ITSG‐Grace2018: Overview and Evaluation of a New GRACE‐Only Gravity Field Time Series

Abstract

ITSG‐Grace2018 is a new series of GRACE‐only gravity field solutions based on reprocessed GRACE observation data (L1B RL03) and the latest atmosphere and ocean dealiasing product (AOD1B RL06). It includes unconstrained monthly and constrained daily solutions, as well as a high‐resolution static gravity field. Compared to the previous ITSG release, we implemented a number of improvements within the processing chain and use updated background models. In an effort to better model all known error sources, we propagate synthetic orientation uncertainties of the star camera assembly to the antenna offset correction for intersatellite ranging observations. This enables the disentanglement of the stationary noise of the K‐Band system and the nonstationary noise of the antenna offset correction. We further incorporated uncertainties of the atmosphere and ocean dealiasing product to reduce temporal aliasing effects. To mitigate errors in the applied ocean tide model, we used constrained GRACE estimates of selected tidal constituents as an additional background model. Variability over quiet ocean areas suggests a 27% to 46% lower noise level compared to the current spherical harmonic solutions of the official processing centers (300 km Gaussian filter applied). To ensure that the low noise floor is not accompanied by signal loss, we examined drainage basin averages, which showed consistent amplitudes with the official GRACE time series. These evaluations lead to the conclusion that ITSG‐Grace2018 is a state‐of‐the‐art GRACE time series which exhibits an excellent signal‐to‐noise ratio.

The GFZ GRACE RL06 Monthly Gravity Field Time Series: Processing Details and Quality Assessment

“…Hence, reprocessing of a GRACE RL07 time series is already planned, for which a final release of Level-1 products will be available. Apart from using these new Level-1 data and possible background model updates, the specific focus at GFZ for RL07-or other likely upcoming releases-will be on the reported C 21 /S 21 issue, as well as on a further reduction of noise as achieved by other groups (see, e.g., [14]). Whereas modification or fine tuning of the parametrization is always a promising option in view of improvements, in particular the application of an improved stochastic modeling of errors in observations and background models is envisaged.…”

“…A comparison between GFZ RL06 and recently published GRACE time series by other processing centers is not the purpose of this work; however, such comparisons were already done in several other studies: Göttl et al [56] report an increased consistency of the SDS (CSR, JPL, GFZ) RL06 and ITSG-Grace2018 solutions compared to the SDS RL05 and ITSG-Grace2016 solutions. Kvas et al [14] investigated the signal content of the SDS RL06 and ITSG-Grace2018 solutions by evaluating river basin averages and conclude that all four solutions exhibit the same signal content. Adhikari et al [57] calculated sea-level fingerprints using the SDS RL06 time series and find that differences between these three solutions are within 1-sigma uncertainties.…”

“…Apart from the SDS, recent GRACE gravity field time series are provided by other processing centers, e.g., ITSG-Grace2018 [14], CNES/GRGS RL04 [15], AIUB RL02 [16], or Tongji-Grace2018 [17].…”

Exploring Thermospheric Variations Triggered by Severe Geomagnetic Storm on 26 August 2018 Using GRACE Follow‐On Data

“…The mission consisted of two identical spacecraft, which are following each other in the same orbit, separated by about 220 km. The primary objective of the mission was to measure the temporal variations of the Earth's gravity field and to subsequently deduce mass changes in the hydrosphere, cryosphere and oceans (Jeon et al, ; Kvas et al, ; Tapley et al, ; Wouters et al, ). To provide the research community pursuing gravity field observations after the lifetime of GRACE, the successor mission GRACE‐FO was successfully launched on May 22, 2018 from Vandenberg Airforce Base, California, on a Space‐X Falcon 9 rocket.…”

A Generic Approach to Covariance Function Estimation Using ARMA-Models

“…Covariance function modeling is a task in various fields of application. They are used for example to represent a stochastic model of the observations within parameter estimation in e.g., laserscanning [18], GPS [47,48], or gravity field modeling [49][50][51][52][53][54]. The collocation approach is closely related to Gaussian Process Regression from the machine learning domain [55].…”