GNSS Water Vapor Tomography Algorithm Constrained with High Horizontal Resolution PWV Data

  Objectives  Global navigation satellite system (GNSS) tomography technique has become one of the most potential techniques for retrieving the three-dimensional (3D) distribution of water vapor with the advantages of high precision observations, low cost and all-weather monitoring.

  Methods  High horizontal resolution water vapor information provided by remote sensing satellites is introduced. We propose a GNSS water vapor tomography algorithm constrained with high horizontal resolution precipitable water vapor (PWV) data for the first time, which supplements and improves the constraints of existing water vapor tomography algorithms. In the proposed algorithm, firstly, the high horizontal resolution PWV observations are calibrated, and then the PWV constraint equations are constructed based on the densified tomographic voxels. Finally, the PWV constraint equations are included into the GNSS tomography model, which optimizes the constraint conditions and improves the tomographic results.

  Results  GNSS data and remote sensing water vapor data from FY-3A satellite over Xuzhou area, China in August 2017 are used to assess the feasibility and accuracy of the proposed algorithm. Taking the high-precision radiosonde water vapor profile and 3D water vapor density field from ERA5 as reference values, it can be observed that the proposed algorithm is superior to traditional method in retrieving water vapor profile and 3D water vapor distribution. Three kinds of accuracy indexes of the tomographic results have been significantly improved using the proposed method, with the mean root mean square error (RMSE) decreasing from 2.73 g/m³ to 1.78 g/m³, showing an improvement of 34.80%.

  Conclusions  This highlights that the improved tomography algorithm has significant potential to reconstruct the accurate and reliable 3D atmospheric water vapor distribution.