Comparison of remove-compute-restore and least squares modification of Stokes' formula techniques to quasi-geoid determination over the Auvergne test area

Yıldız, Hasan; Forsberg, R.; Ågren, Jonas; Tscherning, C. C.; Sjöberg, Lars E.

doi:10.2478/v10156-011-0024-9

Cited by 51 publications

(24 citation statements)

References 11 publications

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“…From practical point of view, the use of precise additive correction terms, the LSMS approach is more accurate and efficient method than others, according to recently published studies (e.g. Ågren et al 2009;Yildiz et al 2012). …”

Section: Least-squares Modification Of Stokes' Formulamentioning

confidence: 89%

Effect of ASTER DEM on the prediction of mean gravity anomalies: a case study over the Auvergne test region

Abbak

2014

Acta Geod Geophys

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A precise gravimetric geoid determination requires height information and terrestrial gravity data with high accuracy and resolution. The height data is utilized for predicting mean free-air gravity anomalies as well as computing the topographic, atmospheric and downward continuation effects which are fundamental components of any geoid model. Nowadays the Digital Elevation Model (DEM) obtained from the Shuttle Radar Topography Mission (SRTM) has been widely used when an accurate regional DEM does not exist. In addition the DEM generated from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) was newly released by researchers from Japan and United States. In this study the effect of ASTER DEM on the estimating mean free-air gravity anomalies in geoid determination were investigated in the Auvergne test area where one of its regions exhibits one of the most rugged topography over the world. The numerical results show that ASTER DEM gives worse statistics than SRTM DEM with respect to the accuracy of the height. Using ASTER DEM introduces discrepancies (compared to SRTM DEM) in the range of −4 to 10 mGal in the interpolation of free-air gravity anomalies. It is also proven that the geoid differences due to the use of ASTER DEM are a few centimeters, which remain below the accuracy level of the external GPS-levelling data.

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Section: Least-squares Modification Of Stokes' Formulamentioning

confidence: 89%

Effect of ASTER DEM on the prediction of mean gravity anomalies: a case study over the Auvergne test region

Abbak

2014

Acta Geod Geophys

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“…Accuracy of the LSC gravity estimation based on uniform, partial and local COVs modeling in each region (mGal). α ∼ = 0 for R4 could not be a precise estimation (Yildiz et al, 2012). As was mentioned in Sect.…”

Section: Sensitivity Of the Localization And Covariance Parameters Anmentioning

confidence: 92%

Assessment of local covariance estimation through Least Squares Collocation over Iran

et al. 2020

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Abstract. Covariance determination as the heart of Least Squares Collocation gravity field modeling is based on fitting an analytical covariance to the empirical covariance, which is stemmed from gravimetric data. The main objective of this study is to process different local covariance strategies over four regions with different topography and spatial data distribution in Iran. For this purpose, Least Squares Collocation based on Remove – Compute – Restore technique is implemented. In the Remove step, gravity reduction in regions with a denser distribution and a rougher topography is more effective. In the Compute step, the assessment of the Collocation estimates on the gravity anomaly control points illustrates that data density is more relevant than topography roughness to have a good covariance determination. Moreover, among the different attempts of localizing the covariance estimation, a recursive approach correcting the covariance parameters based on the agreement between Least Squares Collocation estimates and control points shows better performance. Furthermore, we could see that covariance localization in a region with sparse or bad distributed observations is a challenging task and may not necessarily improve the Collocation gravity modeling. Indeed, the geometrical fitness of the empirical and analytical covariances – which is usually a qualitative test to verify the precision of the covariance determination – is not always an adequate criterion.

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“…This is the most adopted and applied approach in geosciences, especially in geodesy. It has been widely used in modelling the regional geoid using satellite gravity data and developing semi-empirical tidal models in recent years [45][46][47][48][49]. In this approach a base model (FES2014) is used to remove tides from SLAt data to calculate the Sea Level Anomaly (SLA).…”

Section: Empirical Tidal Modelmentioning

confidence: 99%

UoNGBR: A Regional Assimilation Barotropic Tidal Model for the Great Barrier Reef and Coral Sea Based on Satellite, Coastal and Marine Data

2019

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All available satellite altimetry, coastal and marine data have been used to develop a new assimilative barotropic tidal model over the Great Barrier Reef (GBR) and Coral Sea using the Oregon State University Tidal Inverse Software (OTIS) with the specific consideration of bathymetry and drag coefficients. The model, named the University of Newcastle Great Barrier Reef (UoNGBR), has a 2′ × 2′ spatial resolution and includes 37 major and shallow water tidal constituents. The key to the development of UoNGBR is the use of a high-resolution bathymetry model gbr100 (3.6″ × 3.6″, corresponding to 100 meters resolution) and a recent baroclinic GBR1 hydrodynamic model. The gbr100 provides more detailed and accurate bottom topography, while the GBR1 hydrodynamic model provides spatially variable drag coefficients. These are particularly important in our study area due to the existence of numerous islands, coral reefs and complex bottom topography. The UoNGBR and seven existing tidal models have been used to detide independent datasets from the coastal tide gauges and Sentinel-3A altimeter mission. The detided datasets are then compared to the UoNGBR-detided data. The results show that UoNGBR has the minimum root sum square value (25.1 cm) when compared to those (between 26.1 and 66.7 cm) from seven other models, indicating that UoNGBR is among the best models in predicting tidal heights in the GBR and Coral Sea. Over coastline and coastal zones, the UoNGBR’s mean RMS errors are ~18 and 5 cm, respectively, smaller than TPXO models, as well as about 1–5 cm smaller than FES2012 and FES2014. These suggest that the UoNGBR model is a major improvement over other models in coastline and coastal zones.

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Comparison of remove-compute-restore and least squares modification of Stokes' formula techniques to quasi-geoid determination over the Auvergne test area

Cited by 51 publications

References 11 publications

Effect of ASTER DEM on the prediction of mean gravity anomalies: a case study over the Auvergne test region

Effect of ASTER DEM on the prediction of mean gravity anomalies: a case study over the Auvergne test region

Assessment of local covariance estimation through Least Squares Collocation over Iran

UoNGBR: A Regional Assimilation Barotropic Tidal Model for the Great Barrier Reef and Coral Sea Based on Satellite, Coastal and Marine Data

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