ALBERT

Description: Although the knowledge of the gravity of the Earth has improved considerably with CHAMP, GRACE, and GOCE (see appendices for a list of abbreviations) satellite missions, the geophysical community has identified the need for the continued monitoring of the time-variable component with the purpose of estimating the hydrological and glaciological yearly cycles and long-term trends. Currently, the GRACE-FO satellites are the sole dedicated provider of these data, while previously the GRACE mission fulfilled that role for 15 years. There is a data gap spanning from July 2017 to May 2018 between the end of the GRACE mission and start the of GRACE-FO, while the Swarm satellites have collected gravimetric data with their GPS receivers since December 2013. We present high-quality gravity field models (GFMs) from Swarm data that constitute an alternative and independent source of gravimetric data, which could help alleviate the consequences of the 10-month gap between GRACE and GRACE-FO, as well as the short gaps in the existing GRACE and GRACE-FO monthly time series. The geodetic community has realized that the combination of different gravity field solutions is superior to any individual model and set up the Combination Service of Time-variable Gravity Fields (COST-G) under the umbrella of the International Gravity Field Service (IGFS), part of the International Association of Geodesy (IAG). We exploit this fact and deliver the highest-quality monthly GFMs, resulting from the combination of four different gravity field estimation approaches. All solutions are unconstrained and estimated independently from month to month. We tested the added value of including kinematic baselines (KBs) in our estimation of GFMs and conclude that there is no significant improvement. The non-gravitational accelerations measured by the accelerometer on board Swarm C were also included in our processing to determine if this would improve the quality of the GFMs, but we observed that is only the case when the amplitude of the non-gravitational accelerations is higher than during the current quiet period in solar activity. Using GRACE data for comparison, we demonstrate that the geophysical signal in the Swarm GFMs is largely restricted to spherical harmonic degrees below 12. A 750 km smoothing radius is suitable to retrieve the temporal variations in Earth's gravity field over land areas since mid-2015 with roughly 4 cm equivalent water height (EWH) agreement with respect to GRACE. Over ocean areas, we illustrate that a more intense smoothing with 3000 km radius is necessary to resolve large-scale gravity variations, which agree with GRACE roughly at the level of 1 cm EWH, while at these spatial scales the GRACE observes variations with amplitudes between 0.3 and 1 cm EWH. The agreement with GRACE and GRACE-FO over nine selected large basins under analysis is 0.91 cm, 0.76 cm yr−1, and 0.79 in terms of temporal mean, trend, and correlation coefficient, respectively. The Swarm monthly models are distributed on a quarterly basis at ESA's Earth Swarm Data Access (at https://swarm-diss.eo.esa.int/, last access: 5 June 2020, follow Level2longterm and then EGF) and at the International Centre for Global Earth Models (http://icgem.gfz-potsdam.de/series/02_COST-G/Swarm, last access: 5 June 2020), as well as identified with the DOI https://doi.org/10.5880/ICGEM.2019.006 (Encarnacao et al., 2019).

Description: The capabilities of radar altimetry to measure inland water bodies are well established, and several river altimetry datasets are available. Here we produced a globally distributed dataset, the Global River Radar Altimeter Time Series (GRRATS), using Envisat and Ocean Surface Topography Mission (OSTM)/Jason-2 radar altimeter data spanning the time period 2002–2016. We developed a method that runs unsupervised, without requiring parameterization at the measurement location, dubbed virtual station (VS) level, and applied it to all altimeter crossings of ocean-draining rivers with widths 〉900 m (〉34 % of the global drainage area). We evaluated every VS, either quantitatively for VS locations where in situ gages are available or qualitatively using a grade system. We processed nearly 1.5 million altimeter measurements from 1478 VSs. After quality control, the final product contained 810 403 measurements distributed over 932 VSs located on 39 rivers. Available in situ data allowed quantitative evaluation of 389 VSs on 12 rivers. The median standard deviation of river elevation error is 0.93 m, Nash–Sutcliffe efficiency is 0.75, and correlation coefficient is 0.9. GRRATS is a consistent, well-documented dataset with a user-friendly data visualization portal, freely available for use by the global scientific community. Data are available at https://doi.org/10.5067/PSGRA-SA2V1 (Coss et al., 2016).

Description: Remote sensing and satellite geodetic observations are capable of hydrologic monitoring of freshwater resources. Although satellite radar altimetry has been used in monitoring water level or discharge, its use is often limited to monitoring large rivers (〉1 km) with longer interval periods (〉1 week) because of its low temporal and spatial resolutions (i.e., satellite revisit period). Several studies have reported successful retrieval of water levels for small rivers as narrow as 40 m. However, processing current satellite altimetry signals for such small water bodies to retrieve water levels accurately remains challenging. Physically, the radar signal returned by water bodies smaller than the satellite footprint is most likely contaminated by non-water surfaces, which may degrade the measurement quality. In order to address this scientific challenge, we carefully selected the waveform shapes corresponding to the range measurement resulting from standard retrackers for the European Space Agency's (ESA's) Envisat (Environmental Satellite) radar altimetry. We applied this approach to small (40–200 m in width) and medium-sized (200–800 m in width) rivers and small lakes (extent

Description: Remote sensing and satellite geodetic observations are capable for hydrologic monitoring of freshwater resources. For the case of satellite radar altimetry, limited temporal resolutions (e.g., satellite revisit period) prohibit the use of this method for a short (〈 weekly) interval monitoring of water level or discharge. On the other hand, the current satellite radar altimeter footprints limit the water level measurement for rivers wider than 1 km. Some studies indeed reported successful retrieval of water level for small-size rivers as narrow as 80 m; however, the processing of current satellite altimetry signals for small water bodies to retrieve accurate water levels, remains challenging. To address this scientific challenge, this study tries to monitor small (40–200 m width) and medium-sized (200–800 m width) rivers and lakes using satellite altimetry through identification and choice of the over-water radar waveforms corresponding to the appropriately waveform-retracked water level. This study addresses the humid tropics of Southeast Asia, specifically in Indonesia, where similar studies do not yet exist and makes use Level 2 radar altimeter measurements generated by European Space Agency's (ESA's) Envisat (Environmental Satellite) mission. This experiment proves that satellite altimetry provides a good alternative, or the only means in some regions, to measure the water level of medium-sized river (200–800 m width) and small lake (extent 〈 1000 km2) in Southeast Asia humid tropic with reasonable accuracy. In addition, the procedure to choose retracked Envisat altimetry water level heights via identification or selection of standard waveform shapes for inland water is recommended and should be a standard measure especially over small rivers and lakes. This study also found that Ice-1 is not necessarily the best retracker as reported by previous studies, among the four standard waveform retracking algorithms for Envisat radar altimetry observing inland water bodies.

Description: The capabilities of radar altimetry to measure inland water bodies are well established and several river altimetry datasets are available. Here we produced a globally-distributed dataset, the Global River Radar Altimeter Time Series (GRRATS), using Envisat and Ocean Surface Topography Mission (OSTM)/Jason-2 radar altimeter data spanning the time period 2002–2016. We developed a method that runs unsupervised, without requiring parameterization at the measurement location, dubbed virtual station (VS) level and applied it to all altimeter crossings of ocean draining rivers with widths 〉 900 m (〉 34 % of global drainage area). We evaluated every VS, either quantitatively for VS where in-situ gages are available, or qualitatively using a grade system. We processed nearly 1.5 million altimeter measurements from 1,478 VS. After quality control, the final product contained 810,403 measurements distributed over 932 VS located on 39 rivers. Available in-situ data allowed quantitative evaluation of 389 VS on 12 rivers. Median standard deviation of river elevation error is 0.93 m, Nash-Sutcliffe efficiency is 0.75, and correlation coefficient is 0.9. GRRATS is a consistent, well-documented dataset with a user-friendly data visualization portal, freely available for use by the global scientific community. Data are available at DOI 10.5067/PSGRA-SA2V1 (Durand et al., 2016).

Description: The gravity field dedicated satellite missions like CHAMP, GRACE, and GOCE are supposed to map the Earth’s global gravity field with unprecedented accuracy and resolution. New models of Earth’s static and time-variable gravity field will be available every month as one of the science products from GRACE. Here we present an alternative method to estimate the gravity field efficiently using the in situ satellite-to-satellite observations at the altitude and show results on static as well as temporal gravity field recovery. Considering the energy relation between the kinetic energy of the satellite and the gravitational potential, the disturbing potential difference observations can be computed from the orbital parameter vectors in the inertial frame, using the high-low GPS-LEO GPS tracking data, the low-low satelliteto- satellite GRACE measurements, and data from 3-axis accelerometers (Jekeli, 1999). The disturbing potential observation also includes other potentials due to tides, atmosphere, other modeled signals (e.g. N-body) and the geophysical fluid signals (hydrological and oceanic mass variations), which should be recoverable from GRACE mission with a monthly resolution. The simulation results confirm that monthly geoid accuracy is expected to be a few cm with the 160 km resolution (up to degree and order 120) once other corrections are made accurately. The time-variable geoids (ocean and ground water mass) might be recovered with a noise-to-signal ratio of 0.1 with the resolution of 800 km every month assuming no temporal aliasing.Key words. GRACE mission, Energy integral, Geopotential, Satellite-to-satellite tracking, Temporal gravity field

Description: Ice velocity constitutes a key parameter for estimating ice-sheet discharge rates and is crucial for improving coupled models of the Antarctic ice sheet to accurately predict its future fate and contribution to sea-level change. Here, we present a new Antarctic ice velocity map at a 100-m grid spacing inferred from Landsat 8 imagery data collected from December 2013 through March 2016 and robustly processed using the feature tracking method. These maps were assembled from over 73,000 displacement vector scenes inferred from over 32,800 optical images. Our maps cover nearly all the ice shelves, landfast ice, ice streams, and most of the ice sheet. The maps have an estimated uncertainty of less than 10 m yr-1 based on robust internal and external validations. These datasets will allow for a comprehensive continent-wide investigation of ice dynamics and mass balance combined with the existing and future ice velocity measurements and provide researchers access to better information for monitoring local changes in ice glaciers. Other uses of these datasets include control and calibration of ice-sheet modelling, developments in our understanding of Antarctic ice-sheet evolution, and improvements in the fidelity of projects investigating sea-level rise (https://doi.pangaea.de/10.1594/PANGAEA.895738).