Ruixian Hao

Monitoring coastal sea levels is crucial for assessing the impacts of climate change and predicting flood risks. In this context, GNSS-Multipath Reflectometry (GNSS-MR) has emerged as a promising technique, offering an efficient and stable method for sea level retrieval. To fully realize the ability of various GNSS observations in GNSS-MR for the water level inversion, the GMR-Water, an open-source MATLAB-based software, has been developed which is capable of efficiently using the multi-observations including Signal-to-Noise Ratio (SNR), carrier phase, and pseudo range. Moreover, the GMR-Water supports the integration of multi-GNSS, multi-frequency, and multi-observation data, employing advanced error correction and combination algorithms to ensure high-precision retrieval of water levels. The software is designed with a user-friendly interface, equipped with batch processing capabilities. and it offers a comprehensive set of visualization tools to enhance the interpretation of results. We validated the performance of GMR-Water using observational data from the AT01 GNSS station, where the software was used to process the GNSS observation and analyze the water level measurement. The results demonstrate that GMR-Water can effectively retrieve water levels with high accuracy, achieving consistent and reliable results across various GNSS observation types. The performance of GMR-Water proves its potential as a valuable tool for precise coastal sea level monitoring, contributing to the further development and application of GNSS-MR water level monitoring technology.

Precipitable water vapor (PWV) reflects the vertically integrated amount of water vapor in the atmosphere, but it only represents the two-dimensional distribution of water vapor. With the development of GNSS satellite constellation and station network, GNSS water vapor tomography has emerged as a prominent technique for obtaining the three-dimensional water vapor distribution and become a hotspot in GNSS meteorology. We propose an optimal method for GNSS water vapor tomography based on parameter adaptation to solve the ill-posed problem caused by the discretization parameter redundancy in the tomographic model. This method introduces the minimum probability factor of voxels being penetrated by signals, determines the number and position of the redundant parameters in the observation equation by iterating the coefficient matrix, establishes the optimal tomographic model by eliminating the zero elements of the coefficient matrix and the redundant parameters, and calculates the water vapor density of all voxels by constructing a Laplace smoothing equation. The tomography experiments conducted in Hong Kong show that the average root mean square error (RMSE) and mean absolute error (MAE) for the proposed and traditional method are 1.25/0.70 and 1.83/0.92 mm in the internal accurate testing, respectively, compared to the GAMIT-estimated slant water vapor (SWV) as a reference. The average RMSE and normalized RMSE are 10.9 mm and 8.2% for the proposed method, while these values are 13.2 mm and 10% for the traditional method. Using the radiosonde data as references, the proposed method improves average RMSE and MAE by 20.4% and 25.3% compared with the traditional method.

随着北斗卫星导航系统（BDS）的不断发展，基于 BDS 干涉反射测量（BDS-IR）的海面高反演受到了越来越多的关注。本文介绍了基于 BDS 信噪比（SNR）信号、载波相位三频组合以及载波伪距单频组合的海面高反演方法，并分析了信号种类、信号频点对 BDS-IR 海面高反演的影响。基于 AT01 测站的 120 d BDS 观测数据反演结果表明，SNR 反演模型、载波相位三频组合反演模型与载波伪距单频组合反演模型的均方根误差分别为 0.199 m、0.189 m 和 0.204 m，偏差分别为 0.126 m、0.067 m 与 0.130 m。

The scale height (SH) represents the height increment for a certain parameter to decrease to 36.7% (1/e) of its value at a certain height. Here we present ERA5-SH, a gridded dataset containing the SH values of six troposphere key parameters (PWV, WVD, Tm, ZTD, ZHD and ZWD) based on ERA5 reanalysis from 2013 to 2022, with a temporal resolution of 1 hour and a spatial resolution of 1 degree. The dataset was generated using numerical integral and exponential fitting, and exhibits high reliability with mean coefficients of determination being 0.991, 0.957, 0.980, 0.999, 0.999, and 0.995, respectively. Using the global distributed radiosonde sites as references, the mean RMSE for the six parameters were 0.243 km, 0.189 km, 3.290 km, 0.879 km, 0.681 km, and 0.263 km, respectively. This dataset will contribute to a deeper understanding of the tropospheric vertical distribution and to improve the accuracy of atmospheric delay modeling, which are vital for the advancement of the Earth observation technologies with high precision.

Water vapor scale height reflects the vertical distribution of water vapor in the atmosphere, which is a vital parameter in tropospheric zenith wet delay (ZWD) modeling and GNSS meteorology. This study accurately calculated the long-term water vapor scale height using 89 radiosonde stations in China, analyzed the characteristics in temporal and spatial variations and explored the impact factors of the water vapor scale height from the perspective of the climate zones. The numerical results indicate that the water vapor scale height in China exhibits annual and semiannual variations with a rising-then-decreasing trend across the four seasons. A relatively larger value for the water vapor scale height were observed in the subtropical monsoon climate and the temperate continental climate zone, in which the values are greater than 2.35 km, while the values in temperate monsoon climate and the alpine plateau climate zone are relatively small. In addition, the water vapor scale height in different climate zones shows different trends, with a downward trend appeared in the subtropical monsoon climate and the alpine plateau climate zone, while an upward trend was observed in the temperate monsoon climate and the temperate continental climate zone. Furthermore, a positive correlation between water vapor scale height and temperature, pressure, and atmospheric precipitable water was found in China, with a varied degree of correlation in different climate zones.