A combined regional Geopotential Model using optimized global gravity field solutions
To develop a gravimetric geoid, a Global Geopotential Model (GGM) is required to minimise the truncation error arising from using the Stokes integral with a limited number of gravity data points. The choice of a best-fitting GGM determines the accuracy of a gravimetric geoid solution. Selecting a su...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | http://eprints.utm.my/103682/1/AmiHassanDin2021_ACombinedRegionalGeopotentialModel.pdf http://eprints.utm.my/103682/ http://dx.doi.org/10.1088/1755-1315/1051/1/012001 |
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| Summary: | To develop a gravimetric geoid, a Global Geopotential Model (GGM) is required to minimise the truncation error arising from using the Stokes integral with a limited number of gravity data points. The choice of a best-fitting GGM determines the accuracy of a gravimetric geoid solution. Selecting a suitable GGM is a rigorous process, requiring both internal and external evaluation of all GGMs available at the International Center for Globa Earth Models (ICGEM). Moreover, GGMs perform differently depending on the wavelength, and it is difficult to obtain a GGM that performs best across the full harmonic spectrum. In this study, a combined GGM is developed from a selection of the most recent and high-resolution GGMs covering Peninsular Malaysia. The selected models are first synthesized harmonically to obtain geoid undulations at collocated GNSS-levelled points, and free air anomalies at randomly sampled points across the study area. These quantities are compared with the observed geoid undulations and point gravity anomalies interpolated from a grid of free air anomalies. The best performing GGMs are then used to produce a combined GGM, by selecting the spherical harmonic coefficients with the best characteristics for every degree. The signal and error spectra of the new GGM are compared with the selected geopotential models. The combined GGM produced a higher cumulative signal to noise ratio (SNR) of 4402.669 compared to all the selected GGMs, with XGM2016 and Eigen-6C following suit with SNR of 4139.561 and 4092.462, respectively. Besides, the new combined GGM performed better across the whole harmonic spectrum than all selected GGMs. The use of combined GGMs in geoid modelling, instead of a single GGM may be more desirable because they can improve the quality of results. |
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