Satellite altimetry provides the possibility to observe key parts of the hydrosphere, namely the ocean, ice, and continental surface water from space. Since the launch of Topex/Poseidon in 1992 the applications of altimetry have expanded from the open oceans to coastal zones, inland water, land and sea ice. Today, seven missions are in orbit, providing dense and near-global observations of surface elevation and several other parameters. Satellite altimetry has become an integral part of the global observation of the Earth‘s system and changes therein.
In recent years, new satellite altimetry missions have been launched carrying new instruments; the CryoSat-2/Sentinel-3 missions equipped with a Delay/Doppler altimeter, the Saral AltiKa mission carrying the first Ka band altimeter, and the 2018 launched six beam photon counting laser altimeter on-board NASAs ICESat-2. Further, new orbits with high inclination and long-repeat time are used for CryoSat-2 and ICESat-2.
Fully exploiting this unprecedented availability of observables will enable new applications and results but also require novel and adapted methods of data analysis.
Across the different applications for satellite altimetry, the data analysis and underlying methods are similar and a knowledge exchange between the disciplines has been proofed to be fruitful.
In this multidisciplinary altimetry session, we therefore invite contributions which discuss new methodology and applications for satellite altimetry in the fields of geodesy, hydrology, cryosphere, oceanography, and climatology.
Topics of such studies could for example be (but not limited to); creation of robust and consistent time series across sensors, validation experiments, combination of radar and laser altimetry for e.g. remote sensing of snow, classification of waveforms, application of data in a geodetic orbit, retracking, or combination with other remote sensing data sets.
vPICO presentations: Mon, 26 Apr
Satellite altimeters have monitored the surface elevation change of the Greenland ice sheet since 1978 and with an ice-sheet wide coverage since 1991. The satellite altimeters of interest for Greenland mass balance studies operate at different wavelengths; Ku-band radar, Ka-band radar, infrared laser, and visible laser. Some of the applied wavelengths can penetrate the surface in snow-covered regions and map the elevation change of subsurface layers. Especially the longer radar wavelength can penetrate the upper meters of the snow cover, whereas the infrared laser measurements from ICESat observes the snow-air interface of ice sheets. The pure surface elevation change derived from ICESat has been widely used in mass balance studies and may provide a benchmark for altimetric mass balance estimates after being corrected for changes in the firn-air content. The Ku-band radar observation provides the longest time series of ice sheet volume change, but the record is more difficult to convert into mass balance due to climate-induced variations in the surface penetration.
Here, we apply machine learning to build an empirical calibration method for converting the observed radar-derived volume change into mass balance. We train the machine learning model during the limited period of coinciding laser and radar satellite altimetry data (2003-2009). The radar and laser datasets are not sufficient to guide the empirical calibration alone. Hence, additional datasets are used to help build a stable predictor needed for radar calibration, such as ice velocity.
We focus on the lessons learned from this machine learning approach but also highlight results from the resulting 28-yearlong time series of Greenland ice sheet mass balance. For example, the Greenland Ice Sheet contribution to global sea-level rise has been 12.1±2.3 mm since 1992, with more than 80% of this originating after 2003.
How to cite: Simonsen, S. B., Barletta, V. R., Colgan, W., and Sørensen, L. S.: A machine learning approach for Greenland ice sheet altimetric mass balance, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2474, https://doi.org/10.5194/egusphere-egu21-2474, 2021.
About a third of Greenland’s total ice losses come from the Northwest sector, a sector that includes a large number of marine-terminating outlet glaciers, which have all experienced widespread retreat triggered by ocean-induced melting. Here, we derive changes in surface elevation, volume and mass in the Northwest sector of the Greenland Ice Sheet using a decade of CryoSat-2 observations. We find an average elevation change rate of 18.7 ± 0.4 cm/yr, with rapid thinning at the ice sheet margins at a rate of 42.7 ± 0.9 cm/yr. We compare our CryoSat-2 rates of elevation change to airborne laser altimetry data from Operation IceBridge. Overall, there is a good agreement between the two datasets with a mean difference of 6.5 ± 0.5 cm/yr and standard deviation of 31.1 cm/yr. We further compute volume change, which we convert to mass change by testing three alternate density models and we find that the northwest sector has lost 386 ± 3.7 Gt of ice between July 2010 and July 2019. We compare our mass balance estimate to independent estimates from gravimetry and the mass budget method across different spatial scales. First, we compare the different estimates by splitting the sector into two and four regions. While our altimetry estimate is the least negative across all regions, the gravimetry and mass budget estimates alternate in recording the largest ice losses. We further compare mass changes derived from altimetry and the mass budget method in each of the 74 individual glacier basins of the Northwest sector. We find a high correlation of 0.81 between rates of mass change from altimetry and the mass budget method, with the highest differences recorded in Steenstrup-Dietrichson and Kjer Gletscher basins. Our comparisons show that the spatial pattern of the differences between mass balance estimates is complex, suggesting that discrepancies between techniques do not solely originate from one single region or technique. Finally, we explore several factors that could potentially bias our altimetry mass balance estimation, by investigating differences between satellite radar and airborne laser altimetry, the dependency on grid spatial resolution and the impact of using different density models.
How to cite: Otosaka, I., Shepherd, A., and Groh, A.: Changes in Northwest Greenland Ice Sheet Elevation and Mass, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2480, https://doi.org/10.5194/egusphere-egu21-2480, 2021.
Satellite radar altimetry is one of the most important tools for monitoring changes in the mass balance of the world's ice sheets. Different altimetry techniques however, come with their own pitfalls. In radar altimetry, signal penetration into the snowpack introduces ambiguity in the origin of reflected echo, a major issue not present in laser altimetry. Fine tuning the developed processing algorithms for the CryoSat-2 radar altimetry data, using the IceSat2 laser altimetry data as a benchmark, may allow for a more precise surface elevation and snowpack depth estimations. Furthermore, bridging the gap between radar and laser altimetry will result in larger spatial and temporal data coverage when the two data sets are combined. Focusing on Greenland Ice Sheet (GIS), we have developed a processing chain for the estimation of surface elevations and elevation changes from the ESA level-1 product (L1b) Baseline D. We investigated the importance of a retracker type, retracker threshold, Digital Elevation Model (DEM) in the slope correction, and how these affect the estimated surface elevation as compared to the ICESat2 data.
Firstly, ESA L1b Baseline-D data was processed at several different thresholds and with various waveform retracker algorithms, including threshold first maxima retracker algorithm (TFMRA) (Helm, 2012; Nilsson, 2015) and the offset center of gravity (OCOG) retracker algorithm (Bamber, 1994; Ricker et al. 2014). We then apply slope correction to adjust for the slope induced error in the radar altimetry data (Hurkmans, 2012), the correction was applied using three different DEMs, ArcticDEM Release 7 (Porter et al., 2018), Greenland Ice Mapping Project (GIMP) DEM (Howat et al., 2017) and ‘Helm’ DEM (Helm, 2014). We checked all of the produced data sets against IceSat-2 data (Smith et al., 2019) corresponding to the same time period, and selected by nearest neighbor calculation for specified maximum distance. We analyze and discuss the differences between IceSat-2 data and CryoSat-2 data and their dependence on several radar altimetry processing parameters and methodologies.
How to cite: Sejan, K., Wouters, B., and van den Broeke, M.: First steps to bridging the gap between CryoSat-2 and ICESat2: retrackers and slope induced error., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13083, https://doi.org/10.5194/egusphere-egu21-13083, 2021.
Satellite altimetry has been an important tool for observing the cryosphere. Various radar altimetry missions including CryoSat-2, Sentinel-3, and AltiKa have been exploited to measure ice-sheet elevation or to capture ice-sheet anomalies (e.g. the extensive melt in Greenland in 2012). These studies usually serve for understanding the change and status of the ice sheet, thus require highly accurate height measurements. However, multiple error sources exist that significantly lower the accuracy of the radar altimeter-derived heights. A potential multi-meter source of error is the slope-induced error caused by the undulating topography within the kilometre-wide pulse-limited footprint. The topography directs the reflecting point of radar pulse from the nadir to the point on the ground that is closest to the satellite.
To correct for this error, different methods have been developed to determine the impact point, which all rely on footprint assumptions: e.g. slope-method, which assumes a constant slope within the footprint, or the refined point-based method, which assumes a fixed footprint size and defines the reflecting point as the shortest mean range of points within each assumed footprint. Each of these methods have shortcoming as they either neglect the actual topography or the actual footprint that can be estimated by a combination of the leading edge and topography.
To overcome this shortcoming, we present a novel Leading Edge Point-Based (LEPTA) method that corrects for the slope-induced error by including the leading edge information of the radar waveform to determine the impact point. The principle of the method is that only the points on the ground that are within range determined by the begin and end of the leading edge are used to determine the impact point. This requires the assistance of a high-resolution DEM, e.g. 100m resolution. To assess the performance of the LEPTA method, we adopt it to all CryoSat-2 LRM acquisitions over Greenland in 2019 and benchmark it to the slope- and point-based method. To evaluate the results, we use the newly-launched laser altimeter, ICESat-2.
Validation results show that heights obtained by LEPTA have good agreements with ICESat-2 height observations, both in the flat, interior regions of Greenland and in regions with more complex topography. The median difference between the slope-corrected CryoSat-2 heights and the ICESat-2 heights is almost negligible, whereas the other methods can have a 0.22m and 0.69m difference, and the Level-2 data provided by ESA have a 0.01m difference. The median absolute deviation, which we use as an indicator of the variation of errors, is also the lowest in LEPTA (0.09m) in comparison to the aforementioned methods (0.22m and 0.13m) and ESA Level-2 data (0.15m). Based on that, we recommend considering LEPTA to obtain accurate height measurements with radar altimetry data, especially in regions with complex topography.
How to cite: Li, W., Slobbe, C., and Lhermitte, S.: A Novel Method for Correction of Slope-Induced Errors in Radar Altimetry, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16445, https://doi.org/10.5194/egusphere-egu21-16445, 2021.
Monitoring the Ice Sheets and ice caps in the polar region is important in a changing climate, and especially the coastal regions, which is the area that is most sensitive to changes in the climate and contributes to the global sea level rise (Gardner et al., 2013).
In this study, swath processed CryoSat-2 ice surface elevations are validated at four different locations with four different types of validation datasets; The Petermann Glacier and Nioghalvfjerdsfjorden Glacier in Northern Greenland, the Helheim glacier in the Eastern Greenland, and the ice cap of Austfonna located in Svalbard. The validation data consist of X-band radar data, Operation ICEBridge, ICESat-2 laser data, and Airborne Laser Scanner data respectively.
Swath processing improves the radar data coverage compared to conventional retracking, though, the extra amount of data leads to lower signal-to-noise ratio (Foresta et al., 2018), making validation of the swath processed data immensely important. Using different validation datasets allow us to investigate how the validation is impacted by the different platforms’ ability to measure the surface topography.
Foresta L, Gourmelen N, Weissgerber F, Nienow P, Shepherd A and Drinkwater M (2018) Heterogeneous and rapid ice loss over the Patagonian Ice Fields 2 revealed by CryoSat-2 swath radar altimetry. Remote Sensing of Environment, (minorrev(March), 0–1, ISSN 00344257 (doi: 10.1016/j.rse.2018.03.041)
Gardner AS, Moholdt G, Cogley JG, Wouters B, Arendt AA, Wahr J, Berthier E, Hock R, Pfeffer WT, Kaser G, Ligtenberg SRM, Bolch T, Sharp MJ, Hagen JO, van den Broeke MR and Paul F (2013) A Reconciled Estimate of Glacier Contributions to Sea Level Rise: 2003 to 2009. Science, 340(6134), 852–857, ISSN 0036-8075 (doi: 10.1126/science.1234532)
How to cite: Havelund, N., Sørensen, L., and Simonsen, S.: Validation of swath processed Cryosat-2 SARin data with different validation datasets at four different locations., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11985, https://doi.org/10.5194/egusphere-egu21-11985, 2021.
The Ice, Cloud, and Land Elevation Satellite-2 is well past its second year on orbit, and continues to collect high-quality measurements of the changing Cryosphere and beyond. The Advanced Topographic Laser Altimeter System (ATLAS) has now emitted more than ~800 billion laser shots to support science associated with sea ice and the polar oceans, glaciers and ice sheets, the world’s forests, oceans, lakes and rivers in addition to vertical profiles of clouds and aerosols. The ATLAS lidar measurements provide elevations with horizontal and vertical accuracies of 10 m and 10 cm respectively. Analysis also reveals the required precision (~2 cm) needed to resolve sea ice freeboard. The data is a unique resource for derived products as well with contributions to global biomass estimations, ice sheet mass balance determination and inventories of our planet’s surface water stores. Recently, there have been many open source data tools released to the community to help with data inquires, access and analytics. These tools are important resources as the data volume continues to build. In this presentation, we will provide an update on the operations and health of the observatory, review the many available data products served through the National Snow and Ice Data Center in the US, review new data tools available and highlight selected science results from the mission. As of this writing, more than ~10 million data granules have been downloaded by ~2700 unique data users. Recent science papers have documented the ongoing loss of mass from the Antarctic and Greenland ice sheets, the ability of ICESat-2 to measure the seasonal changes in sea ice freeboard and thickness throughout the year, and the potential for world-wide measurements of coastal bathymetry.
How to cite: Magruder, L., Neumann, T., and Kurtz, N.: The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2): Mission Status, Science Results and Outlook, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8967, https://doi.org/10.5194/egusphere-egu21-8967, 2021.
The A68 iceberg calved from the Larsen C ice shelf on the Antarctic Peninsula in July 2017 and has since been drifting northwards towards South Georgia. Originally covering an area of 5664 sq km, A68A's extent has been reduced to 2606 sq km (as of 23 December 2020) following the detachment of multiple smaller bergs. Using Satellite Altimetry data from CryoSat-2 and ICESat-2, we measure the thickness of the A68 iceberg. We use CryoSat-2 data acquired in the year before A68's calving from the Larsen C Ice Shelf in 2017 to create an initial thickness map. Following its calving, both CryoSat-2 and ICESat-2 tracks are geocoded onto the iceberg using imagery from MODIS and Sentinel-1. Comparing these measurements to the initial thickness allows us to track changes in A68's thickness. The thickness map reveals the presence of multiple 30m deep channels oriented along its narrow side, forming lines of weakness along which the iceberg shattered into multiple large fragments in December 2020. At the time of calving, its average thickness was 232m with a maximum thickness of 285m. Repeated measurements from satellite altimetry show the iceberg has thinned by an average of 32m, a thinning rate of 2.5cm per day. Combined with changes in area, we estimate that the iceberg has lost 64% of its original volume, or 941 cubic kilometres, representing a significant input of freshwater to the surrounding ocean.
How to cite: Izzard, J., Braakmann-Folgmann, A., Shepherd, A., and Lawrence, I.: Tracking the thickness of the A68 Iceberg using CryoSat-2 and ICESat-2, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5059, https://doi.org/10.5194/egusphere-egu21-5059, 2021.
The knowledge of ocean extreme wave climate is of significant importance for a number of coastal and marine activities (e.g. coastal protection, marine spatial planning, offshore engineering). This study uses the recently released Sea State CCI v1 altimeter product to analyze extreme wave climate conditions at global scale. The dataset comprises 28-years inter-calibrated and denoised significant wave height data from 10 altimeter missions.
First, a regional analysis of the available satellite information of extreme waves associated with both, tropical and extratropical cyclones, is carried out. As tropical cyclones, we analyze two intense events which affected the Florida Peninsula and Caribbean Islands: Wilma (in October 2005) and Irma (in August 2017) hurricanes. As extratropical cyclones, we focused on the extreme waves during the 2013-2014 winter season along the Atlantic European coasts. The extreme waves associated with these events are identified in the satellite dataset and are compared with in situ and high-resolution simulated data. The analysis of the satellite data during the storm tracks and its comparison against other data sources indicate that satellite data can provide added value for the analysis of extreme wave conditions that caused important coastal damages.
After assessing the quality of extreme wave data measured by altimeters from this regional analysis, we explore a method to characterize wave height return values (e.g. 50yr return period significant wave height) from the multi-mission satellite data. The method is validated through comparisons with return values estimated from long-term wave buoy records. The extreme analysis is based on monthly maxima of satellite significant wave height computed over marine areas of varying extensions and centered on a target location (e.g. the wave buoy location for comparison and validation of the method). The extension of the areas is defined from a seasonal study of the spatial correlation and the error metrics of the satellite data against the selected coastal location. We found a threshold of 0.85 correlation as the isoline to select the satellite data subsample (i.er. larger areas to select satellite maxima are found during winter seasons). Finally, a non-stationary extreme model based on GEV distribution is applied to obtain quantiles of low probability. Outcomes from satellite data are validated against extreme estimates from buoy records.
How to cite: Ramirez, M., Menendez, M., and Dodet, G.: Wave climate extremes from the Sea State CCI satellite data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7492, https://doi.org/10.5194/egusphere-egu21-7492, 2021.
How to cite: Hornschild, A., Saynisch-Wagner, J., Irrgang, C., and Thomas, M.: Using satellite altimetry and magnetometer to detect magnetic signals from ocean circulation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5434, https://doi.org/10.5194/egusphere-egu21-5434, 2021.
Thanks to a Observing System Simulation Experiment (OSSE) that simulate the along-track satellite measuring process on the sea surface of the high resolution model CROCO-MED60v40-15-16 we investigate how the reliability and the accuracy of the detected eddies are influenced by the satellite sampling and the mapping procedure. The main result of this study is that there is that there 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 cyclones and anticyclones. Large mesoscale cyclones often splits into smaller sub mesoscale structures hav ing a rapid dynamical evolution. The high resolution model CROCO-MED60v40 shows that this complex dynamic is too fast and too small to be accurately captured by the gridded altimetry products based on a strong spatio-temporal interpolation. The later smooth out this sub mesoscale dynamics and tend to generate an excessive number of unrealistic (i.e. unreliable) mesoscale cyclones in comparison with the reference field. On the other hand, large mesoscale anticyclones, which are more robust and that evolve more slowly, can be spatially resolved and accurately tracked by standard altimetry products. However, we confirm that gridded altimetry products have a systematic bias on the eddy intensity and especially for anticyclones. The azimuthal geostrophic velocities are always underestimated on the AVISO/CMEMS products even for large mesoscale anticyclones.
How to cite: Stegner, A., Le Vu, B., Dumas, F., Ghannami, M., Nicolle, A., and Faugere, Y.: Cyclone-Anticyclone asymmetry of eddy detection on gridded altimetry product in the Mediterranean Sea , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7860, https://doi.org/10.5194/egusphere-egu21-7860, 2021.
Several satellite missions are planned or have been launched to contribute to our understanding of coastal oceanography and to observe sea level, a variable of high societal importance. One of those satellites is Sentinel-3A, which was launched in February 2016, giving near-global coverage at 27-day repeat cycle and carrying Ku- and C-band synthetic aperture radar altimeter (SRAL). SRAL has enabled more reliable remote sensing of coastal ocean sea level with a higher resolution than conventional altimetry. Here, the ability to robustly discern coherent sea level changes with Sentinel-3A SRAL products is evaluated at the oceanographically complex coastal regions of the Atlantic coast of North America.
We used RADS (Radar Altimeter Database System) L2 product to calculate sea surface height anomaly (SSHA) at a set of comparison points (CP)—interpolating the measurements onto nominal ground tracks—within 250 km around selected tide gauges (TG). We compared these CP with TG measurements and ECCO2 Cube92 model output to determine the correlations and obtain spatial scales and patterns of decorrelation between the SRAL observations and the other source of data (in situ and the model).
How to cite: Abele, A., Royston, S., and Bamber, J.: Coastal sea level change from Sentinel-3A SRAL over the U.S. Eastern seaboard, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10971, https://doi.org/10.5194/egusphere-egu21-10971, 2021.
Accurate long-term measurements of coastal sea level are fundamental for understanding changes in ocean circulation and assessing the impact of low-frequency sea-level variability on, e.g., near-shore ecosystems, groundwater dynamics, and coastal flooding. However, tide gauges are sparsely distributed in space and the extent to which satellite altimetry data can be used to infer the complex patterns of sea level near the coast is a subject of debate. Here, we revisit earlier attempts of connecting tide gauge and altimetry observations of low-frequency sea-level changes across the coastal zone. Our interest lies both in short-scale spatial structures indicative of dynamic decoupling between coastal areas and the deep ocean, and in the benefits of using a reprocessed, coastal altimetry product (X-TRACK) for the analysis. The mean annual cycle is chosen as a first benchmark and more than 200 globally distributed tide gauges are examined. We compute statistics between tide gauge and along-track altimeter series within spatial radii of 20 km (“coastal”) and 134 km of the tide gauge location, and additionally split altimetry data inside the 134-km circle into “shallow” and “deep” groups relative to the 200-m isobaths. Globally averaged RMS (root-mean-square) differences in the “coastal” and “shallow” categories are 1.9 and 2.4 cm for the X-TRACK product, somewhat lower than the corresponding values from the non-optimized Integrated Multi-Mission Ocean Altimeter Data for Climate Research Version 4.2 (2.3 and 2.6 cm). Examination of inter-annual sea-level variability from 1993 to 2019 is underway, with initial focus on regions where poor correspondence between satellite and tide gauge sea-level estimates has been noted in the past (e.g., US East Coast and western South America). At most locations analyzed so far, RMS differences decrease and correlations improve as one approaches the coast along the satellite tracks. However, the X-TRACK estimates tend to become erratic within 20–30 km from the tide gauge, suggesting that the usability of classical nadir altimetry measurements for studying short-scale coastal dynamics is still limited despite ongoing reprocessing efforts.
How to cite: Kudabayeva, A., Schindelegger, M., Ponte, R. M., and Uebbing, B.: Annual and inter-annual sea-level variability from coastal altimetry and tide gauge data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8760, https://doi.org/10.5194/egusphere-egu21-8760, 2021.
The northwestern coast of Luzon Island is located within the forearc region of the Manila Trench where emergent coral reef platforms have been reported; and an uplift rate of 0.5 m/kyr has been estimated for the past 7,000 years in San Fernando and Currimao. This study examined the present-day vertical land movement (VLM) in both sites using tide gauge records and retracked Jason satellite altimeter missions. Both the tide gauge and satellite data were corrected for tides using the T_Tide algorithm and the difference between the tide gauge sea level (TGSL) and sea surface heights (SSH) from the satellite were calculated. The influence of VLM was inferred from the differences between the TGSL and SSH, then validated using available GNSS data.
Hourly TGSL for San Fernando is available from 2002 to 2018 with a completeness index (CI) of 37%. The satellite products used were the 20 Hz MLE4 and 1Hz ALES retracked Jason satellite series downloaded from AVISO+ and OpenADB, respectively. The MLE4 product indicates subsidence with a rate of 0.43 ± 0.10 mm/yr, while ALES indicates uplift at 1.93 ± 0.42 mm/yr. GNSS observations at the San Fernando TG benchmark (TGBM) from 2017 to 2019 shows subsidence at 0.74 ± 0.40 mm/yr, which agrees well with the VLM estimate from the difference between TGSL and MLE4 SSH.
Currimao TG station has a CI of 90% from 2008 to 2016. Satellite products used were the 20 Hz MLE4 and 20 Hz ALES retracked Jason-2 downloaded from AVISO+, and both indicate uplift with a rate of 7.30 ± 0.17 and 6.24 ± 0.25 mm/yr, respectively. The present-day uplift agrees with the geological records, however, there are no GNSS data at the TGBM to validate the present-day vertical motion.
The differences between the present-day vertical motion of San Fernando and Currimao may indicate the influence of other fault systems associated with the Philippine Fault or segmentation of the forearc. Subsidence in San Fernando could imply stress accumulation in the area and the observed uplift in the geological records are cumulative co-seismic vertical displacements.
How to cite: Flores, P. C., Rediang, A., Pasaje, N. A., Alfante, R. M., Bauzon, M. D. A., Reyes, R., Siringan, F., Amedo-Repollo, C. L., Bringas, D., and Blanco, A.: Present-day vertical land movement in San Fernando (La Union) and Currimao (Ilocos Norte), northwest Luzon, Philippines, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12815, https://doi.org/10.5194/egusphere-egu21-12815, 2021.
The absolute and relative accuracy of sea surface heights derived from six altimeter missions (Jason-1/2/3, Envisat, Saral, Sentinel-3A) is evaluated at five GNSS-controlled tide gauge stations in the German Bight (SE North Sea). The precision of the total water level envelope (TWLE) is assessed for the period 2000 to 2019 based on RMS errors and explained variances. The comparison is based on TWLE instead of dealiased sea level data since the tidal and barotropic dynamic is not known with sufficient accuracy in this area. The tide gauges are partly located at the open sea, partly at the coast close to mudflats. The tide gauge data is available every minute, the 20 Hz level 2 altimetry data is interpolated to virtual stations at distances between 2 and 15 km to the tide gauges. The altimeter data is based on standard retrackers, the correction models are adjusted to coastal applications and exclude the corrections for ocean tides and dynamic atmosphere to allow a direct comparison to the tide gauge data. To account for slight differences of the tidal dynamics between gauge and altimetry an optimal time shift and scale between each pair of locations is estimated and applied. This tidal correction improves the RMS errors by 15-75%. The explained variances are excellent at all stations (> 96%). The resultant RMS errors are mainly between 2-5 cm depending on location and mission. The RMS errors rise up to 10 cm where coastal dynamics play a dominant role or the altimeter approaches the land very closely (<7 km). The accuracy of the absolute biases is strongly dependent on the knowledge of the mean sea surface heights in the region.
How to cite: Esselborn, S., Illigner, J., Schöne, T., Weiß, R., Artz, T., and Huang, X.: Validation of recent altimeter missions at non-dedicated tide gauge stations in the Southeastern North Sea, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16122, https://doi.org/10.5194/egusphere-egu21-16122, 2021.
Accurate knowledge of sea level change, especially close to the coast, is of major importance in order to analyze and understand drivers of local sea level change and to plan coastal protection measures. Satellite altimetry provides a continuous global record of sea level rise since about 1993. In recent years, the delay doppler altimetry (DDA), also called SAR altimetry, provides improved results compared to conventional altimetry (CA) by utilizing the Doppler effect along the satellite’s groundtrack.
The altimeter emits a radar pulse from the satellite to the Earth’s surface and measure the power reflected over time from the radar footprint forming a so called “waveform”. From the shift, shape and amplitude of this waveform it is possible to estimate sea surface height (SSH), significant waveheight (SWH) and backscatter which is related to wind speed. Due to influences from land surfaces within the radar footprint standard methods of retrieving those estimates tend to become increasingly uncertain or even fail when the satellite groundtrack approaches the coastline. In order to still derive meaningful geophysical parameters it is necessary to reprocess or “retrack” those waveforms with specialized algorithms resulting in improved estimates.
Here, we present a novel retracker which adapts the Spatio Temporal Altimetry Retracker (STARv1.0) processing scheme for CA to DDA. Generally, the STAR algorithm consists of three steps: (1) Partitioning of the total return waveform into individual sub-waveforms, (2) retracking of each individual sub-waveform resulting in a point-cloud of potential estimates of SSH, SWH and backscatter and (3) selection of final estimates at each 20Hz measurement position. For the application to DDA the three parameter Brown model used in CA-STAR is replaced by the Signal model Involving Numerical Convolution for SAR (SINCS) model, already implemented in the Technical University Darmstadt – University Bonn SAR-Reduced SAR (TUDaBo SAR-RDSAR) processing scheme.
The combination of the updated STARv2.5 processing scheme with the SINCS model (STARS) allows to retrieve high quality sea level estimates for contemporary DDA altimeter missions. We will provide validation results for Cryosat-2 and Sentinel-3 data in the North Sea region for the time period 2016-2019. Our preliminary results suggest that we are able to derive significantly improved results for SSH, SWH and backscatter from STARS compared to existing state of the art approaches for DDA. While originally developed for coastal regions, the STAR processing scheme also leads to improved open ocean results.
How to cite: Uebbing, B., Buchhaupt, C., Stolzenberger, S., Fenoglio, L., Kusche, J., and Dinardo, S.: Improving coastal altimetry results using the Spatio Temporal Altimetry Retracking for SAR (STARS), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3046, https://doi.org/10.5194/egusphere-egu21-3046, 2021.
Improved SAR Altimetry Techniques in Coastal Island Areas
Synthetic Aperture Radar (SAR) Altimetry has made a remarkable progress over the past years. Advances in data processing, combined with technological progress such as the advent of new Altimetry satellites (Sentinel 3A,3B,6, SWOT etc.) increased the accuracy of the retrieved geophysical parameters (i.e., Sea Level Anomaly, Significant Wave Height and Wind Speed) in coastal zones within several hundred meters from the coastline.
The improvement in the estimation of the geophysical parameters using SAR Altimetry has been reported by many researchers. The improved accuracy is obtained through the development of new SAR Altimetry retracking algorithms in several research and development projects (i.e., SAR Altimetry Mode Studies and Applications-SAMOSA). Similar to Low Resolution Mode (LRM) Altimetry, the requirement of specialized retrackers for SAR waveforms is vital in improving the estimated ocean parameters. The waveform retracking is a postprocessing protocol to convert waveforms into scientific parameters of power amplitude (related to wind speed), range (related to sea level), and slope of leading edge (related to SWH) that characterize the observed scene (Idris et al., 2021).
However, several issues remain open. Close to the coastline, SAR altimeter simultaneously views scattering surfaces of both water and land producing complicated waveform patterns therefore a huge range of waveform shapes is observed. This complexity poses a real challenge to today’s approach to retrack waveform.
The combination of different retracking algorithms is essential for dealing with this high diversity of altimetric waveform patterns since there is no single retracker that can retrack all of them. However, this raises two significant issues. The first is regarding to the selection of the optimal retracker under various conditions. The lack of a clear guideline on the selection criteria of the optimal retracker limits the use of this combining method. The second is how to reduce the offset caused by switching retrackers, as it results in relative offsets in altimeter-derived SLAs. This offset is partly caused by the retracking method itself, in which the fitting algorithms are affected by noise in the trailing edge due to the SWHs variability (Idris et al., 2018).
Due to the issues in coastal Altimetry data the focus of this work is:
- 1) To improve the sea measurements from the SAR Altimetry missions by developing a new retracking algorithm taking advantage artificial intelligence and machine learning technologies.
- 2) To further investigate the assessment of the offset between various retrackers and the use of a neural network for reducing the offset in the retracked SLAs by including information about SWH.
- 3) To validate the altimeter derived SLAs by performing tests and comparisons with data from many island coastal areas worldwide.
Also, this work aims to improve the Sea State Bias corrections (SSB), which is currently one of the range corrections with the largest uncertainty in the coastal zone (Vignudelli et al., 2019), by providing more accurate sea measurements near the coast.
How to cite: Flokos, N. and Tsakiri, M.: Improved SAR Altimetry Techniques in Coastal Island Areas, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8942, https://doi.org/10.5194/egusphere-egu21-8942, 2021.
HYDROCOASTAL is a two year project funded by ESA, with the objective to maximise exploitation of SAR and SARin altimeter measurements in the coastal zone and inland waters, by evaluating and implementing new approaches to process SAR and SARin data from CryoSat-2, and SAR altimeter data from Sentinel-3A and Sentinel-3B. Optical data from Sentinel-2 MSI and Sentinel-3 OLCI instruments will also be used in generating River Discharge products.
New SAR and SARin processing algorithms for the coastal zone and inland waters will be developed and implemented and evaluated through an initial Test Data Set for selected regions. From the results of this evaluation a processing scheme will be implemented to generate global coastal zone and river discharge data sets.
A series of case studies will assess these products in terms of their scientific impacts.
All the produced data sets will be available on request to external researchers, and full descriptions of the processing algorithms will be provided
The scientific objectives of HYDROCOASTAL are to enhance our understanding of interactions between the inland water and coastal zone, between the coastal zone and the open ocean, and the small scale processes that govern these interactions. Also the project aims to improve our capability to characterize the variation at different time scales of inland water storage, exchanges with the ocean and the impact on regional sea-level changes
The technical objectives are to develop and evaluate new SAR and SARin altimetry processing techniques in support of the scientific objectives, including stack processing, and filtering, and retracking. Also an improved Wet Troposphere Correction will be developed and evaluated.
There are four tasks to the project
- Scientific Review and Requirements Consolidation: Review the current state of the art in SAR and SARin altimeter data processing as applied to the coastal zone and to inland waters
- Implementation and Validation: New processing algorithms with be implemented to generate a Test Data sets, which will be validated against models, in-situ data, and other satellite data sets. Selected algorithms will then be used to generate global coastal zone and river discharge data sets
- Impacts Assessment: The impact of these global products will be assess in a series of Case Studies
- Outreach and Roadmap: Outreach material will be prepared and distributed to engage with the wider scientific community and provide recommendations for development of future missions and future research.
The presentation will provide an overview to the project, present the different SAR altimeter processing algorithms that are being evaluated in the first phase of the project, and early results from the evaluation of the initial test data set.
How to cite: Cotton, D. and the HYDROCOASTAL Project Team: Improving SAR Altimeter processing over the coastal zone and inland waters - the ESA HYDROCOASTAL project, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9, https://doi.org/10.5194/egusphere-egu21-9, 2020.
Lake water height is a key variable in water cycle and climate change studies, which is achievable using satellite altimetry constellation. A method based on data processing of altimetry from several satellites has been developed to interpolate mean lake surface (MLS) over a set of 22 big lakes distributed on the Earth. It has been applied on nadir radar altimeters in Low Resolution Mode (LRM: Jason-3, Saral/AltiKa, CryoSat-2) in Synthetic Aperture Radar (SAR) mode (Sentinel-3A), and in SAR interferometric (SARin) mode (CryoSat-2), and on laser altimetry (ICESat). Validation of the method has been performed using a set of kinematic GPS height profiles from 18 field campaigns over the lake Issykkul, by comparison of altimetry’s height at crossover points for the other lakes and using the laser altimetry on ICESat-2 mission. The precision reached ranges from 3 to 7 cm RMS (Root Mean Square) depending on the lakes. Currently, lake water level inferred from satellite altimetry is provided with respect to an ellipsoid. Ellipsoidal heights are converted into orthométric heights using geoid models interpolated along the satellite tracks. These global geoid models were inferred from geodetic satellite missions coupled with absolute and regional anomaly gravity data sets spread over the Earth. However, the spatial resolution of the current geoid models does not allow capturing short wavelength undulations that may reach decimeters in mountaineering regions or for rift lakes (Baikal, Issykkul, Malawi, Tanganika). We interpolate in this work the geoid height anomalies with three recent geoid models, the EGM2008, XGM2016 and EIGEN-6C4d, and compare them with the Mean Surface of 22 lakes calculated using satellite altimetry. Assuming that MLS mimics the local undulations of the geoid, our study shows that over a large set of lakes (in East Africa, Andean mountain and Central Asia), short wavelength undulations of the geoid in poorly sampled areas can be derived using satellite altimetry. The models used in this study present very similar geographical patterns when compared to MLS. The precision of the models largely depends on the location of the lakes and is about 18 cm, in average over the Earth. MLS can serve as a validation dataset for any future geoid model. It will also be useful for validation of the future mission SWOT (Surface Water and Ocean Topography) which will measure and map water heights over the lakes with a high horizontal resolution of 250 by 250 meters.
How to cite: Crétaux, J.-F., Berge-Nguyen, M., Calmant, S., Fleury, S., Satylkanov, R., and Bonnefond, P.: Mapping Mean Lake Surface from satellite altimetry and GPS kinematic surveys, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14797, https://doi.org/10.5194/egusphere-egu21-14797, 2021.
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