Enhanced Troposphere Tomography: Integration of GNSS and Remote Sensing Data With Optimal Vertical Constraints

Abstract

This article explores the enhancement of Global Navigation Satellite Systems (GNSS) tropospheric tomography by integrating remote sensing data and employing various vertical constraints. Wet refractivity modeling, critical for understanding atmospheric dynamics, has shown promising advancements. Leveraging tropospheric data from the Ocean and Land Color Instrument (OLCI), this research addresses the issue of empty voxels that impede GNSS-based tomography due to satellite and receiver geometries. Incorporating tropospheric data from remote sensing sensors mitigates empty voxels, enhancing retrieval accuracy for tropospheric water vapor. This study evaluates various vertical constraint functions in tropospheric tomography, presenting eight tomography schemes that utilize GNSS and OLCI data, highlighting their capacity to fill empty voxels without relying on empirical horizontal constraints. Results highlight the superiority of using OLCI observations in accuracy. Validation against radiosonde measurements and Weather Research and Forecasting model outputs affirms the reliability of this approach. Integrating OLCI observations with GNSS data reduces the average root mean square error by approximately 27%, with the Gaussian function exhibiting superior vertical constraint performance.

Keywords

• Global navigation satellite systems (GNSS)
• Ocean and Land Color Instrument (OLCI)
• troposphere tomography
• vertical constraints