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Sea level rise is among the most pressing environmental, social and economic challenges facing humanity, and requires timely and reliable information for adaptation and mitigation. The ice sheets of Greenland and Antarctica currently contribute approximately one third of global sea level rise, yet monitoring their coastal regions, which are often populated by numerous, highly dynamic outlet glaciers remains a challenge. One of the principal methods for monitoring ice sheet change is that of satellite radar altimetry, which provides a near continuous 30-year record of surface elevation and volume change. However, this technique can suffer from incomplete measurements and larger uncertainties over rugged coastal topography, where the instrument may fail to track the ice surface, or may record multiple distinct reflections within the illuminated ground footprint. In these situations, current Level 2 processing approaches can be sub-optimal, leading to inaccuracies being introduced into the resulting elevation measurements. Therefore, this study aims to explore new approaches to retrieving elevation measurements, comprising (1) multipeak waveform retracking and (2) a refined slope correction approach over complex regions. The developed approaches offer the potential for multiple elevation retrievals from a single waveform, and in turn the opportunity to increase both the reliability and quantity of elevation measurements.Within the study, these processing techniques were developed and evaluated across Russell Glacier and the whole Greenland as two typical test cases, based upon Sentinel-3 SAR altimeter acquisitions over ice sheet regions that exhibit complex topography. Laser altimeters including Airborne Topographic Mapper (ATM) and Ice, Cloud, and Land Elevation Satellite-2 (IceSat-2) data were used as independent validation sources. Ice sheet wide analysis showed that the developed approaches were capable of delivering equally high accuracy for multiple elevation retrievals with comparable dispersion (~ 1 m) but much lower bias (~ 0.5 m) and outliers (~ 4%) compared to standard Level-2 products (~ 4 m bias and ~ 20% outliers). The developed approaches have the potential to further extend the capability of satellite radar altimetry over complex glaciological targets, and to improve the accuracy and coverage of altimeter measurements across these regions.

Producing accurate time series of surface elevation change is vital to our understanding of ice sheet contributions to sea level rise. Such estimates are derived from a variety of sources, including automatic weather station measurements, regional climate models, and satellite altimeter observations. For satellite altimetry, a common approach for computing elevation changes is to fit a simple polynomial model to repeated measurements, from an individual satellite mission, within small (typically < 10km) ice sheet regions. However, since there now exists multiple altimeters in orbit synchronously, we instead aim to employ techniques that are more capable of fusing measurements from multiple sensors whilst also respecting any differences in uncertainties. One such technique is Kalman filtering/smoothing, which also provides opportunities for assimilating this data with ice sheet models. Four-dimensional local ensemble transform Kalman filtering (4D-LETKF) is a Bayesian approach capable of estimating large spatiotemporally chaotic systems and has already seen use in the fields of meteorology and oceanography for assimilating observations into complex forecasting models. Therefore, we explore applying a similar approach to cryosphere datasets to generate time series of surface elevation change. Our method uses satellite altimeter observations, which provide a continuous continental-scale record of ice sheet elevation measurements. The chosen state transition model is the identity matrix, predicting no change from one day to the next, allowing the observations to drive the results. Ensembles are not required because of the model’s simplicity but will be explored in the future to enable more advanced predictive models. Here, we present the method development for this novel application of the 4D-LETKF, demonstrating these techniques on CryoSat-2 radar altimetry measurements over the Greenland ice sheet from ESA’s Cryo-TEMPO Baseline-B dataset. Our next steps are to fuse other current and historical missions to produce a single collective gridded time series of surface elevation change over ice sheets. As such, we hope this study can provide the first steps towards more formal data assimilation of Earth observation data into physical ice sheet models.