Towards the Selection of an Optimal Global Geopotential Model for the Computation of the Long-Wavelength Contribution: A Case Study of Ghana

Abstract

Abstract:The selection of a global geopotential model (GGM) for modeling the long-wavelength for geoid computation is imperative not only because of the plethora of GGMs available but more importantly because it influences the accuracy of a geoid model. In this study, we propose using the Gaussian averaging function for selecting an optimal GGM and degree and order (d/o) for the remove-compute-restore technique as a replacement for the direct comparison of terrestrial gravity anomalies and GGM anomalies, because ground data and GGM have different frequencies. Overall, EGM2008 performed better than all the tested GGMs and at an optimal d/o of 222. We verified the results by computing geoid models using Heck and Grüninger's modification and validated them against GPS/trigonometric data. The results of the validation were consistent with those of the averaging process with EGM2008 giving the smallest standard deviation of 0.457 m at d/o 222, resulting in an 8% improvement over the previous geoid model. In addition, this geoid model, the Ghanaian Gravimetric Geoid 2017 (GGG 2017) may be used to replace second-order class II leveling, with an expected error of 6.8 mm/km for baselines ranging from 20 to 225 km.

Assessment of Recent Goce-Based Global Geopotential Models and Egm2008 in Niger Republic

“…In order to select the best GGM for gravity field modelling and geoid computation by RCR, several validation studies were carried out around the world, at local, regional or global scales eg. (Apeh, Moka, & Uzodinma, 2018;Foroughi, Afrasteh, Ramouz, & Safari, 2017;Godah & Krynski, 2015;Yakubu, Ferreira, & Asante, 2017). The quantities derived from GGMs are compared to terrestrial gravity anomalies, deflection of the vertical, existing geoids, GNSS/Levelling data and those from other GGMs.…”

“…The quantities derived from GGMs are compared to terrestrial gravity anomalies, deflection of the vertical, existing geoids, GNSS/Levelling data and those from other GGMs. Some studies are based on direct comparison by simple differences between GGMs and terrestrial data at different d/o of truncation (Yakubu et al, 2017). Statistical measures such as standard deviation (σ) and root mean square (RMS) of differences are analysed as function of d/o of truncation.…”

“…Statistical measures such as standard deviation (σ) and root mean square (RMS) of differences are analysed as function of d/o of truncation. Due to different spectral content between GGMs and terrestrial data, low-pass filters are commonly used to make the data comparable, such as Gaussian, average and inverse distance filters (Godah & Krynski, 2015;Yakubu et al, 2017). However, the lowpass filters are more suitable for densely distributed or gridded data.…”

“…The global assessment results are available on ICGEM website (Barthelmes & Köhler, 2016). Several validation studies showed high performances and improvements worldwide of GOCE-based GGMs in medium and long wavelengths (El Brirchi & El Azzab, 2011;Godah & Krynski, 2015;Hirt et al, 2011;Yakubu et al, 2017). Also, the latest releases of GOCE-based GGMs derived from large time span measurements showed better performances (Tocho & Vergos, 2015).…”

Land Water-Storage Variability over West Africa: Inferences from Space-Borne Sensors

“…Such an approach would be more interesting since it would also allow for an estimation of river bed changes when large flows are introduced. Unfortunately, there is no accurate DEM available for the public domain at present [49]. Thus, this shall be carried out in the future.…”