G3.4

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.

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 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.

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.

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.

Introduction

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

Objectives

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.

Project  Outline

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.

Presentation

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, 2021.