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**A methodology for least-squares local quasi-geoid modelling using a noisy satellite-only gravity field model.** / Klees, R.; Slobbe, D. C.; Farahani, H. H.

Research output: Contribution to journal › Article › Scientific › peer-review

Klees, R, Slobbe, DC & Farahani, HH 2018, 'A methodology for least-squares local quasi-geoid modelling using a noisy satellite-only gravity field model' *Journal of Geodesy*, vol. 92, no. 4, pp. 431–442. https://doi.org/10.1007/s00190-017-1076-0

Klees, R., Slobbe, D. C., & Farahani, H. H. (2018). A methodology for least-squares local quasi-geoid modelling using a noisy satellite-only gravity field model. *Journal of Geodesy*, *92*(4), 431–442. https://doi.org/10.1007/s00190-017-1076-0

Klees R, Slobbe DC, Farahani HH. A methodology for least-squares local quasi-geoid modelling using a noisy satellite-only gravity field model. Journal of Geodesy. 2018;92(4):431–442. https://doi.org/10.1007/s00190-017-1076-0

@article{6673b8fd34ca407cab99e4ac958b3760,

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

abstract = "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–compute–restore 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 quasi-geoid model, which exploits the information in all datasets.",

keywords = "Least-squares approximation, Local quasi-geoid modelling, Multi-scale analysis, Noisy global gravity field model, Poisson wavelets, Spherical radial basis functions",

author = "R. Klees and Slobbe, {D. C.} and Farahani, {H. H.}",

year = "2018",

doi = "10.1007/s00190-017-1076-0",

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pages = "431–442",

journal = "Journal of Geodesy",

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T1 - A methodology for least-squares local quasi-geoid modelling using a noisy satellite-only gravity field model

AU - Klees, R.

AU - Slobbe, D. C.

AU - Farahani, H. H.

PY - 2018

Y1 - 2018

N2 - 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–compute–restore 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 quasi-geoid model, which exploits the information in all datasets.

AB - 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–compute–restore 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 quasi-geoid model, which exploits the information in all datasets.

KW - Least-squares approximation

KW - Local quasi-geoid modelling

KW - Multi-scale analysis

KW - Noisy global gravity field model

KW - Poisson wavelets

KW - Spherical radial basis functions

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