Abstract. Surface pressure (Ps) and weighted mean temperature (Tm) are two necessary variables for the accurate retrieval of precipitable water vapor (PWV) from Global Navigation Satellite System (GNSS) zenith total delay (ZTD) estimates. The lack of Ps or Tm information is a concern for those GNSS sites that are not collocated with meteorological sensors. This paper investigates an alternative method of inferring accurate Ps and Tm at the GNSS station using nearby synoptic observations. Ps and Tm obtained at the nearby synoptic sites are interpolated onto the location of the GNSS station by performing both vertical and horizontal adjustments, in which the parameters involved in Ps and Tm calculation are estimated from ERA-Interim reanalysis profiles. In addition, we present a method of constructing high-quality PWV maps through vertical reduction and horizontal interpolation of the retrieved GNSS PWVs. To evaluate the performances of the Ps and Tm retrieval, and the PWV map construction, GNSS data collected from 58 stations of the Hunan GNSS network and synoptic observations from 20 nearby sites in 2015 were processed to extract the PWV so as to subsequently generate the PWV maps. The retrieved Ps and Tm and constructed PWV maps were assessed by the results derived from radiosonde and the ERA-Interim reanalysis. The results show that (1) accuracies of Ps and Tm derived by synoptic interpolation are within the range of 1.7–3.0 hPa and 2.5–3.0 K, respectively, which are much better than the GPT2w model; (2) the constructed PWV maps have good agreements with radiosonde and ERA-Interim reanalysis data with the overall accuracy being better than 3 mm; and (3) PWV maps can well reveal the moisture advection, transportation and convergence during heavy rainfall.

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This paper presents a terrain-related method to simultaneously correct the global error trends and local linear/nonlinear terrain-related errors of the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), which have not been focused in most of the previous methods. To meet this goal, an adaptive strategy for modelling the SRTM DEM errors is first proposed, especially over mountainous areas, based on the Bayesian information criterion. Then, the M-estimator, instead of the ordinary least squares' solver, is utilized to estimate the model parameters of the constructed model to improve the estimation robustness. The proposed method was tested over the Zhangjiajie areas of China, where the ground surface terrain varies from plains to steep mountains. The results show that the errors of the SRTM DEM over the region of interest decrease from 10.1 m to approximately 8.1 m after correction with the proposed method, indicating an improvement of approximately 20%. In addition, compared with two existing and common methods that can correct the global error trends and local linear terrain-related errors of the SRTM DEM, the accuracy of the corrected SRTM DEM improves by approximately 29% and 27.9% over mountainous areas with slopes larger than 20° when the proposed method is used. Moreover, the proposed method will be beneficial to the correction of other airborne or spaceborne DEM products, especially over mountainous areas.

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An improved ionospheric tomography algorithm is developed for the tomographic reconstruction of the ionospheric electron density distribution based on the automatic search technology of relaxation factor, in which the automatic search technology is a training process to optimize the relaxation factors of the iterative algorithm. In comparison with some classical tomography algorithms, the proposed algorithm can not only greatly improve the efficiency of inversion but also obtain ionospheric electron density images with high fidelity. A careful validation of the proposed algorithm is performed by conducting numerical experiments with Global Positioning System simulation and real data, and according to the results of the quantitative comparison and statistical analysis, compared with other classical ionospheric tomography algorithms, the proposed algorithm exhibits significantly reconstruction accuracy.