In this paper we review the precision orbit determination (POD) performance of the CryoSat-2 mission where we used all tracking data between June-2010 and Jan-2023; with station and beacon coordinates provided in the ITRF2020 reference system, we use a mean gravity model, and we use spacecraft specific models for modeling drag and radiation pressure. To model time variable gravity (TVG) we distinguish between two components, there is a short term oceanic and atmospheric part for which we use the AOD1B model; for the longer term part we employ GRACE and GRACE-FO monthly potential coefficient solutions. Our experience is that adding TVG information is not necessarily successful during POD, and that attention must be paid to the proper processing of the GRACE and GRACE-FO data. To demonstrate this property we define four runs where we gradually implement TVG information. An evaluation criterion is the level of POD tracking residuals, the level of the empirical accelerations, and a comparison to precision orbit ephemeris provided by the Centre National d'Etudes Spatiales (CNES). Unexplained empirical accelerations found during POD are on the level of 3 nm/s ² for the along-track component and 13 nm/s ² for the cross-track component. The laser residuals converge at approximately 1.02 cm and the Doppler residuals are on the level of 0.406 mm/s, the radial orbit difference to the CNES POE-F (Precision Orbit Ephemeris version F) orbits narrows to 6.5 mm. Tracking residuals are not evenly distributed for DORIS (Doppler Orbitography and Radiopositioning Integrated by Satellite) beacons, the South Atlantic Anomaly effect is for instance clearly visible in the first empirical orthogonal function EOF mode of monthly binned DORIS residuals. After consideration of all possible TVG approaches our conclusion is that 3 hourly AOD1B model fields result in a small but visible improvement. The addition of TVG from GRACE and GRACE-FO is implemented in two different ways from which we can select a version that does lead to a reduction in the Doppler tracking residuals and which does reduce the level of solved for empirical accelerations.

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Geodynamic processes in Antarctica such as glacial isostatic adjustment (GIA) and post-seismic deformation are measured by geo-detic observations such as global navigation satellite systems (GNSS) and satellite gravimetry. GNSS measurements have comprised both continuous measurements and episodic measurements since the mid-1990s. The estimated velocities typically reach an accuracy of 1 mm a⁻¹ for horizontal velocities and 2 mm a⁻¹ for vertical velocities. However, the elastic deformation due to present-day ice-load change needs to be considered accordingly. Space gravimetry derives mass changes from small variations in the inter-satellite distance of a pair of satellites, starting with the GRACE (Gravity Recovery and Climate Experiment) satellite mission in 2002 and continuing with the GRACE-FO (GRACE Follow-On) mission launched in 2018. The spatial resolution of the measurements is low (about 300 km) but the measurement error is homogeneous across Ant-arctica. The estimated trends contain signals from ice-mass change, and local and global GIA signals. To combine the strengths of the individual datasets, statistical combinations of GNSS, GRACE and satellite altimetry data have been developed. These combinations rely on realistic error estimates and assumptions of snow density. Nevertheless, they capture signals that are missing from geodynamic forward models such as the large uplift in the Amundsen Sea sector caused by a low-viscous response to century-scale ice-mass changes.

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Ice losses from the Greenland and Antarctic ice sheets have accelerated since the 1990s, accounting for a significant increase in the global mean sea level. Here, we present a new 29-year record of ice sheet mass balance from 1992 to 2020 from the Ice Sheet Mass Balance Inter-comparison Exercise (IMBIE). We compare and combine 50 independent estimates of ice sheet mass balance derived from satellite observations of temporal changes in ice sheet flow, in ice sheet volume, and in Earth's gravity field. Between 1992 and 2020, the ice sheets contributed 21.0±1.9g€¯mm to global mean sea level, with the rate of mass loss rising from 105g€¯Gtg€¯yr-1 between 1992 and 1996 to 372g€¯Gtg€¯yr-1 between 2016 and 2020. In Greenland, the rate of mass loss is 169±9g€¯Gtg€¯yr-1 between 1992 and 2020, but there are large inter-annual variations in mass balance, with mass loss ranging from 86g€¯Gtg€¯yr-1 in 2017 to 444g€¯Gtg€¯yr-1 in 2019 due to large variability in surface mass balance. In Antarctica, ice losses continue to be dominated by mass loss from West Antarctica (82±9g€¯Gtg€¯yr-1) and, to a lesser extent, from the Antarctic Peninsula (13±5g€¯Gtg€¯yr-1). East Antarctica remains close to a state of balance, with a small gain of 3±15g€¯Gtg€¯yr-1, but is the most uncertain component of Antarctica's mass balance. The dataset is publicly available at 10.5285/77B64C55-7166-4A06-9DEF-2E400398E452 (IMBIE Team, 2021).

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ESA’s Earth Explorer CryoSat-2 precisely measures the changes in the thickness of marine ice floating on the polar oceans and variations in the thickness of the vast ice sheets that overlie Greenland and Antarctica. The data delivered by the CryoSat-2 mission completes the picture to determine and understand the ice role in the Earth system in general and climate change in particular. For this, the quality of the satellite orbit, the measurements of the altimeter, and all required corrections have to meet the highest performance; not only over the ice caps and sea-ice surface but also over the oceans. As Cryosat-2 ocean products continuously evolve they need to be quality controlled and thoroughly validated via science-oriented diagnostics based on multi-platform in situ data, models and other (altimeter) satellite missions. The rationale for this is based on the new CryoSat-2 scientific roadmap, which specifically addresses the key technical and scientific challenges related to the long-term monitoring of sea-level and ocean circulation changes in the context of Global Warming. This also involves opportunities for synergy with missions like ICESAT-2 and the upcoming Copernicus CRISTAL mission. In this context, the objective of our research is the long-term monitoring of the level-2 CryoSat-2 Geophysical Ocean Product (GOP), by evaluating the stability of the measurement system and identifying potential biases, trends and drifts over the ocean, through calibration and comparisons with concurrent ocean altimeter data, supported by the Radar Altimeter Database System (RADS). Independently, we also address this by comparing the GOP geophysical parameters with external models and in situ measurements such as the ones from selected sets of tide gauges. The very precise determination of the orbital height is part of the research activity but dealt with in a separate paper. For our activity we persistently monitor, analyze and identify systematic errors in the observations, estimated (trends in) biases in range, significant wave height, backscatter, wind speed and sea state bias, and timing biases. An important finding is that GOP CryoSat-2 Baseline C data seem to have a range bias of -2.82 cm and no apparent drift w.r.t. altimeter (Jason) reference missions (< 0.1 mm/yr). The comparison with tide gauges is based on monthly averaged sea level from the PSMSL archive, for which we conclude that GOP data has a correlation of better than 0.84 with a selected set of 185 PSMSL tide gauges, a mean standard deviation better than 5.8 cm, and an average drift of -0.19 mm/yr, which translates to an overall drift of +0.11 mm/yr when taking a global GIA correction of +0.3 mm/yr into account. We conclude that Cryosat-2 GOP represents a (long-term) stable measurement.@en

Regular products within the field of Space Surveillance and Tracking (SST) and Space Traffic Management (STM), such as high-risk collisions, upcoming re-entries or fragmentations, rely both on the estimated state and associated uncertainty of detectable resident space objects (RSOs). Classical orbit determination (OD) algorithms provide the required estimations, assuming that the uncertainty in the state of the object is properly characterized by its state vector covariance and assuming Gaussian processes. However, a common problem of classical orbit determination processes is the misrepresentation of the RSOs uncertainty through the estimated covariance. Ultimately, this causes a great impact in the quality and accuracy of SST products as the estimated covariance is overly optimistic (too small) and the true uncertainty of the object is not captured. One of the causes for the unrealism of the estimated covariance is found in the classical OD approaches, as they fail to consider, or properly characterize, the uncertainty of the dynamical models used to describe the motion of the objects, such as the atmospheric drag force or the solar radiation pressure acting on the orbiting RSOs. Because these models provide a deterministic solution to a stochastic phenomenon, an inherent associated uncertainty should be regarded when used during an orbit determination. The aim of this work is to devise a methodology to improve the covariance realism of common OD processes through the classical theory of consider parameters of batch least squares methods. The methodology uses the classical theory of consider parameter to add to the estimated covariance the contribution coming from the uncertainty of the consider parameters. To do so, the variances of the consider parameters are estimated through another least squares process, with which the propagated covariance best fits a so-called observed covariance, previously derived, in a process named covariance determination. The influence of the main sources of dynamic model uncertainty can be evaluated by examining the resulting covariance correction for each uncertainty source (e.g. atmospheric drag force modelling, sensor calibration parameters or solar radiation prediction). This publication focus on studying the effect of the atmospheric drag force and range bias modelling uncertainty in the correction of an estimated covariance. The proposed methodology has been applied to a simulated realistic scenario of measurements and objects to evaluate the consistency of the corrected covariance via Monte Carlo analysis. Thorough analyses are presented to illustrate the effect of dynamic model errors on covariance realism. Copyright © 2019 by the International Astronautical Federation (IAF). All rights reserved.@en

Orbit determination of low earth orbiting satellites depends on accurate dynamic models to describe the accelerations acting on the satellite. The importance of dynamic models increases when the tracking data are not continuously available; as is the case for instance with the CryoSat-2 satellite where precision orbit determination depends on DORIS and laser tracking. CryoSat-2 POD is significantly affected by time variable gravity (TVG) model, which in turn depends on the availability of GRACE and GRACE-FO monthly gravity solutions. The GRACE / GRACE-FO monthly series of gravity solutions began in March 2002 well before CryoSat-2 was launched in April 2010. The TVG solutions from GRACE are available until June 2017 when the scientific mission came to an end; from this date on there is an outage until June 2018 when the GRACE-FO TVG solutions became available. The last retrieved TVG solution from GRACE-FO is for May 2019; both GRACE and GRACE-FO solutions are based on inter-satellite microwave tracking. For application in POD the TVG solution from this series must be complemented with ocean and atmospheric time variable gravity effects predicted by the AOD1B models. On monthly intervals the GRACE and GRACE-FO data are de-aliased, the result mostly represents mass changes in the Earth’s continental water storage. Ocean and atmospheric time variable gravity modelling is not affected by the outage in the GRACE/GRACE-FO time series. The quality of the GRACE TVG series deteriorates in the last year of the GRACE mission when eclipsing affects power management. In an attempt to preserve the batteries, data acquisition was halted in several months. The expectation is that the accuracy of POD deteriorates since the beginning of 2016. The total length of the data gap in the non-AOD1B TVG model may therefore extend until the beginning of the GRACE-FO monthly solution series. We show that GRACE-FO and GRACE can be successfully combined for POD of CryoSat-2; a variance optimization technique is used to derive a TVG model. This is the new strategy that replaces our former TVG model derived from GRACE alone. DORIS residuals noticeably improve for various implementations of TVG models; we use this technique to find an optimal strategy for the POD of CryoSat-2. @en

The Greenland Ice Sheet has been a major contributor to global sea-level rise in recent decades ^1,2, and it is expected to continue to be so ³. Although increases in glacier flow ^4–6 and surface melting ^7–9 have been driven by oceanic ^10–12 and atmospheric ^13,14 warming, the magnitude and trajectory of the ice sheet’s mass imbalance remain uncertain. Here we compare and combine 26 individual satellite measurements of changes in the ice sheet’s volume, flow and gravitational potential to produce a reconciled estimate of its mass balance. The ice sheet was close to a state of balance in the 1990s, but annual losses have risen since then, peaking at 345 ± 66 billion tonnes per year in 2011. In all, Greenland lost 3,902 ± 342 billion tonnes of ice between 1992 and 2018, causing the mean sea level to rise by 10.8 ± 0.9 millimetres. Using three regional climate models, we show that the reduced surface mass balance has driven 1,964 ± 565 billion tonnes (50.3 per cent) of the ice loss owing to increased meltwater runoff. The remaining 1,938 ± 541 billion tonnes (49.7 per cent) of ice loss was due to increased glacier dynamical imbalance, which rose from 46 ± 37 billion tonnes per year in the 1990s to 87 ± 25 billion tonnes per year since then. The total rate of ice loss slowed to 222 ± 30 billion tonnes per year between 2013 and 2017, on average, as atmospheric circulation favoured cooler conditions ¹⁵ and ocean temperatures fell at the terminus of Jakobshavn Isbræ ¹⁶. Cumulative ice losses from Greenland as a whole have been close to the rates predicted by the Intergovernmental Panel on Climate Change for their high-end climate warming scenario ¹⁷, which forecast an additional 70 to 130 millimetres of global sea-level rise by 2100 compared with their central estimate.

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Satellite gravimetry data acquired by the Gravity Recovery and Climate Experiment (GRACE) allows to derive the temporal evolution in ice mass for both the Antarctic Ice Sheet (AIS) and the Greenland Ice Sheet (GIS). Various algorithms have been used in a wide range of studies to generate Gravimetric Mass Balance (GMB) products. Results from different studies may be affected by substantial differences in the processing, including the applied algorithm, the utilised background models and the time period under consideration. This study gives a detailed description of an assessment of the performance of GMB algorithms using actual GRACE monthly solutions for a prescribed period as well as synthetic data sets. The inter-comparison exercise was conducted in the scope of the European Space Agency’s Climate Change Initiative (CCI) project for the AIS and GIS, and was, for the first time, open to everyone. GMB products generated by different groups could be evaluated and directly compared against each other. For the period from 2003-02 to 2013-12, estimated linear trends in ice mass vary between −99 Gt/yr and −108 Gt/yr for the AIS and between −252 Gt/yr and −274 Gt/yr for the GIS, respectively. The spread between the solutions is larger if smaller drainage basins or gridded GMB products are considered. Finally, findings from the exercise formed the basis to select the algorithms used for the GMB product generation within the AIS and GIS CCI project.@en

Global mean sea level is an integral of changes occurring in the climate system in response to unforced climate variability as well as natural and anthropogenic forcing factors. Its temporal evolution allows changes (e.g., acceleration) to be detected in one or more components. Study of the sea-level budget provides constraints on missing or poorly known contributions, such as the unsurveyed deep ocean or the still uncertain land water component. In the context of the World Climate Research Programme Grand Challenge entitled “Regional Sea Level and Coastal Impacts”, an international effort involving the sea-level community worldwide has been recently initiated with the objective of assessing the various datasets used to estimate components of the sea-level budget during the altimetry era (1993 to present). These datasets are based on the combination of a broad range of space-based and in situ observations, model estimates, and algorithms. Evaluating their quality, quantifying uncertainties and identifying sources of discrepancies between component estimates is extremely useful for various applications in climate research. This effort involves several tens of scientists from about 50 research teams/institutions worldwide (www.wcrp-climate.org/grand-challenges/gc-sea-level, last access: 22 August 2018). The results presented in this paper are a synthesis of the first assessment performed during 2017–2018. We present estimates of the altimetry-based global mean sea level (average rate of 3.10.3mmyr-1 and acceleration of 0.1mmyr-2 over 1993–present), as well as of the different components of the sea-level budget (http://doi.org/10.17882/54854, last access: 22 August 2018). We further examine closure of the sea-level budget, comparing the observed global mean sea level with the sum of components. Ocean thermal expansion, glaciers, Greenland and Antarctica contribute 42 %, 21 %, 15% and 8% to the global mean sea level over the 1993–present period.We also study the sea-level budget over 2005–present, using GRACE-based ocean mass estimates instead of the sum of individual mass components. Our results demonstrate that the global mean sea level can be closed to within 0.3mmyr-1 (1). Substantial uncertainty remains for the land water storage component, as shown when examining individual mass contributions to sea level.@en

The Antarctic Ice Sheet is an important indicator of climate change and driver of sea-level rise. Here we combine satellite observations of its changing volume, flow and gravitational attraction with modelling of its surface mass balance to show that it lost 2,720 ± 1,390 billion tonnes of ice between 1992 and 2017, which corresponds to an increase in mean sea level of 7.6 ± 3.9 millimetres (errors are one standard deviation). Over this period, ocean-driven melting has caused rates of ice loss from West Antarctica to increase from 53 ± 29 billion to 159 ± 26 billion tonnes per year; ice-shelf collapse has increased the rate of ice loss from the Antarctic Peninsula from 7 ± 13 billion to 33 ± 16 billion tonnes per year. We find large variations in and among model estimates of surface mass balance and glacial isostatic adjustment for East Antarctica, with its average rate of mass gain over the period 1992-2017 (5 ± 46 billion tonnes per year) being the least certain.

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In this paper we discuss our efforts to perform precision orbit determination (POD) of CryoSat-2 which depends on Doppler and satellite laser ranging tracking data. A dynamic orbit model is set-up and the residuals between the model and the tracking data is evaluated. The average r.m.s. of the 10 second averaged Doppler tracking pass residuals is approximately 0.39 mm/s; and the average of the laser tracking pass residuals becomes 1.42 cm. We discuss three improvements that have brought the orbit accuracy to this level, it concerns the way we implement temporal gravity accelerations observed by GRACE; the implementation of ITRF2014 coordinates and velocities for the DORIS beacons and the SLR tracking sites. We also discuss an adjustment of the SLR retroreflector position within the satellite reference frame. An unexpected result is that we find a systematic difference between the median of the 10s Doppler tracking residuals which displays a statistically significant pattern in the South Atlantic Anomaly area where the median of the velocity residuals varies in the range of -0.15 to +0.15 mm/s@en

In this paper we discuss our efforts to perform precision orbit determination (POD) of CryoSat-2 which depends on Doppler and satellite laser ranging tracking data. A dynamic orbit model is set-up and the residuals between the model and the tracking data is evaluated. The average r.m.s. of the 10 s averaged Doppler tracking pass residuals is approximately 0.39 mm/s; and the average of the laser tracking pass residuals becomes 1.42 cm. There are a number of other tests to verify the quality of the orbit solution, we compare our computed orbits against three independent external trajectories provided by the CNES. The CNES products are part of the CryoSat-2 products distributed by ESA. The radial differences of our solution relative to the CNES precision orbits shows an average r.m.s. of 1.25 cm between Jun-2010 and Apr-2017. The SIRAL altimeter crossover difference statistics demonstrate that the quality of our orbit solution is comparable to that of the POE solution computed by the CNES. In this paper we will discuss three important changes in our POD activities that have brought the orbit performance to this level. The improvements concern the way we implement temporal gravity accelerations observed by GRACE; the implementation of ITRF2014 coordinates and velocities for the DORIS beacons and the SLR tracking sites. We also discuss an adjustment of the SLR retroreflector position within the satellite reference frame. An unexpected result is that we find a systematic difference between the median of the 10 s Doppler tracking residuals which displays a statistically significant pattern in the South Atlantic Anomaly (SSA) area where the median of the velocity residuals varies in the range of -0.15 to +0.15 mm/s.

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The universal altimetry toolbox, BRAT (Broadview Radar Altimetry Toolbox) which can read all previous and current altimetry missions’ data, incorporates now the capability to read the upcoming Sentinel3 L1 and L2 products. ESA endeavoured to develop and supply this capability to support the users of the future Sentinel3 SAR Altimetry Mission. BRAT is a collection of tools and tutorial documents designed to facilitate the processing of radar altimetry data. This project started in 2005 from the joint efforts of ESA (European Space Agency) and CNES (Centre National d’Etudes Spatiales), and it is freely available at http://earth.esa.int/brat. The tools enable users to interact with the most common altimetry data formats. The BratGUI is the frontend for the powerful command line tools that are part of the BRAT suite. BRAT can also be used in conjunction with MATLAB/IDL (via reading routines) or in C/C++/Fortran via a programming API, allowing the user to obtain desired data, bypassing the dataformatting hassle. BRAT can be used simply to visualise data quickly, or to translate the data into other formats such as NetCDF, ASCII text files, KML (Google Earth) and raster images (JPEG, PNG, etc.). Several kinds of computations can be done within BRAT involving combinations of data fields that the user can save for posterior reuse or using the already embedded formulas that include the standard oceanographic altimetry formulas. The Radar Altimeter Tutorial, that contains a strong introduction to altimetry, shows its applications in different fields such as Oceanography, Cryosphere, Geodesy, Hydrology among others. Included are also “use cases”, with step-by-step examples, on how to use the toolbox in the different contexts. The Sentinel3 SAR Altimetry Toolbox shall benefit from the current BRAT version. While developing the toolbox we will revamp of the Graphical User Interface and provide, among other enhancements, support for reading the upcoming S3 datasets and specific “usecases” for SAR altimetry in order to train the users and make them aware of the great potential of SAR altimetry for coastal and inland applications. As for any open source framework, contributions from users having developed their own functions are welcome. The Broadview Radar Altimetry Toolbox is a continuation of the Basic Radar Altimetry Toolbox. While developing the new toolbox we will revamp of the Graphical User Interface and provide, among other enhancements, support for reading the upcoming S3 datasets and specific “use cases” for SAR altimetry in order to train the users and make them aware of the great potential of SAR altimetry for coastal and inland applications. As for any open source framework, contributions from users having developed their own functions are welcome. The first release of the new Radar Altimetry Toolbox was published in September 2015. It incorporates the capability to read S3 products as well as the new CryoSat2 Baseline C. The second release of the Toolbox, published in October 2016, has a new graphical user interface and other visualisation improvements. The third release (January 2017) includes more features and solves issues from the previous versions.@en

The universal altimetry toolbox, BRAT (Broadview Radar Altimetry Toolbox) which can read all previous and current altimetry missions’ data, incorporates now the capability to read the upcoming Sentinel3 L1 and L2 products. ESA endeavoured to develop and supply this capability to support the users of the future Sentinel3 SAR Altimetry Mission. BRAT is a collection of tools and tutorial documents designed to facilitate the processing of radar altimetry data. This project started in 2005 from the joint efforts of ESA (European Space Agency) and CNES (Centre National d’Etudes Spatiales), and it is freely available at http://earth.esa.int/brat. The tools enable users to interact with the most common altimetry data formats. The BratGUI is the frontend for the powerful command line tools that are part of the BRAT suite. BRAT can also be used in conjunction with MATLAB/IDL (via reading routines) or in C/C++/Fortran via a programming API, allowing the user to obtain desired data, bypassing the dataformatting hassle. BRAT can be used simply to visualise data quickly, or to translate the data into other formats such as NetCDF, ASCII text files, KML (Google Earth) and raster images (JPEG, PNG, etc.). Several kinds of computations can be done within BRAT involving combinations of data fields that the user can save for posterior reuse or using the already embedded formulas that include the standard oceanographic altimetry formulas. The Radar Altimeter Tutorial, that contains a strong introduction to altimetry, shows its applications in different fields such as Oceanography, Cryosphere, Geodesy, Hydrology among others. Included are also “use cases”, with step-by-step examples, on how to use the toolbox in the different contexts. The Sentinel3 SAR Altimetry Toolbox shall benefit from the current BRAT version. While developing the toolbox we will revamp of the Graphical User Interface and provide, among other enhancements, support for reading the upcoming S3 datasets and specific “usecases” for SAR altimetry in order to train the users and make them aware of the great potential of SAR altimetry for coastal and inland applications. As for any open source framework, contributions from users having developed their own functions are welcome. The Broadview Radar Altimetry Toolbox is a continuation of the Basic Radar Altimetry Toolbox. While developing the new toolbox we will revamp of the Graphical User Interface and provide, among other enhancements, support for reading the upcoming S3 datasets and specific “use cases” for SAR altimetry in order to train the users and make them aware of the great potential of SAR altimetry for coastal and inland applications. As for any open source framework, contributions from users having developed their own functions are welcome. The first release of the new Radar Altimetry Toolbox was published in September 2015. It incorporates the capability to read S3 products as well as the new CryoSat2 Baseline C. The second release of the Toolbox, published in October 2016, has a new graphical user interface and other visualisation improvements. The third release (January 2017) includes more features and solves issues from the previous versions.@en

With the advent of the new reference system ITRF2014 we have modified our precision orbit determination procedures so that all CryoSat-2 trajectories are now realized in the new reference system which provides a consistent survey of DORIS and SLR station coordinates. For SLR stations in ITRF2014 we take the solution from the SINEX files, there is no need to estimate SLR station coordinates. This is also the case for most DORIS beacons although some beacons require us to estimate station positions since they are not in ITRF2014. Preliminary results show that the DORIS residuals are consistent at 0.4 mm/s and that the SLR residuals are around to 1.5 cm. The level of unexplained accelerations with the presently used dynamic models is around 3.6 nanometer per second squared in the flight direction, for traverse track accelerations it is around 10 nm/s2. Crossover residuals of the CryoSat-2 altimeter typically reduce to under 5 cm, an independent comparison to the CNES precision orbit solutions yields radial differences of around 1.5 cm.@en

In this study, we use satellite gravimetry data from the Gravity Recovery and Climate Experiment (GRACE) to estimate regional mass change of the Greenland ice sheet (GrIS) and neighboring glaciated regions using a least squares inversion approach. We also consider results from the input–output method (IOM). The IOM quantifies the difference between the mass input and output of the GrIS by studying the surface mass balance (SMB) and the ice discharge (D). We use the Regional Atmospheric Climate Model version 2.3 (RACMO2.3) to model the SMB and derive the ice discharge from 12 years of high-precision ice velocity and thickness surveys. We use a simulation model to quantify and correct for GRACE approximation errors in mass change between different subregions of the GrIS, and investigate the reliability of pre-1990s ice discharge estimates, which are based on the modeled runoff. We find that the difference between the IOM and our improved GRACE mass change estimates is reduced in terms of the long-term mass change when using a reference discharge derived from runoff estimates in several subareas. In most regions our GRACE and IOM solutions are consistent with other studies, but differences remain in the northwestern GrIS. We validate the GRACE mass balance in that region by considering several different GIA models and mass change estimates derived from data obtained by the Ice, Cloud and land Elevation Satellite (ICESat). We conclude that the approximated mass balance between GRACE and IOM is consistent in most GrIS regions. The difference in the northwest is likely due to underestimated uncertainties in the IOM solutions.@en

Driven by wide interest among TU Delft (Delft University of Technology) students to acquire focussed knowledge on space engineering, missions and planetary exploration, a new spaceflight minor was developed for the minor program of the university. With its minor program, TU Delft affords its students an opportunity to dedicate the first semester of their 3rd BSc year to a set of courses chosen among the numerous options offered specifically by the TU Delft or another university. Students are not only allowed but encouraged to explore topics and study fields outside their main BSc track. The spaceflight minor is designed as a multidisciplinary, thematic program, in which the students gain insight in the demand for space applications, mission analysis, system requirements and sizing. This multidisciplinary setup is facilitated by the recently established TU Delft Space Institute (DSI), of which all the faculties involved in the minor are members. The minor and the DSI provide a unique opportunity to strengthen space education and research across TU Delft. The minor covers two quarters of the academic year, spanning twenty weeks, and includes six courses. Offered in the first quarter are: Introduction to Spaceflight (for students without Aerospace Engineering background) or Electronic Circuits (for the other students); Space Exploration, with basics and examples of planetary and astronomical exploration and an introduction to space law; Earth Observation, covering basics of remote sensing of the Earth. The second quarter includes: Spacecraft Technology, providing an overview of the technology of spacecraft subsystems with emphasis on small satellites; Satellite Tracking & Communication, on telecommunications, ground station operations and telemetry analysis from a theoretical and practical point of view; Spaceflight Assignment, the final project in which students produce real, small-scale space deliverables, and reflect on the process and results of development and analysis in the complex space engineering and scientific environment. In total, 15 lecturers from three TU Delft faculties and one from Leiden University contributed to the minor. Many of the courses employ innovative education techniques, such as flipped classrooms and videos produced by the lecturers. Some courses are simultaneously offered to campus students and external participants in a full online format. The first edition of the minor, delivered from September 2015 to January 2016 to 44 students from various TU Delft faculties, can be considered a success with excellent feedback from participants. The paper elaborates on the minor design and learning objectives, showing how multidisciplinary, innovative education can be effectively implemented for students with different academic backgrounds. @en

We estimate the total land water storage (LWS) change between 2003 and 2013 using a global water mass budget approach. Hereby we compare the ocean mass change (estimated from GRACE space gravimetry on the one hand, and from the satellite altimetry-based global mean sea level corrected for steric effects on the other hand) to the sum of the main water mass components of the climate system: glaciers, Greenland and Antarctica ice sheets, atmospheric water and LWS(the latter being the unknown quantity to be estimated). For glaciers and ice sheets, we use published estimates of ice mass trends based on various types of observations covering different time spans between 2003 and 2013. From the mass budget equation, we derive a net LWS trend over the study period. The mean trend amounts to+0.30 0.18mmyr1 in sea level equivalent. This corresponds to a net decrease of 108 64 km3 yr1 in LWS over the 2003–2013 decade. We also estimate the rate of change in LWS and find no significant acceleration over the study period. The computed mean global LWS trend over the study period is shown to be explained mainly by direct anthropogenic effects on land hydrology, i.e. the net effect of groundwater depletion and impoundment of water in man-made reservoirs, and to a lesser extent the effect of naturally-forced land hydrology variability. Our results compare well with independent estimates of human-induced changes in global land hydrology. @en