bayesian method involves several steps:</p> <ul> <li>The initial step entails defining a probabilistic model that describes the relationship between observed data and the parameters we aim to estimate. In many hydrological applications, the relationship between discharge data (Q) and water surface elevation data (WSE) is often expressed as a power function:</li> </ul> <p><em> Q = a⋅(WSE-z</em><em>0</em><em>)</em><sup><em>b</em></sup></p> <p>Here, <em>a, z0</em> and <em>b</em> are the parameters of the rating curve. <em>a,</em> is a scaling coefficient governing the magnitude of the Q-WSE relationship, <em>b,</em> characterizes the nature of this relationship, and <em>z0</em>, represents the height of the free surface above the reference point, corresponding to the river bottom's altitude. The power relationship is especially pertinent due to its consistency with numerous hydrodynamic phenomena. The exponent b within the equation allows for the representation of distinctive flow characteristics, including factors like roughness and channel geometry. Moreover, it offers adaptability in modelling to accommodate variations in flow characteristics, whether they are turbulent or laminar. This relationship, despite its mathematical simplicity, facilitates the fine-tuning of model adjustments in accordance with observed data (Chow, 1959).</p> <ul> <li>The second step involves the use of prior normal distributions, reflecting our prior knowledge about these parameters. These distributions can either be informative or uninformative, depending on our level of knowledge. The limits and ranges for a, z0 and b can vary depending on the specific context of the study, the dataset used, and the characteristics of the river or channel being analysed.</li> </ul> <p><u>- Coefficient “a”</u>: adjustment parameter for the rating curve representing the scaling factor for discharge. Its value can significantly fluctuate based on various factors such as the characteristics of the river or channel, hydraulic conditions, and other influencing factors. Consequently, "a" must be non-negative and constrained within a sensible range specific to the system under study. Following the Manning equation, “a” must be equal to W/n*S<sup>1/2</sup> (Chow et al., 1988) where W is the river’s width (m), n the Manning’s roughness coefficient and S the slope (m/m). Given the considerable variability in river width and slope across different stations, a feasible range for this coefficient can be considered as:</p> <p> a ∈ [0; 3000]</p> <p><u>- Coefficient “b”</u>: adjustment parameter representing the exponent of the rating curve and indicating the hydraulic condition of the study site. Like "a," this value must comply with physical constraints and cannot be negative. Following the Manning equation, “b” must be equal to 5/3 for reference hydraulic condition (Rantz et al., 1982). To accommodate the variability in system characteristics across sites, the following range values can be considered for this coefficient:</p> <p> b ∈ [0; 5]</p> <p><u>- Coefficient “z0”</u>: offset or the elevation at which discharge begins. It should be within the range of elevations relevant to your study. For this <em>reason, the value</em> cannot exceed the minimum value of water surface elevation (WSE) and the range value need to consider of the variability in term of water depth over the sites. A feasible range for this coefficient can be considered as:</p> <p> z0 ∈ [min(WSE)-30; min(WSE)]</p> <ul> <li>The final step involves parameter estimation. The posterior distribution of the parameters yields probabilistic estimates of the rating curve parameters in the form of mean values (optimal values) and credibility intervals (95th percentiles). This accounts for the uncertainty associated with these parameters and is achieved through Markov Chain Monte Carlo (MCMC) sampling from the posterior distribution. Two commonly employed MCMC algorithms are "NUTS" (No-U-Turn Sampler) and "Metropolis-Hastings." The Metropolis-Hasting sampler "MH" algorithm, which is relatively simple and efficient where a balance between exploration and exploitation is desired. This algorithm can be adapted to sample from discrete state spaces.</li> </ul> <h2>Quantile approach : </h2> <p>The Quantile approach employs statistical modelling using quantile functions to create a rating curve, eliminating the necessity for overlapping measurements. This algorithmic method enables the estimation of river discharge using satellite altimetry, even in instances where there are no in situ measurements within the altimeter's timeframe. This approach has undergone application and validation in diverse river basins spanning different climatic zones, such as the Amazon, Brahmaputra, Danube, Niger, and Ob (Tourian et al., 2013).</p> <p>Assuming a stationary flow behaviour and no modification in the river bathymetry both at the altimetry virtual station and at the in-situ gage, this approach ensures the utilization of historical in situ data in current applications. This method computes the quantile functions of the altimetry water surface elevation on one hand and of the discharge time series on the other hand. Then a scatter plot of these in-situ discharge quantiles versus altimetry water surface elevation quantiles is computed to establish the rating curve using the bayesian approach described previously.</p> <h1>File description :</h1> <table> <tbody> <tr> <td><strong>Column name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>basin-station</td> <td>Basin name in capital letters and Station name in capital letters separated by "_" and where spaces have been replaced by "-".</td> </tr> <tr> <td>lon</td> <td>Longitude in decimal degrees [-180,180] with 4 decimals - corresponding to the insitu discharge station.</td> </tr> <tr> <td>lat</td> <td>Latitude in decimal degrees [-90,90] with 4 decimals – corresponding to the insitu discharge station.</td> </tr> <tr> <td>a</td> <td>Adjustment parameter for the rating curve representing the scaling factor for discharge. Number with 3 decimals.</td> </tr> <tr> <td>b</td> <td>Adjustment parameter representing the exponent of the RC and indicating the hydraulic condition of the study site. Number with 3 decimals.</td> </tr> <tr> <td>z0</td> <td>Offset of the elevation at which discharge begins. Number with 3 decimals.</td> </tr> <tr> <td>a_sd</td> <td>Standard deviation of the coefficient "a". Number with 3 decimals.</td> </tr> <tr> <td>b_sd</td> <td>Standard deviation of the coefficient "b". Number with 3 decimals.</td> </tr> <tr> <td>z0_sd</td> <td>Standard deviation of the coefficient "z0". Number with 3 decimals.</td> </tr> <tr> <td>period</td> <td>Period used to compute the rating curve under the format %Y-%m-%d where the start and the end dates are separated by ":"</td> </tr> <tr> <td>nb</td> <td>Number of overlap dates to compute the rating curve.</td> </tr> <tr> <td>Methodology</td> <td>Methodology used to compute the rating curve. The first part describes the approach used to compute the RC and the second part, separated by “_”, describes the algorithm used. To avoid any issue for the reader the spaces have been replaced by “-”. At the end 2 approaches has been used: “Overlap-approach” or “Quantile-approach” and 2 algorithms: “Bayesian-algorithm” or “Multiple-algorithms” designed for Arctic rivers experiencing frozen periods. </td> </tr> <tr> <td>Source</td> <td>In-situ data sources to compute the rating curve. If multiple sources has been used, the sources are separate by "/"</td> </tr> </tbody> </table> <p>---------</p> <p><em>THE DATASET IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR </em><em>IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</em><br><em>FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE </em><em>AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER </em><em>LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, </em><em>OUT OF OR IN CONNECTION WITH THE DATASET OR THE USE OR OTHER DEALINGS IN THE </em><em>DATASET.</em></p>
Time series of Agulhas leakage from satellite altimetry. Update of Le Bars et al. 2014.
<p>Agulhas leakage time series updated from Le Bars et al. 2014 and Kelly et al. 2016.</p> <p>Final results are stored in:</p> <p>Trp_track_twb122_barot30W.nc,Trp_track_twb20_barot30W.nc and Trp_track_twb198_barot30W.nc</p> <p>An example of analysis and comparison with older time series is in the Python 3.9 Jupyter notebook:</p> <p>AnalyseOutputs.ipynb (AnalyseOutputs.html to simply visualise)</p> <p>The code and intermediate results are also provided. See README.txt for instructions on how to use it. It was written in 2013 with the NCAR Command Language and Python 2.? but it can still run now with later version of NCL and with Python 2.7.</p> <p>References:</p> <p>Le Bars, D., Durgadoo, J. V., Dijkstra, H. A., Biastoch, A., and De Ruijter, W. P. M.: An observed 20-year time series of Agulhas leakage, Ocean Sci., 10, 601–609, https://doi.org/10.5194/os-10-601-2014, 2014.</p> <p>Kelly, K. A., K. Drushka, L. Thompson, D. Le Bars, and E. L. McDonagh (2016), Impact of slowdown of Atlantic overturning circulation on heat and freshwater transports, Geophys. Res. Lett., 43, doi:10.1002/2016GL069789.</p>
A Synthetic Global Spatiotemporal Sampled River Discharge Database for Different Satellite Altimetry Mission Orbits
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all the RRR input and output files that were used in the study reported in:</p> <ul> <li> <p>Sikder, Md. S., Bonnema, M., Emery, C. M., David, C. H., Lin, P., Pan, M., et al. (2021). A Synthetic Data Set Inspired by Satellite Altimetry and Impacts of Sampling on Global Spaceborne Discharge Characterization. <em>Water Resources Research</em>, <em>57</em>(2), e2020WR029035. <a href="https://doi.org/10.1029/2020WR029035">https://doi.org/10.1029/2020WR029035</a></p> </li> </ul> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. </p> <p>Note that this dataset makes extensive use of the river network and RAPID simulations that were produced in the following study, and the paper is gratefully acknowledged here:</p> <ul> <li> <p>Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., et al. (2019). Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches. <em>Water Resources Research</em>, <em>55</em>(8), 6499–6516. <a href="https://doi.org/10.1029/2019WR025287">https://doi.org/10.1029/2019WR025287</a></p> </li> </ul> <p><strong>Version of record and details of this version</strong></p> <p>The version of record for this dataset (i.e. the one used in the aforementioned paper) is version V1.1 available at <a href="https://doi.org/10.5281/zenodo.4064188">https://doi.org/10.5281/zenodo.4064188</a>. This version V2.1 was produced to facilitate testing of the RRR software (<a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a>). Notable details regarding this version compared to V2.0 are as follows:</p> <ul> <li>The temporal sequence files (seq_TIM*.csv) of observations for regular temporal sampling now all have a sampling mean time of 0 second for every river reach instead of the previous value which corresponded to the cycle of observations (e.g. 259,200 seconds for a three-day regular temporal sampling). This allows to start sampling at the onset of each simulation instead of at the end of the first cycle. This change does impact the findings of the study.</li> <li>The sampled discharge files (Qout*.nc) where produced with an updated version of rrr_anl_spl_mod.py which now selects the time step at which a sample is retained using a slightly different approach. The update only impacts sampling results when the sampling time matches the river model output time step exactly, and is more accurate now. This change does impact the findings of the study.</li> </ul>
A global Lagrangian eddy dataset based on satellite altimetry (GLED v1.0)
<p>Mesoscale eddies, defined as rotating structures ranging typically from tens to hundreds of kilometers and lasting for several weeks to months, are ubiquitous in the global ocean. Isolated mesoscale eddies are generally considered as coherent structures with a material barrier that can trap the fluid within the eddy interior. Methods employed to identify coherent eddies can be classified into Eulerian and Lagrangian frameworks. Eddy datasets based on Eulerian methods, especially the eddy census of Chelton et al. (2011), have been used in a huge range of applications, from physics to biology. However, recent works have shown that Eulerian eddies are not necessarily coherent because there is strong and persistent water exchange across the Eulerian eddy boundary. In this study, millions of Lagrangian particles are advected by satellite-derived surface geostrophic velocities over a period of 1993-2019. Using the method of Lagrangian-averaged vorticity deviation by Haller et al. (2016), we present a global Lagrangian eddies dataset (GLED v1.0). This open-source dataset contains not only general features (eddy center position, equivalent radius, rotation property, etc.) of eddies with lifespans of 30, 90, and 180 days, but also the trajectory of particles trapped by coherent eddy boundaries over the lifetime. The greatest strength of GLED v1.0 is that the identified eddies are all material objects by construction. Our eddy dataset provides an additional option for oceanographers in studying the interaction between coherent eddies and other physical or biochemical processes in the Earth system.</p>
SDUST2020 MSS: A global 1′×1′ mean sea surface model determined from multi-satellite altimetry data
<p>SDUST2020 MSS (Shandong University of Science and Technology 2020 mean sea surface) model with a grid of 1′×1′ is established with 19-year moving average method from multi-satellite altimetry data over 27-year (from January 1993 to December 2019). Its spatial coverage is 80°S-84°N. The missions data of Topex/Poseidon, Jason-1, Jason-2, Jason-3, ERS-1, ERS-2, GFO, Envisat, SARAL, HY-2A, Sentinel-3A and Cryosat-2 are ingested in the SDUST2020 MSS model.</p>
Figure 3 in Validation of Wind Speed Calculated on Satellite Altimetry Data by Measurements on Weather Stations Located Along the White Sea Coast
Figure 3. The dependence of the speed wind at a height Figure 4. In-situ MS data breakdown scheme for a
Figure 4 in Interannual Variability of Water Exchange Anomalies Between the Northern, Middle and Southern Caspian Based on Satellite Altimetry Data
Figure 4. Temporal variability of anomalies of surface geostrophic velocities (m/s) directed normal to 133 (a) and 209 (b) tracks. Positive values correspond to the southeast direction of currents, negative values correspond to the northwest direction.
Figure 5 in Satellite Altimetry of Sea Level and Ice Cover in the Barents Sea
Figure 5. Location of 34 descending tracks of satellites ERS-1 (phases C and G), ERS-2 and ENVISAT, which were used to analyze the position of the sea ice edge in the Barents Sea. The red line is the average climatic position of the sea ice edge. The green line shows the position of track N444, the purple line shows the track N360. Dashed line is a reference line used for calculation the distance to the ice edge along the tracks.
Figure 6 in Satellite Altimetry of Sea Level and Ice Cover in the Barents Sea
Figure 6. Interannual variability of the position of the ice edge along track N118 according to altimetry measurements of the ERS − 1/2, ENVISAT and SARAL/AltiKA satellites in 1992-2018. Dashed line shows a linear trend for the variability of the distance to the sea ice edge in the Barents Sea.
Figure 4 in Satellite Altimetry of Sea Level and Ice Cover in the Barents Sea
Figure 4. Interannual variability of the Barents Sea level anomalies according to satellite altimetry measurements of the ERS − 1/2, ENVISAT and SARAL/AltiKa for the period 1992–2018 for June, July, August and September. Dashed lines show linear trends.
Figure 2 in Satellite Altimetry of Sea Level and Ice Cover in the Barents Sea
Figure 2. The position of the tracks of the ERS − 1/2, ENVISAT and SARAL/AltiKA (a) satellites with a repetition period of 35 days and the Sentinel – 3A / 3B satellites (b) with a repetition period of 27 days in the Barents Sea.
Figure 3 in Satellite Altimetry of Sea Level and Ice Cover in the Barents Sea
Figure 3. Interannual variability of the Barents Sea level anomalies according to satellite altimetry measurements of the ERS − 1/2, ENVISAT and SARAL/AltiKa for the period 1992 –2018. Dashed line shows a linear trend.
Figure 1 in Interannual Variability of Water Exchange Anomalies Between the Northern, Middle and Southern Caspian Based on Satellite Altimetry Data
Figure 1. The Caspian Sea. Main parts of the Caspian Sea: (1) – the Northern Caspian (2) - the Middle Caspian; (3) – the Southern Caspian; (4) – the Kara-Bogaz-Gol Bay. Isobaths are shown in meters. The coastline corresponds to year 1934, when the sea level was -26.46 m relative to the World Ocean level (Lebedev, 2018).
Figure 5 in Validation of Wind Speed Calculated on Satellite Altimetry Data by Measurements on Weather Stations Located Along the White Sea Coast
Figure 5. The dependence of the correlation coefficient between in-situ wind speed at the WS and remote sensing data on the orientation angle of the main quadrants (a) and their position relative to the White Sea coastline (b).
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>
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.
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>
Greenland mass trends from airborne and satellite altimetry during 2011–2020
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