Mean kernels to improve gravimetric geoid determination based on modified Stokes's integration

Gravimetric geoid computation is often based on modified Stokes's integration, where Stokes's integral is evaluated with some stochastic or deterministic kernel modification. Accurate numerical evaluation of Stokes's integral requires the modified kernel to be integrated across the ar...

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Main Author: Hirt, Christian
Format: Journal Article
Published: Elsevier 2011
Subjects:
Online Access:http://hdl.handle.net/20.500.11937/44183
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author Hirt, Christian
author_facet Hirt, Christian
author_sort Hirt, Christian
building Curtin Institutional Repository
collection Online Access
description Gravimetric geoid computation is often based on modified Stokes's integration, where Stokes's integral is evaluated with some stochastic or deterministic kernel modification. Accurate numerical evaluation of Stokes's integral requires the modified kernel to be integrated across the area of each discretised grid cell (mean kernel). Evaluating the modified kernel at the centre of the cell (point kernel) is an approximation which may result in larger numerical integration errors near the computation point, where the modified kernel exhibits a strongly nonlinear behaviour. The present study deals with the computation of whole-of-thecell mean values of modified kernels, exemplified here with the Featherstone-Evans-Olliver (1998) kernel modification (Featherstone, W.E., Evans, J.D., Olliver, J.G., 1998. A Meissl modified Vancek and Kleusberg kernel to reduce the truncation error in gravimetric geoid computations. Journal of Geodesy 72(3), 154-160). We investigate two approaches (analytical and numerical integration) which are capable of providing accurate mean kernels. The analytical integration approach is based on kernel weighting factors which are used for the conversion of point to mean kernels. For the efficient numerical integration, Gauss-Legendre Quadrature is applied. The comparison of mean kernels from both approaches shows a satisfactory mutual agreement at the level of 10-4 and better, which is considered to be sufficient for practical geoid computation requirements. Closed-loop tests based on the EGM2008 geopotential model demonstrate that using mean instead of point kernels reduces numerical integration errors by ~65%. The use of mean kernels is recommended in remove-compute-restore geoid determination with the Featherstone-Evans-Olliver (1998) kernel or any other kernel modification under the condition that the kernel changes rapidly across the cells in the neighbourhood of the computation point.
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spelling curtin-20.500.11937-441832019-02-19T05:35:12Z Mean kernels to improve gravimetric geoid determination based on modified Stokes's integration Hirt, Christian modified kernel mean kernel Stokes's integral modified Stokes's integration Geoid determination Gravimetric geoid computation is often based on modified Stokes's integration, where Stokes's integral is evaluated with some stochastic or deterministic kernel modification. Accurate numerical evaluation of Stokes's integral requires the modified kernel to be integrated across the area of each discretised grid cell (mean kernel). Evaluating the modified kernel at the centre of the cell (point kernel) is an approximation which may result in larger numerical integration errors near the computation point, where the modified kernel exhibits a strongly nonlinear behaviour. The present study deals with the computation of whole-of-thecell mean values of modified kernels, exemplified here with the Featherstone-Evans-Olliver (1998) kernel modification (Featherstone, W.E., Evans, J.D., Olliver, J.G., 1998. A Meissl modified Vancek and Kleusberg kernel to reduce the truncation error in gravimetric geoid computations. Journal of Geodesy 72(3), 154-160). We investigate two approaches (analytical and numerical integration) which are capable of providing accurate mean kernels. The analytical integration approach is based on kernel weighting factors which are used for the conversion of point to mean kernels. For the efficient numerical integration, Gauss-Legendre Quadrature is applied. The comparison of mean kernels from both approaches shows a satisfactory mutual agreement at the level of 10-4 and better, which is considered to be sufficient for practical geoid computation requirements. Closed-loop tests based on the EGM2008 geopotential model demonstrate that using mean instead of point kernels reduces numerical integration errors by ~65%. The use of mean kernels is recommended in remove-compute-restore geoid determination with the Featherstone-Evans-Olliver (1998) kernel or any other kernel modification under the condition that the kernel changes rapidly across the cells in the neighbourhood of the computation point. 2011 Journal Article http://hdl.handle.net/20.500.11937/44183 10.1016/j.cageo.2011.01.005 Elsevier fulltext
spellingShingle modified kernel
mean kernel
Stokes's integral
modified Stokes's integration
Geoid determination
Hirt, Christian
Mean kernels to improve gravimetric geoid determination based on modified Stokes's integration
title Mean kernels to improve gravimetric geoid determination based on modified Stokes's integration
title_full Mean kernels to improve gravimetric geoid determination based on modified Stokes's integration
title_fullStr Mean kernels to improve gravimetric geoid determination based on modified Stokes's integration
title_full_unstemmed Mean kernels to improve gravimetric geoid determination based on modified Stokes's integration
title_short Mean kernels to improve gravimetric geoid determination based on modified Stokes's integration
title_sort mean kernels to improve gravimetric geoid determination based on modified stokes's integration
topic modified kernel
mean kernel
Stokes's integral
modified Stokes's integration
Geoid determination
url http://hdl.handle.net/20.500.11937/44183