European Gravity Service for Improved Emergency Management (EGSIEM)—from concept to implementation

Jäggi, Adrian; Weigelt, Matthias; Flechtner, Frank; Güntner, Andreas; Mayer‐Gürr, Torsten; Martinis, Sandro; Bruinsma, Sean; Flury, Jakob; Bourgogne, Stéphane; Steffen, Holger; Meyer, Ulrich; Jean, Yoomin; Sušnik, Andreja; Grahsl, Andrea; Arnold, Daniel; Cann-Guthauser, Keith; Dach, Rolf; Li, Z.; Chen, Qiang; Dam, Tonie van; Gruber, Christian; Poropat, Lea; Gouweleeuw, Ben; Kvas, Andreas; Klinger, Beate; Lemoine, J.M.; Biancale, R.; Zwenzner, Hendrik; Bandikova, Tamara; Shabanloui, Akbar

doi:10.1093/gji/ggz238

Cited by 35 publications

(26 citation statements)

References 61 publications

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“…It is foreseen to continue the combination of SLR data, taking into account the contributions by different analysis centers, and the combination of SLR with Swarm and possibly also GRACE-FO on the normal equation level in the frame of the Combination Service for Time-variable Gravity field models (COST-G) [34,52], the newly established product center of the International Gravity Field Service (IGFS) under the umbrella of the International Association of Geodesy (IAG).…”

Section: Discussionmentioning

confidence: 99%

“…In order to reduce the absorption of gravity field signal by these parameters, their frequency has to be tailored to the orbit altitude and observation sampling of the specific satellite and their magnitude has to be limited by constraints depending on the satellites' environment at orbit altitude. By extensive cross-validation with other analysis centers in the frame of the European Gravity Service for Improved Emergency Management (EGSIEM) [52] project, it could be shown that mass trends and the amplitude of seasonal variations in the AIUB gravity fields are not affected by the pseudo-stochastic parameterization. Since a separate determination of sub-groups of the parameter space leads to a regularization favoring the a priori force model applied [53] orbit, stochastic and force model parameters have to be determined together in one adjustment process.…”

Section: Methodsmentioning

confidence: 99%

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SLR, GRACE and Swarm Gravity Field Determination and Combination

et al. 2019

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Satellite gravimetry allows for determining large scale mass transport in the system Earth and to quantify ice mass change in polar regions. We provide, evaluate and compare a long time-series of monthly gravity field solutions derived either by satellite laser ranging (SLR) to geodetic satellites, by GPS and K-band observations of the GRACE mission, or by GPS observations of the three Swarm satellites. While GRACE provides gravity signal at the highest spatial resolution, SLR sheds light on mass transport in polar regions at larger scales also in the pre- and post-GRACE era. To bridge the gap between GRACE and GRACE Follow-On, we also derive monthly gravity fields using Swarm data and perform a combination with SLR. To correctly take all correlations into account, this combination is performed on the normal equation level. Validating the Swarm/SLR combination against GRACE during the overlapping period January 2015 to June 2016, the best fit is achieved when down-weighting Swarm compared to the weights determined by variance component estimation. While between 2014 and 2017 SLR alone slightly overestimates mass loss in Greenland compared to GRACE, the combined gravity fields match significantly better in the overlapping time period and the RMS of the differences is reduced by almost 100 Gt. After 2017, both SLR and Swarm indicate moderate mass gain in Greenland.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

SLR, GRACE and Swarm Gravity Field Determination and Combination

et al. 2019

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“…The individual GFMs are combined in the frame of the Combination Service of Time-variable Gravity Fields (COST-G) of the IGFS, applying the methods developed in the frame of the EGSIEM (Jäggi et al, 2019). We derive VCE weights in order to produce the combined GFMs from the individual GFMs produced at AIUB, ASU, IfG and OSU.…”

Section: Combinationmentioning

confidence: 99%

“…The operational activities currently under way pertaining to the combined models described in this article are conducted in the frame of the Combination Service of Time-variable Gravity Fields (COST-G), under the umbrella of International Association of Geodesy (IAG)'s International Gravity Field Service (IGFS) (Jäggi et al, 2019), with additional support from the Swarm Data, Innovation and Science Cluster (DISC) and funded by ESA. The Swarm monthly models are distributed on a quarterly basis at ESA's Earth Online Swarm Data Access (at https://swarm-diss.eo.esa.int/#swarm, follow Level2longterm and then EGF) and at the International Centre for Global Earth Models (http://icgem.gfz-potsdam.de/series/03_COST-G/Swarm), as well as identified with the DOI 10.5880/ICGEM.2019.006 (Encarnacao et al, 2019).…”

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confidence: 99%

Multi-approach gravity field models from Swarm GPS data

Encarnação

Visser

Arnold

et al. 2019

Preprint

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Abstract. Although the knowledge of the gravity of the Earth has improved considerably with CHAMP, GRACE and GOCE 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 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 a 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 to the highest quality monthly-independent gravity field models, 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 Gravity Field Models (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 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 gravity field models is largely restricted to Spherical Harmonic degrees below 12. A 750 km smoothing radius is suitable to retrieve the temporal variations of Earth’s gravity field over land areas since mid-2015 with roughly 4 cm Equivalent Water Height (EWH) agreement with respect to a GRACE-derived parametric model. 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 the aforementioned parametric model under 2 cm EWH, while at these spatial scales the model represents variations with amplitudes between 2 and 3.5 cm EWH. The agreement with GRACE and GRACE-FO over nine selected large basins under analyses is 1.19 cm, 0.60 cm/year and 0.75 in terms of temporal mean, trend and correlation coefficient, respectively.

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“…For an operational use of GRACE TWSA in seasonal forecasts, the data need to be provided in near-real time (NRT). Given the recent development of a NRT service for GRACE products within the European Gravity Service for Improved Emergency Management EGSIEM (Jäggi et al (2019)), such data are expected to be soon available based on the GRACE-Follow On mission, which was launched in May 2018. Our study thus paves the road for a novel application of satellite gravity data in hydrological forecasting, possibly in several regions worldwide, including chronically data scarce areas.…”

Section: Discussionmentioning

confidence: 99%

Forecast of seasonal water availability in Central Asia with near-real time GRACE water storage anomalies

Apel

Gouweleeuw

Gafurov

et al. 2019

Environ. Res. Commun.

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Water availability during summer in Central Asia is controlled by the snow melt in the surrounding mountains. Reliable forecasts of river discharge during this period are essential for the management of water resources. This study tests the predictive power of GRACE gravity-based water storage anomalies in a linear regression framework for two large catchments. The results show substantial improvements of the forecasts in the larger Amudarya catchment compared to forecasts using just climate, snow cover, and discharge data. In this catchment, GRACE water storage anomalies even provide the largest share of explained variance. This leads to the conclusion that GRACE data can improve the forecast of seasonal water availability for large basins in Central Asia. The GRACE-FO mission launched in May 2018 opens up the possibility of operational forecasts utilizing upcoming near-real time products from satellite gravimetry for Central Asia and similar environments.

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European Gravity Service for Improved Emergency Management (EGSIEM)—from concept to implementation

Cited by 35 publications

References 61 publications

SLR, GRACE and Swarm Gravity Field Determination and Combination

SLR, GRACE and Swarm Gravity Field Determination and Combination

Multi-approach gravity field models from Swarm GPS data

Forecast of seasonal water availability in Central Asia with near-real time GRACE water storage anomalies

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