GRACE-derived ice-mass loss spread over Greenland

Nahavandchi, Hossein; Joodaki, Gholamreza; Schwarz, V.

doi:10.1515/jogs-2015-0010

Cited by 2 publications

(1 citation statement)

References 25 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deviations between ice mass trends found in the literature and our estimates can be expected due to the positive mass trend signal of the GIA effect which we did not correct our data for, as well as the differing reference periods of the data incorporated. As can be found, e.g., in [37], the GIA correction in Greenland is on the order of a few Gt yr −1 . In comparison to the summed catchment trends obtained in our study, we find that the mascon-derived ice mass trend results agree much better with the values found in the literature than the results from the SH approach.…”

Section: Greenland Ice Mass Trends From Grace and Combination Modelsmentioning

confidence: 65%

Temporal Gravity Signals in Reprocessed GOCE Gravitational Gradients

et al. 2020

View full text Add to dashboard Cite

The reprocessing of the satellite gravitational gradiometry (SGG) data from the Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite mission in 2018/2019 considerably reduced the low-frequency noise in the data, leading to reduced noise amplitudes in derived gravity field models at large spatial scales, at which temporal variations of the Earth’s gravity field have their highest amplitudes. This is the motivation to test the reprocessed GOCE SGG data for their ability to resolve time-variable gravity signals. For the gravity field processing, we apply and compare a spherical harmonics (SH) approach and a mass concentration (mascon) approach. Although their global signal-to-noise ratio is <1, SH GOCE SGG-only models resolve the strong regional signals of glacier melting in Greenland and Antarctica, and the 2011 moment magnitude 9.0 earthquake in Japan, providing an estimation of gravity variations independent of Gravity Recovery and Climate Experiment (GRACE) data. The benefit of combined GRACE/GOCE SGG models is evaluated based on the ice mass trend signals in Greenland and Antarctica. While no signal contribution from GOCE SGG data additional to the GRACE models could be observed, we show that the incorporation of GOCE SGG data numerically stabilizes the related normal equation systems.

show abstract

Section: Greenland Ice Mass Trends From Grace and Combination Modelsmentioning

confidence: 65%

Temporal Gravity Signals in Reprocessed GOCE Gravitational Gradients

et al. 2020

View full text Add to dashboard Cite

show abstract

A gap-filling algorithm selection strategy for GRACE and GRACE Follow-On time series based on hydrological signal characteristics of the individual river basins

Karimi

Pour

Amiri-Simkooei

et al. 2023

Journal of Geodetic Science

View full text Add to dashboard Cite

Gravity recovery and climate experiment (GRACE) and GRACE Follow-On (GRACE-FO) are Earth’s gravity satellite missions with hydrological monitoring applications. However, caused by measuring instrumental problems, there are several temporal missing values in the dataset of the two missions where a long gap between the mission dataset also exists. Recent studies utilized different gap-filling methodologies to fill those data gaps. In this article, we employ a variety of singular spectrum analysis (SSA) algorithms as well as the least squares-harmonic estimation (LS-HE) approach for the data gap-filling. These methods are implemented on six hydrological basins, where the performance of the algorithms is validated for different artificial gap scenarios. Our results indicate that each hydrological basin has its special behaviour. LS-HE outperforms the other algorithms in half of the basins, whereas in the other half, SSA provides a better performance. This highlights the importance of different factors affecting the deterministic signals and stochastic characteristics of climatological time series. To fill the missing values of such time series, it is therefore required to investigate the time series behaviour on their time-invariant and time-varying characteristics before processing the series.

show abstract

GRACE-derived ice-mass loss spread over Greenland

Cited by 2 publications

References 25 publications

Temporal Gravity Signals in Reprocessed GOCE Gravitational Gradients

Temporal Gravity Signals in Reprocessed GOCE Gravitational Gradients

A gap-filling algorithm selection strategy for GRACE and GRACE Follow-On time series based on hydrological signal characteristics of the individual river basins

Contact Info

Product

Resources

About