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  • 1
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    TND-IGG RL01: Thermospheric neutral density from accelerometer measurements of GRACE, CHAMP and Swarm (2021)
    PANGAEA
    Details
    Publication Date: 2024-04-20
    Description: TND-IGG RL01: This dataset is the first release of thermospheric neutral densities (TND) processed at the Institute of Geodesy and Geoinformation (IGG), University of Bonn, Germany. TNDs are derived from accelerometer measurements of the satellites GRACE-A, CHAMP and Swarm-C. For GRACE-A and CHAMP we first calibrate the accelerometer data within a precise orbit determination procedure (Vielberg et al., 2018). For Swarm-C we use the calibrated along-track accelerations from ESA (Siemes et al., 2016). In a second step, solar and Earth radiation pressure accelerations according to Vielberg and Kusche (2020) are reduced from the calibrated accelerometer data. The resulting atmospheric drag is then related to the thermospheric neutral density following the direct procedure by Doornbos et al. (2010) with temperature and density of atmospheric constituents from the empirical model NRLMSIS2.0. We apply an accommodation coefficient of 0.93 for GRACE, 0.82 for Swarm and 0.85 for CHAMP. Detailed information about the processing can be found in the ReadMe.txt and in Vielberg et al. (2021, in review). The final thermospheric neutral densities with a temporal resolution of 10 seconds are provided as monthly netCDF files.
    Keywords: accelerometer; Accelerometer; ACCM; Binary Object; Binary Object (File Size); Binary Object (Media Type); CHAMP; GRACE; mass density; neutral density; satellite data; Swarm; thermopshere
    Type: Dataset
    Format: text/tab-separated-values, 3 data points
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    DATA PANGAEA
  • 2
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    Comparison of accelerometer data calibration methods used in thermospheric neutral density estimation (2018)
    Copernicus
    Details
    Publication Date: 2018-05-18
    Description: Ultra-sensitive space-borne accelerometers on board of low Earth orbit (LEO) satellites are used to measure non-gravitational forces acting on the surface of these satellites. These forces consist of the Earth radiation pressure, the solar radiation pressure and the atmospheric drag, where the first two are caused by the radiation emitted from the Earth and the Sun, respectively, and the latter is related to the thermospheric density. On-board accelerometer measurements contain systematic errors, which need to be mitigated by applying a calibration before their use in gravity recovery or thermospheric neutral density estimations. Therefore, we improve, apply and compare three calibration procedures: (1) a multi-step numerical estimation approach, which is based on the numerical differentiation of the kinematic orbits of LEO satellites; (2) a calibration of accelerometer observations within the dynamic precise orbit determination procedure and (3) a comparison of observed to modeled forces acting on the surface of LEO satellites. Here, accelerometer measurements obtained by the Gravity Recovery And Climate Experiment (GRACE) are used. Time series of bias and scale factor derived from the three calibration procedures are found to be different in timescales of a few days to months. Results are more similar (statistically significant) when considering longer timescales, from which the results of approach (1) and (2) show better agreement to those of approach (3) during medium and high solar activity. Calibrated accelerometer observations are then applied to estimate thermospheric neutral densities. Differences between accelerometer-based density estimations and those from empirical neutral density models, e.g., NRLMSISE-00, are observed to be significant during quiet periods, on average 22 % of the simulated densities (during low solar activity), and up to 28 % during high solar activity. Therefore, daily corrections are estimated for neutral densities derived from NRLMSISE-00. Our results indicate that these corrections improve model-based density simulations in order to provide density estimates at locations outside the vicinity of the GRACE satellites, in particular during the period of high solar/magnetic activity, e.g., during the St. Patrick's Day storm on 17 March 2015.
    Print ISSN: 0992-7689
    Electronic ISSN: 1432-0576
    Topics: Geosciences , Physics
    Published by Copernicus on behalf of European Geosciences Union.
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  • 3
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    Time-variable gravity fields and ocean mass change from 37 months of kinematic Swarm orbits (2017)
    Copernicus
    Details
    Publication Date: 2017-11-27
    Description: Measuring the spatiotemporal variation of ocean mass allows one to partition volumetric sea level change, sampled by radar altimeters, into a mass-driven and a steric part, the latter being related to ocean heat change and the current Earth’s energy imbalance. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) mission provides estimates of the Earth’s time-variable gravity field, from which one can derive ocean mass variability. However, GRACE has reached the end of its lifetime with data degradation and several gaps during the last years, and there will be a prolonged gap until the launch of the follow-on mission GRACE-FO. Therefore, efforts focus on generating a long and consistent ocean mass time series by analyzing kinematic orbits from other low-flying satellites; i.e. extending the GRACE time series. Here we utilize data from the European Space Agency’s (ESA) Swarm Earth Explorer satellites to derive and investigate ocean mass variations. We investigate the potential to bridge the gap between the GRACE missions and to substitute missing monthly solutions. Our monthly Swarm solutions have a root mean square error (RMSE) of 4.0 mm with respect to GRACE, whereas directly estimating trend, annual and semiannual signal terms leads to an RMSE of only 1.7 mm. Concerning monthly gaps, our Swarm solution appears better than interpolating existing GRACE data in 13.5 % of all cases, for 80.0 % of all investigated cases of an 18-months-gap, Swarm ocean mass was found closer to the observed GRACE data compared to interpolated GRACE data. Furthermore, we show that precise modelling of non-gravitational forces acting on the Swarm satellites is the key for reaching these accuracies. Our results have implications for sea level budget studies, but they may also guide further research in gravity field analysis schemes, including non-dedicated satellites.
    Electronic ISSN: 1869-9537
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 4
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    Time-variable gravity fields and ocean mass change from 37 months of kinematic Swarm orbits (2018)
    Copernicus
    Details
    Publication Date: 2018-03-23
    Description: Measuring the spatiotemporal variation of ocean mass allows for partitioning of volumetric sea level change, sampled by radar altimeters, into mass-driven and steric parts. The latter is related to ocean heat change and the current Earth's energy imbalance. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) mission has provided monthly snapshots of the Earth's time-variable gravity field, from which one can derive ocean mass variability. However, GRACE has reached the end of its lifetime with data degradation and several gaps occurred during the last years, and there will be a prolonged gap until the launch of the follow-on mission GRACE-FO. Therefore, efforts focus on generating a long and consistent ocean mass time series by analyzing kinematic orbits from other low-flying satellites, i.e. extending the GRACE time series. Here we utilize data from the European Space Agency's (ESA) Swarm Earth Explorer satellites to derive and investigate ocean mass variations. For this aim, we use the integral equation approach with short arcs (Mayer-Gürr, 2006) to compute more than 500 time-variable gravity fields with different parameterizations from kinematic orbits. We investigate the potential to bridge the gap between the GRACE and the GRACE-FO mission and to substitute missing monthly solutions with Swarm results of significantly lower resolution. Our monthly Swarm solutions have a root mean square error (RMSE) of 4.0 mm with respect to GRACE, whereas directly estimating constant, trend, annual, and semiannual (CTAS) signal terms leads to an RMSE of only 1.7 mm. Concerning monthly gaps, our CTAS Swarm solution appears better than interpolating existing GRACE data in 13.5 % of all cases, when artificially removing one solution. In the case of an 18-month artificial gap, 80.0 % of all CTAS Swarm solutions were found closer to the observed GRACE data compared to interpolated GRACE data. Furthermore, we show that precise modeling of non-gravitational forces acting on the Swarm satellites is the key for reaching these accuracies. Our results have implications for sea level budget studies, but they may also guide further research in gravity field analysis schemes, including satellites not dedicated to gravity field studies.
    Print ISSN: 1869-9510
    Electronic ISSN: 1869-9529
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 5
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    Comparison of Data‐Driven Techniques to Reconstruct (1992–2002) and Predict (2017–2018) GRACE‐Like Gridded Total Water Storage Changes Using Climate Inputs (2020)
    American Geophysical Union
    Details
    Publication Date: 2020-05-01
    Print ISSN: 0043-1397
    Electronic ISSN: 1944-7973
    Topics: Architecture, Civil Engineering, Surveying , Geography
    Published by American Geophysical Union
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    PAPER CURRENT
  • 6
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    IGG-Swarm: Temporal Gravity Models from Swarm (2021)
    GFZ Data Services
    Details
    Publication Date: 2022-01-21
    Description: Abstract
    Description: Monthly gravity fields from Swarm A, B, and C, using the integral equation approach with short arcs. Software: GROOPS; Approach: Short-arc approach (Mayer-Gürr, 2006); Kinematic orbit product: IfG Graz: https://ftp.tugraz.at/outgoing/ITSG/satelliteOrbitProducts/operational/Swarm-1/kinematicOrbit/; Arc length: 45 minutes; Reference GFM: GOCO06s (Kvas et. al, 2021), monthly mean has been added back to the solution; Drag model: NRLMSIS2; SRP and EARP and EIRP models: Vielberg & Kusche (2020); Empirical parameters: + for non-gravitational accelerations (sum of Drag+SRP+EIRP+EARP): Bias per arc and direction; + for Drag: Scale per arc and direction; + for radiation pressure (sum of SRP+EIRP+EARP): Scale per day and direction; Non-tidal model: Atmosphere and Ocean De-aliasing Level 1B RL06 (Dobslaw et al., 2017); Ocean tidal model: 2014 finite element solution FES2014b (Carrere et al., 2015); Atmospheric tidal model: AOD1B RL06 atmospheric tides ; Solid Earth tidal model: IERS2010; Pole tidal model: IERS2010; Ocean pole tidal model: IERS2010 (Desai 2002); Third-body perturbations: Sun, Moon, Mercury, Venus, Mars, Jupiter, and Saturn, following the JPL DE421 Planetary and Lunar Ephemerides (Folkner et al., 2014).
    Keywords: Swarm ; monthly gravity field model ; ICGEM ; geodesy ; global gravity field model ; EARTH SCIENCE 〉 SOLID EARTH 〉 GEODETICS ; EARTH SCIENCE 〉 SOLID EARTH 〉 GRAVITY/GRAVITATIONAL FIELD
    Type: Dataset , Dataset
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    DATA GFZ
  • 7
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    Variability of the oceanic bottom pressure from sensor observations and ocean models (2017)
    Geophysical Research Abstracts Vol. 19, EGU2017-17040-1, 2017
    In:  EPIC3EGU General Assembly 2017, 2017-04-23-2017-04-28EGU General Assembly 2017, Geophysical Research Abstracts Vol. 19, EGU2017-17040-1, 2017
    Details
    Publication Date: 2017-10-04
    Description: We discuss time series of ocean bottom pressure (OBP) computed by the Finite Element Sea Ice-Ocean Model (FESOM) driven by realistic forcing. The influence of atmospheric pressure and mesoscale eddies on the OBP and surface height anomalies on time scales up to years was investigated. Also, we estimated space and time scales of mass variability simulated by both climate-type (resolution about 1 degree) and eddy resolving (down to about 10 km) versions of the model. We analyze the African sector of the Southern Ocean. A part of the OBP variance there is associated with eddy activity (especially in the Agulhas region) and explore its respective contribution. Assessment of averaging interval of simulated data for the purpose of minimizing aliasing in variability of OBP is additionally carried out. An important aspect of this study is the comparison of modeled and in situ OBP records. High frequency measurements of OBP with sub-daily resolution available from Pressure Inverted Echo Sounders (PIES) used to infer temporal co-spectra of OBP variability. The PIES are placed along the prime meridian south of Africa can be used to evaluate variations of both barotropic and baroclinic geostrophic transport fluctuations of the Antarctic Circumpolar Current and verify corresponding GRACE estimates. The distance between PIES stations is chosen to resolve the major oceanic fronts for this region, which allows us to compare co-spectra of observed and simulated OBP variability. Variability of the oceanic bottom pressure from sensor observations and ocean models. Available from: https://www.researchgate.net/publication/316789208_Variability_of_the_oceanic_bottom_pressure_from_sensor_observations_and_ocean_models [accessed May 10, 2017].
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
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  • 8
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    Variability of the oceanic bottom pressure from sensor observations and ocean models (2017)
    EGU
    In:  EPIC3European Geosciences Union General Assembly Vienna | Austria | 23–28 April 2017, 2017-04-23Vienna, EGU
    Details
    Publication Date: 2017-07-10
    Description: We discuss time series of ocean bottom pressure (OBP) computed by the Finite Element Sea Ice-Ocean Model (FESOM) driven by realistic forcing. The influence of atmospheric pressure and mesoscale eddies on the OBP and surface height anomalies on time scales up to years was investigated. Also, we estimated space and time scales of mass variability simulated by both climate-type (resolution about 1 degree) and eddy resolving (down to about 10km) versions of the model. We analyze the African sector of the Southern Ocean. A part of the OBP variance there is associated with eddy activity (especially in the Agulhas region) and explore its respective contribution. Assessment of averaging interval of simulated data for the purpose of minimizing aliasing in variability of OBP is additionally carried out. An important aspect of this study is the comparison of modeled and in situ OBP records. High frequency measurements of OBP with sub-daily resolution available from Pressure Inverted Echo Sounders (PIES) used to infer temporal co-spectra of OBP variability. The PIES are placed along the prime meridian south of Africa can be used to evaluate variations of both barotropic and baroclinic geostrophic transport fluctuations of the Antarctic Circumpolar Current and verify corresponding GRACE estimates. The distance between PIES stations is chosen to resolve the major oceanic fronts for this region, which allows us to compare co-spectra of observed and simulated OBP variability. A contribution of DFG SPP 1788 and 1257
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
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  • 9
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    Comparison of Data-Driven Techniques to Reconstruct (1992–2002) and Predict (2017–2018) GRACE-Like Gridded Total Water Storage Changes Using Climate Inputs (2021)
    Details
    Publication Date: 2021-11-03
    Description: The Gravity Recovery and Climate Experiment (GRACE) mission ended its operation in October 2017, and the GRACE Follow-On mission was launched only in May 2018, leading to approximately 1 year of data gap. Given that GRACE-type observations are exclusively providing direct estimates of total water storage change (TWSC), it would be very important to bridge the gap between these two missions. Furthermore, for many climate-related applications, it is also desirable to reconstruct TWSC prior to the GRACE period. In this study, we aim at comparing different data-driven methods and identifying the more robust alternatives for predicting GRACE-like gridded TWSC during the gap and reconstructing them to 1992 using climate inputs. To this end, we first develop a methodological framework to compare different methods such as the multiple linear regression (MLR), artificial neural network (ANN), and autoregressive exogenous (ARX) approaches. Second, metrics are developed to measure the robustness of the predictions. Finally, gridded TWSC within 26 regions are predicted and reconstructed using the identified methods. Test computations suggest that the correlation of predicted TWSC maps with observed ones is more than 0.3 higher than TWSC simulated by hydrological models, at the grid scale of 1° resolution. Furthermore, the reconstructed TWSC correctly reproduce the El Nino-Southern Oscillation (ENSO) signals. In general, while MLR does not perform best in the training process, it is more robust and could thus be a viable approach both for filling the GRACE gap and for reconstructing long-period TWSC fields globally when combined with statistical decomposition techniques.
    Keywords: 551.48 ; GRACE ; total water storage change ; predidicting method
    Language: English
    Type: map
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    CITATION GEO-LEO
  • 10
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    Ocean bottom pressure variability: Which part can be reliably modeled? (2018)
    Geophysical Research Abstracts. Vol. 20, EGU2018-9158, 2018
    In:  EPIC3EGU General Assembly 2018, Vienna, 2018-04-09-2018-04-13EGU General Assembly 2018, Geophysical Research Abstracts. Vol. 20, EGU2018-9158, 2018
    Details
    Publication Date: 2022-09-29
    Description: Ocean bottom pressure (OBP) variability serves as a proxy of ocean mass variability. A question how well it can modeled by the present general ocean circulation models on time scales of 1 day and more is addressed. It is shown that the models simulate consistent patterns of bottom pressure variability on monthly and longer scales except for areas with high mesoscale eddy activity, where high resolution is needed. The simulated variability is compared to a new data set from an array of PIES (Pressure-Inverted Echo Sounder) gauges deployed along a transect in the Southern Ocean. We show that while the STD of monthly averaged variability agrees well with observations except for the locations with high eddy activity, models lose a significant part of variability on shorter time scales. Furthermore, despite good agreement in the amplitude of variability, the OBP from the PIES and simulation show almost no correlation. Our findings point to limitations in geophysical background models required for space geodetic applications. We argue that major improvements in OBP modelling require data assimilation in order to increase the coherence between modelled and observed signals.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
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