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<p>The geoid, an equipotential surface of the Earth's gravity field, serves as the fundamental vertical reference for national height systems and closely approximates Mean Sea Level (<i>MSL</i>), particularly over oceanic regions. While global geoid models such as EGM2008 and XGM2019e provide valuable large-scale representations, their accuracy can vary significantly across regions, especially in areas with sparse or low-quality data. In southern Iran, along the coasts of the Persian Gulf and the Oman Sea, high discrepancies have been observed between EGM2008 and GPS/levelling data, emphasizing the need for regional enhancement. This study aims to improve geoid accuracy in the Persian Gulf and Oman Sea by integrating satellite altimetry data from Jason-3 and Sentinel-3A with the global EGM2008 model, which serves as the background geoid. <i>MSL</i> estimate and Mean Dynamic Topography (<i>MDT</i>) model are derived from satellite altimetry observations and 0.125° × 0.125° global ocean <i>MDT</i> product of Copernicus Marine Environment Monitoring Service (CMEMS), respectively. The differences between altimetry-derived geoid heights and those from EGM2008—referred to as residuals—are interpolated on a regular spherical grid and used to locally correct the background model via statistical collocation. Cross-covariance and observation weight matrices are estimated using experimental variograms, covariance functions, and the Gaussian Process Regression (GPR) method to ensure accurate correction values. The resulting regionalized geoid model is validated against GPS/levelling-derived geometric geoid heights along the coastline, demonstrating substantially improved accuracy, with RMSE reduced to approximately 23&#xa0;cm relative to EGM2008 and 21&#xa0;cm relative to the more recent XGM2019e model. Spectral analysis further reveals that the constructed geoid primarily improves the long-wavelength structure of the model through regional-scale adjustments, which contributes to its enhanced overall accuracy.</p>

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Improving the accuracy of global geoid models in Persian Gulf and Oman sea using satellite altimetry observations

  • 期刊论文

  • 发表日期：

    2026年6月15日

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  • Hany Mahbuby,
  • Rezvan Sahragard,
  • Yazdan Amerian

摘要

The geoid, an equipotential surface of the Earth's gravity field, serves as the fundamental vertical reference for national height systems and closely approximates Mean Sea Level (MSL), particularly over oceanic regions. While global geoid models such as EGM2008 and XGM2019e provide valuable large-scale representations, their accuracy can vary significantly across regions, especially in areas with sparse or low-quality data. In southern Iran, along the coasts of the Persian Gulf and the Oman Sea, high discrepancies have been observed between EGM2008 and GPS/levelling data, emphasizing the need for regional enhancement. This study aims to improve geoid accuracy in the Persian Gulf and Oman Sea by integrating satellite altimetry data from Jason-3 and Sentinel-3A with the global EGM2008 model, which serves as the background geoid. MSL estimate and Mean Dynamic Topography (MDT) model are derived from satellite altimetry observations and 0.125° × 0.125° global ocean MDT product of Copernicus Marine Environment Monitoring Service (CMEMS), respectively. The differences between altimetry-derived geoid heights and those from EGM2008—referred to as residuals—are interpolated on a regular spherical grid and used to locally correct the background model via statistical collocation. Cross-covariance and observation weight matrices are estimated using experimental variograms, covariance functions, and the Gaussian Process Regression (GPR) method to ensure accurate correction values. The resulting regionalized geoid model is validated against GPS/levelling-derived geometric geoid heights along the coastline, demonstrating substantially improved accuracy, with RMSE reduced to approximately 23 cm relative to EGM2008 and 21 cm relative to the more recent XGM2019e model. Spectral analysis further reveals that the constructed geoid primarily improves the long-wavelength structure of the model through regional-scale adjustments, which contributes to its enhanced overall accuracy.