In this paper, the SOMOSTA (Soil Moisture Monitoring Station) experiment on the intercomparison of soil moisture monitoring from Global Navigation Satellite System Reflectometry (GNSS-R) signals and passive L-band microwave radiometer observations at the Valencia Anchor Station is introduced. The GNSS-R instrument has an up-looking antenna for receiving direct signals from satellites, and a dual-pol down-looking antenna for receiving LHCP (left-hand circular polarization) and RHCP (right-hand circular polarization) reflected signals from the soil surface. Data were collected from the three different antennas through the two channels of Oceanpal GNSS-R receiver and, in addition, calibration was performed to reduce the impact from the differing channels. Reflectivity was thus measured, and soil moisture could be retrieved. The ESA (European Space Agency)-funded ELBARA-II (ESA L Band Radiometer II) is an L-band radiometer with two channels with 11 MHz bandwidth and respective center frequencies of 1407.5 MHz and 1419.5 MHz. The ELBARAII antenna is a large dual-mode Picket horn that is 1.4 m wide, with a length of 2.7 m with −3 dB full beam width of 12° (±6° around the antenna main direction) and a gain of 23.5 dB. By comparing GNSS-R and ELBARA-II radiometer data, a high correlation was found between the LHCP reflectivity measured by GNSS-R and the horizontal/vertical reflectivity from the radiometer (with correlation coefficients ranging from 0.83 to 0.91). Neural net fitting was used for GNSS-R soil moisture inversion, and the RMSE (Root Mean Square Error) was 0.014 m³/m³. The determination coefficient between the retrieved soil moisture and in situ measurements was R² = 0.90 for Oceanpal and R² = 0.65 for Elbara II, and the ubRMSE (Unbiased RMSE) were 0.0128 and 0.0734 respectively. The soil moisture retrievals by both L-band remote sensing methods show good agreement with each other, and their mutual correspondence with in-situ measurements and with rainfall was also good.

The national Beidou Navigation Satellite System (BDS) ground-based augmentation network (BGAN) of China is constructed with the existing GNSS observation resources of industrial sectors and local governments, based on the concept of joint building and sharing with sustainable development. This study provides a detailed introduction to the design, construction and operation of a meteorological application system based on BGAN, and validation of its water vapor products. BDS and GPS real-time observation of atmospheric water vapor is achieved nationwide in China and multi-GNSS applications. Through the application of multi-GNSS data and validation of the water vapor products from 2018 to 2020, the accuracy of precipitable water vapor (PWV) derived from BDS only is equivalent to that from GPS only. The root mean square error (RMSE) between them is about 2 mm with high correlation coefficient. Based on radiosonde data, the validation is conducted with the products of BDS-PWV, GPS-PWV, and Combined-PWV derived with multi-GNSS of BDS and GPS. The error characteristics of the three products show a consistent trend over the months. The bias is relatively small. The RMSE of the three products is in the range of 2.18–2.73 mm. The BDS-PWV has the largest RMSE, followed by GPS-PWV, and Combined-PWV has the smallest RMSE.

This study presents a water vapor gradient (WVG) retrieval method based on Global Navigation Satellite System (GNSS) tropospheric parameter estimation. A case study examined the method’s applicability to the extreme rainstorm event in North China in July 2023. Precipitable water vapor (PWV) and WVG data from 332 GNSS sites in this area were retrieved. Radar and precipitation data were combined to perform a spatiotemporal comparison study. The results show that GNSS PWV and WVG of this weather process were highly consistent with radar reflectivity and precipitation. When a high PWV (>60 mm) was accompanied by WVG convergence, radar reflectivity was significantly strong and precipitation occurred at the leading edge of large gradients and the convergence region. Based on the edge of big WVGs, observed by multiple GNSS stations, the location and movement of rainfall could be identified. In case of large amounts of PWV accompanied by plummeting WVG (down to 0.1–0.4 mm/km), high or persistent precipitation occurs. During the event, compared to the northern plateau, the plain region demonstrated higher PWV, lesser WVG variation, and more intense precipitation, likely caused by the topographic dynamic effect. GNSS PWV and WVG can be key indicators for short-range weather forecasting of extreme rainstorm events.

Ground-based BeiDou Navigation Satellite System/Global Navigation Satellite System (BDS/GNSS) observations can be used to retrieve precipitable water vapor (PWV) at high temporal resolution, but the estimates are confined to discrete station locations. We applied the pretrained tabular regression model LimiX, using its 2M variant, to PWV interpolation over the Beijing–Tianjin–Hebei region in 2024. Station spatial features paired with PWV estimates served as context without regional retraining. Comparisons with inverse distance weighting, ordinary kriging, universal kriging, and geographically weighted regression used PWV derived from the ERA5 reanalysis and a separate fixed-station holdout experiment. Against ERA5, LimiX had an annual mean absolute difference of 1.1 mm and a root mean square difference of 1.5 mm. Its spatial correlation coefficient (SCC) was 0.97. LimiX had the lowest root mean square difference relative to ERA5 in all seasons and months. Interpolation validation against observations at 13 withheld GNSS stations gave an annual root mean square error of 1.3 mm, close to both kriging methods. During a heavy precipitation event on 9–10 August 2024, mean PWV began increasing before quantitative precipitation estimation (QPE) rose markedly and remained elevated after the QPE peak. High-PWV and high-QPE areas overlapped locally at some epochs. LimiX combined close ERA5 agreement with station-level interpolation errors comparable to kriging, providing regional PWV fields without retraining for the study area.

In the context of the global warming, we experience increasing extreme weather events, in particular extreme precipitation which may cause natural disasters. As monsoon climate is dominant in China bringing frequent and intense convective precipitations, adequate knowledge of monsoon characteristics is necessary for understanding of extreme weather events. Considering that the monsoon is usually accompanied by distinct variations in water vapor between dry and wet seasons and rapid transitions between dry and wet states, understanding of the distribution and variability of water vapor can play a certain role in indicating monsoon characteristics. Compared with other traditional water vapor acquisition methods, ground-based Global Navigation Satellite System (GNSS) observations have big advantages for monsoon studies due to its high accuracy, high temporal availability, and long-term homogeneity. In this work, GNSS data from nearly 1000 stations densely distributed in China were used to obtain Precipitable Water Vapor (PWV) during 2016. We first utilized the bimodality of PWV statistical distribution and selected a suitable metric to quantify the impact of monsoon in different regions. Results indicate that the strongest bimodality occurs in Southwest China with the metrics below 1.75. Eastern China is also part of the monsoon regions with metrics between 1.75 and 2.25. For the less affected regions such as Northwest China, the bimodality is weak with metrics above 2.25. Subsequently, the normalized precipitable water indexes (NPWI) were constructed using temporal variations in PWV to describe the monsoon onset and retreat. The monsoon reached the south-eastern coastal regions in early/mid May (Day of year 120-140) and advanced to the north-eastern regions at the end of July (Day of year 210). In general, the monsoon advances from the south-east to the north-west with an uneven speed over China.