Abstract
Global geopotential models are widely used in the remove-compute-restore technique for local gravity field modeling. In this paper, a method for regional improvement of global geopotential models using GPS/Leveling data is presented. The part of the spherical harmonic expansion degrees that can be subject to the regional improvement is determined depending on the spatial resolution of the GPS/Leveling data and the size of the study region. In this method, a global geopotential model is required as the original model. Using the GPS/Leveling data corrected for the systematic errors, the geoid surface is obtained at the GPS/Leveling points. By expanding the gravity potential of the geoid surface into the spherical harmonics, a mathematical model is made to estimate the spherical harmonic coefficients of the regionally improved geopotential model. To stabilize the mathematical model, pseudo data of the gravitational potential type produced by the original model on the entire Earth’s surface are added to the GPS/Leveling data. The relative weight of the two types of the data, i.e., the GPS/Leveling data and the pseudo data, is selected based on fitting the original model to the GPS/Leveling data. As numerical tests, the regionally improved geopotential model of the USA from degree 8 to 779 and the regionally improved geopotential model of Iran from degree 12 to 339 are developed. To develop both regionally improved geopotential models, the EGM2008 model up to degree 2160 is selected as the original model. The assessments at the GPS/Leveling checkpoints show that the regionally improved geopotential model of the USA has a 23% improvement and the regionally improved geopotential model of Iran has an 8% improvement with respect to the original model. The numerical tests confirm the efficiency of the proposed method for the regional improvement of global geopotential models using the GPS/Leveling data.
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Mosayebzadeh, M., Ardalan, A.A. & Karimi, R. Regional improvement of global geopotential models using GPS/Leveling data. Stud Geophys Geod 63, 169–190 (2019). https://doi.org/10.1007/s11200-017-1084-9
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DOI: https://doi.org/10.1007/s11200-017-1084-9