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81 results for “Altimetry”
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>
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>
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>
Data for paper "An adaptive nonlinear iterative method for predicting seafloor topography from altimetry-derived gravity data"
<p>LM is the linear inversion seafloor topography model</p> <p>NLM is the nonlinear inversion seafloor topography model</p> <p>PM is the prior seafloor topography model</p>
Dataset (CryoSat-2 altimetry data over Brahmaputra River, river masks, model cross sections) used in Schneider et al., 2017. doi:10.5194/hess-2016-243
<p>Dataset used in</p> <p>Schneider, R., Nygaard Godiksen, P., Villadsen, H., Madsen, H., Bauer-Gottwein, P., 2017. Application of CryoSat-2 altimetry data for river analysis and modelling. Hydrol. Earth Syst. Sci.rticle. doi:10.5194/hess-2016-243</p> <p>The dataset contains</p> <ul> <li>CryoSat-2 satellite altimetry data over the Brahmaputra River from 2010 to 2013</li> <li>River masks, derived from Landsat NDVI imagery, used to filter the CryoSat-2 data</li> <li>Results from the cross section calibration described in Schneider et al., 2017</li> </ul> <p>All data is provided as a .zip file which includes a README.txt with more details on the data.</p> <p> </p>
A monthly 5 km Arctic sea ice thickness product from 1995 to 2023 using multiple radar altimetry data
<p>Arctic sea ice is of great importance to the regional and global climate change study. Satellite observations have demonstrated that the Arctic sea ice extent has been declined for the last four decades. However, long-term variations of the Arctic sea ice thickness (SIT) are less focused as SIT cannot be measured directly by satellite-based instruments. Here, we presented a monthly Arctic SIT product based on multiple radar altimetry observations from ERS-2, Envisat, and CryoSat-2. To ensure the accuracy of the SIT retrievals, we proposed a novel data processing procedure including leads detection, freeboard conversion to thickness and inter-mission bias correction. Finally, we were able to generate the monthly SIT estimates for the Arctic Ocean from October 1995 to December 2023. The thickness estimates are posted on a 5 km resolution polar stereographic grid. The variations of the Arctic SIT are analyzed in terms of the spatial and temporal distributions. We also compared our SIT estimates with observations from upward looking sonars (ULSs) and airborne laser altimetry from Operation IceBridge (OIB), as well as seven publicly released Arctic SIT products. The validation results demonstrate that our SIT product features equivalent accuracy with existing products. The accuracy of our products is about 0.4 m during Envisat period, and is within 0.2 m during CryoSat-2 period.</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>
Data for Directional Surface Wave Spectra And Sea Ice Structure from ICEsat-2 Altimetry
<p>This is data used for <em>Directional Surface Wave Spectra And Sea Ice Structure from ICEsat-2 Altimetry</em> in the Cryosphere.</p> <p>The code that reproduces this data can be found at</p> <pre>10.5281/zenodo.6908645</pre> <p>See README.md for further instructions.</p> <p> </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).
Multi-frequency altimetry snow depth product over Arctic sea ice
<p>Satellite altimetry can be used to estimate sea ice thickness, an essential variable to better understand and forecast the dynamic ice cover. Nevertheless, some sources of uncertainty remain, and one of the most important concerns the snow depth, a key parameter to convert the measured ice freeboard into sea ice thickness.</p> <p>Snow depth can be estimated using different altimeter frequencies with different snow penetration capabilities. We have developed a monthly snow depth product based on the differences between CryoSat-2 SAR Ku and IceSat-2 laser altimeters covering the period 2018-2021 with a spatial resolution of 25 km. </p> <p> </p>
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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
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International Brain Laboratory public data
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OpenNeuro
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