The static gravity field model DGM-1S from GRACE and GOCE data: computation, validation and an analysis of GOCE mission’s added value

et al. 2015

Star cameras (SCs) on board the GRACE satellites provide information about the attitudes of the spacecrafts. This information is needed to reduce the K-band ranging data to the centre of mass of the satellites. In this paper, we analyse GRACE SC errors using two months of real data of the primary and secondary SCs. We show that the errors consist of a harmonic component, which is highly correlated with the satellite's true anomaly, and a stochastic component. We built models of both error components, and use these models for error propagation studies. Firstly, we analyse the propagation of SC errors into inter-satellite accelerations. A spectral analysis reveals that the stochastic component exceeds the harmonic component, except in the 3-10 mHz frequency band. In this band, which contains most of the geophysically relevant signal, the harmonic error component is larger than the random component. Secondly, we propagate SC errors into optimally filtered monthly mass anomaly maps and compare them with the total error. We found that SC errors account for about 18 % of the total error. Moreover, gaps in the SC data series amplify the effect of SC errors by a factor of 5. Finally, an analysis of inter-satellite pointing angles for GRACE data between 2003 and 2010 reveals that inter-satellite ranging errors were exceptionally large during the period February 2003 till May 2003. During these months, SC noise is amplified by a factor of 3 and is a considerable source of errors in monthly GRACE mass anomaly maps. In the context of future satellite gravity missions, the noise models developed in this paper may be valuable for mission performance studies.

Section: Propagation Of Attitude Errors Into Inter-satellite Acceleramentioning

confidence: 99%

Analysis of star camera errors in GRACE data and their impact on monthly gravity field models

Inácio

Ditmar

et al. 2015

“…1) using Poisson wavelets of order 3 (Holschneider and IglewskaNowak 2007). They belong to the class of spherical radial base functions (SRBFs), which have become popular in regional gravity field modelling (e.g., Schmidt et al 2007;Klees et al 2007Klees et al , 2008Eicker 2008;Tenzer and Klees 2008;Wittwer 2009;Panet et al 2011;Slobbe 2013). The horizontal positions of the SRBFs are located on a Fibonacci point distribution (e.g., González 2010), one of several homogeneous point distributions on the sphere.…”

Section: Parameterizationmentioning

confidence: 99%

“…In our case, we found that a second iteration is not necessary. It is worth noting that a similar iterative scheme was successfully applied by Farahani (2013) and Farahani et al (2013Farahani et al ( , 2014 to deal with coloured noise in GRACE inter-satellite accelerations and GOCE gravity gradients in the context of global gravity field modelling. Figure 3 also shows the final √ PSDs of noise for CryoSat-2 and Geosat phase D GM data.…”

Section: Noise Estimation and Modellingmentioning

confidence: 99%

Impact of accounting for coloured noise in radar altimetry data on a regional quasi-geoid model

Farahani

Slobbe

et al. 2016

Self Cite

We study the impact of an accurate computation and incorporation of coloured noise in radar altimeter data when computing a regional quasi-geoid model using leastsquares techniques. Our test area comprises the Southern North Sea including the Netherlands, Belgium, and parts of France, Germany, and the UK. We perform the study by modelling the disturbing potential with spherical radial base functions. To that end, we use the traditional removecompute-restore procedure with a recent GRACE/GOCE static gravity field model. Apart from radar altimeter data, we use terrestrial, airborne, and shipboard gravity data. Radar altimeter sea surface heights are corrected for the instantaneous dynamic topography and used in the form of along-track quasi-geoid height differences. Noise in these data are estimated using repeat-track and post-fit residual analysis techniques and then modelled as an auto regressive moving average process. Quasi-geoid models are computed with and without taking the modelled coloured noise into account. The difference between them is used as a measure of the impact of coloured noise in radar altimeter along-track quasi-geoid height differences on the estimated quasi-geoid model. The impact strongly depends on the availability of shipboard gravity data. If no such data are available, the impact may attain values exceeding 10 centimetres in particular areas. In case shipboard gravity data are used, the impact is reduced, though it still attains values of several centimetres. We use geometric quasi-geoid heights from GPS/levelling data at height markers as control data to analyse the quality of the quasi-geoid models. The quasi-geoid model computed using a model of the coloured noise in radar altimeter alongtrack quasi-geoid height differences shows in some areas a significant improvement over a model that assumes white noise in these data. However, the interpretation in other areas remains a challenge due to the limited quality of the control data.

“…For models such as GOCO05s (Mayer-Gürr et al 2015), the quality of the noise covariance matrix benefits from (i) a post-fit residual analysis, which provides more realistic models of the data noise (e.g. Farahani et al 2013), and (ii) numerically efficient algorithms to propagate the full data noise covariance matrices into the noise covariance matrix of the estimated spherical harmonic coefficients (e.g. Farahani et al 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Farahani et al 2013), and (ii) numerically efficient algorithms to propagate the full data noise covariance matrices into the noise covariance matrix of the estimated spherical harmonic coefficients (e.g. Farahani et al 2013). Some progress has also been made regarding high-resolution datasets frequently used in local quasi-geoid modelling.…”

Section: Introductionmentioning

confidence: 99%

A methodology for least-squares local quasi-geoid modelling using a noisy satellite-only gravity field model

Slobbe

Farahani

2017

Self Cite

The paper is about a methodology to combine a noisy satellite-only global gravity field model (GGM) with other noisy datasets to estimate a local quasi-geoid model using weighted least-squares techniques. In this way, we attempt to improve the quality of the estimated quasi-geoid model and to complement it with a full noise covariance matrix for quality control and further data processing. The methodology goes beyond the classical remove-computerestore approach, which does not account for the noise in the satellite-only GGM. We suggest and analyse three different approaches of data combination. Two of them are based on a local single-scale spherical radial basis function (SRBF) model of the disturbing potential, and one is based on a two-scale SRBF model. Using numerical experiments, we show that a single-scale SRBF model does not fully exploit the information in the satellite-only GGM. We explain this by a lack of flexibility of a single-scale SRBF model to deal with datasets of significantly different bandwidths. The two-scale SRBF model performs well in this respect, provided that the model coefficients representing the two scales are estimated separately. The corresponding methodology is developed in this paper. Using the statistics of the least-squares residuals and the statistics of the errors in the estimated two-scale quasi-geoid model, we demonstrate that the developed methodology provides a two-scale quasigeoid model, which exploits the information in all datasets.