We first present a novel method of monitoring the UHI intensity using GNSS data. We overcomes two major challenges in the algorithm development. The first challenge is the determination of the GNSS tomographic top grid height, and the second challenge is the estimation of temperature from wet refractivity. The result shows that the proposed algorithm can achieve an accuracy of 1.2 K at a 95 % confidence level.

A time-varying global gridded T_s–T_m model with 0.75 × 0.75 degree spatial resolution is developed in our study to precisely estimate water-vapor-weighted mean temperature T_m from surface air temperature T_s. Using multiple statistical tests, we assessed the T_m of our model and its impact on the GPS–PWV retrievals. Comparisons with other models demonstrate that our model has prominent advantages. Matlab codes and arrays used to realize our model are provided in the supplement of this study.

We proposed a new method to determine the scale height of water vapor, which will improve the quality of vertical constraints. Then, the smoothing factor in the horizontal constraint was determined based on ERA-Interim products. The evaluation results show that the water vapor density quality obtained by the optimized technique is 13.8 % better below 3.8 km and 8.1 % better above 3.8 km than that obtained by the traditional technique.

This study focused on the extraction of the atmospheric excess phases using the non-difference processing strategy. The COSMIC POD processing is used to accurately determine the position and velocity of the centre of mass of the satellite and the receiver offset based on PANDA software. Finally, the bending angle, refractive and dry temperature profiles are taken from AEP using ROPP software.