Mean kernels to improve gravimetric geoid determination based on modified Stokes's integration
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NOTICE: this is the author’s version of a work that was accepted for publication in Computers & Geosciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers & Geosciences, 37 (11), (2011) <a href="http://dx.doi.org/10.1016/j.cageo.2011.01.005">http://dx.doi.org/10.1016/j.cageo.2011.01.005</a>
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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