Global Navigation Satellite System (GNSS) radio occultation (RO), owing to its capability to provide high vertical resolution, high accuracy, calibration-free, and all-weather atmospheric observations, has been widely used in numerical weather prediction (NWP) and climate studies. As China’s first commercial GNSS RO constellation supporting all major GNSS systems, Tianmu-1 (TM-1) offers promising observations. However, its data quality and assimilation performance in NWP remain underexplored. This study first evaluates the TM-1 neutral atmospheric refractivity and bending angle profiles collected in January 2024. Compared with the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ECMWF-ERA5), refractivity fractional differences at 5–30 km have mean and standard deviation within ±0.15% and 1.31%, while bending angle differences are within ±0.55% and 1.99%. Radiosonde comparisons over 0–20 km show refractivity differences within ±0.19% and 1.93%, and bending angle differences within ±0.12% and 5.06%. Larger errors are mainly confined to the lower troposphere and low latitudes, with only minor variations across GNSS constellations. After validating data quality, TM-1 refractivity observations are assimilated using the Weather Research and Forecasting (WRF) model and WRFDA 3DVAR system to assess their impact on regional analyses and short-range forecasts over China. Model outputs are validated against ERA5 reanalysis and radiosonde observations. The results show that assimilating TM-1 refractivity data leads to root mean squared error (RMSE) reductions of ~5–10% for temperature analyses and forecasts in the mid-to-upper troposphere and near the surface, and ~5% in specific humidity in the lower troposphere. Wind impacts are mixed, with RMSE improvements ~2–5% above 600 hPa and degradation in the lower troposphere. Overall, this preliminary study confirms the high quality of TM-1 GNSS RO refractivity data and demonstrates its promising contribution in complementing current operational RO assimilation for regional NWP.

Rainfall forecasting from numerical weather prediction (NWP) models is uncertain due to limited spatial and temporal measurements. Radar and ground-based global navigation satellite system (GNSS) are essential sources for acquiring high spatiotemporal resolution atmospheric water parameters, with complementary strengths in accurately retrieving atmospheric moisture characteristics. Integrating radar-derived precipitation and GNSS zenith total delay (ZTD) into NWP models holds the potential to improve the performance of heavy rainfall forecasts. This study explores the potential of assimilating radar-derived precipitation and GNSS ZTDs on short-term quantitative precipitation forecasting (QPF) using the 4-D variational (4DVAR) assimilation system. A heavy precipitation event in northern Germany on June 29, 2017, is used as a case study, with four experiments conducted involving conventional data, radar-derived precipitation, GNSS ZTDs, and their synergistic assimilation. The results indicate that precipitation and ZTD assimilation individually reduce the root-mean-square error (RMSE) in humidity analysis within the mid-to-low atmosphere and improve forecast accuracy for temperature, wind, and specific humidity to varying degrees. These improvements are further enhanced in the synergistic assimilation scheme, potentially attributed to the additive benefits of radar-derived precipitation for temperature field and the precise humidity modeling enabled by GNSS ZTDs. Furthermore, comparisons of rainfall forecasts with Radar Online Adjustment (RADOLAN)-RW products indicate that synergistic assimilation leverages the advantages of both radar-derived precipitation and GNSS ZTDs, achieving substantial improvements in precipitation representation. This preliminary study underscores the potential of synergistically assimilating radar-derived precipitation and GNSS ZTDs in reducing errors in humidity, temperature, and wind analysis fields and in enhancing short-term forecasts of heavy rainfall events.

Accurate precipitation nowcasting with high spatiotemporal resolution is essential for various applications, including meteorological services, ecological conservation and atmospheric research. The current nowcasting models, which are primarily based on single radar echo data, exhibit limitations in accurately capturing the complex and fast-evolving nature of precipitation patterns. Consequently, there is an urgent need to incorporate supplementary data sources that offer high spatiotemporal resolution, and the capability for all-weather, all-day monitoring. In this study, we propose an enhanced precipitation nowcasting model, named RSG-GAN (Radar-Satellite-GNSS Generative Adversarial Network), based on the Generative Adversarial Network (GAN). It effectively combines the strengths of radar quantitative precipitation estimation (QPE), Geostationary Operational Environmental Satellite-16 (GOES-16) split window difference (SWD), and Global Navigation Satellite System (GNSS) Zenith Total Delays (ZTDs) to improve nowcasting performance. The American west coast (36° N to 48° N, 118° W to 124° W) is considered as the experimental area. The RSG-GAN model is compared with the traditional optical flow method as well as two deep learning models of utilizing solely radar data (Radar-only model) and integrating radar and satellite data (Rad-sat model). Results of the cases studies exhibit that compared to the optical flow model, the deep learning models demonstrate enhanced ability in capturing rainfall intensity variations, spatial shifts, and achieving outstanding performance in both image quality and precipitation nowcasting metrics, with the RSG-GAN model showing the most notable improvements. Statistical analysis across 189 precipitation periods reveals that the RSG-GAN model achieves the lowest average Mean Absolute Error (MAE) of 0.34 mm/h and Root Mean Square Error (RMSE) of 0.61 mm/h over a 120-minute lead time, with reductions of 36.3% and 41.6%, respectively, compared to the optical flow method. Additionally, at intermediate and higher rainfall intensity thresholds, the RSG-GAN model consistently outperforms other methods, with significant improvements in Critical Success Index (CSI) and Fractions Skill Score (FSS), while maintaining robust nowcasting performance even when other models struggle to predict precipitation. Compared with three deep learning-based methods (CM-STJointNet, MM-RNN and MM-STMixGAN), the RSG-GAN model consistently shows superior performance in both prediction accuracy and event detection. Furthermore, transfer learning experiments on the publicly dataset Storm EVent ImageRy (SEVIR) also demonstrate the remarkable generalization capability of RSG-GAN model.

Ocean surface wind is vital to the Earth’s meteorological system, and their properties can be detected by spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) measurements. With the growing number of GNSS-R signal sources, machine learning technology exhibits prominent advantages in wind speed estimation. Currently, the deep-learning techniques that establish relationships between GNSS-R measurements and ocean surface wind speeds generally apply grids and sequence structures and lack flexibility and robustness. Additionally, constructing models with individual GNSS-R observations results in the loss of valuable temporal correlation within Delay-Doppler Maps (DDMs). Therefore, this study proposes a novel spatiotemporal graph-based deep neural network (STG-DNN) for retrieving wind speed, which incorporates a graph module with a transformer module to fully exploit the spatial-temporal dependencies of DDMs. Results demonstrate that the graph module significantly improves both the accuracy and reliability in wind speed retrieval. Meanwhile, the transformer module effectively captures temporal features from various DDMs. Validations with Cyclone GNSS (CYGNSS) test data support the superior accuracy of STG-DNN, revealing a correlation coefficient of 0.92 for the wind speeds. The results indicate that the root mean square error (RMSE) of STG-DNN for wind speed is 1.27 m/s, representing improvements of approximately 33.2%, 20.6%, and 13.6% over the minimum variance estimator (MVE), convolutional neural network (CNN), and Vision Graph Neural Networks (VIG), respectively. Additionally, a promising agreement is observed between STG-DNN and ERA5 in the spatial distributions of wind speed retrieval, indicating a robust spatial performance in STG-DNN. As for the temporal scale, the daily variations in retrieval accuracy of STG-DNN exhibit smaller fluctuations compared to both CNN and VIG wind data in the test dataset.

Ocean surface winds play an essential role in regulating the earth’s weather and climate, and the Cyclone GNSS (CYGNSS) mission launched in 2016 is designed specially to monitor the ocean wind speed. In this study, an innovative model is developed based on a deep learning method to retrieve the ocean wind speed by making full use of the spatiotemporal information of CYGNSS observations. The proposed model named CNN-LSTM is established based on two modules, i.e., the Convolution Neural Network (CNN) module that extracts the spatial features around the Specular Point (SP) from a Two-Dimensional matrix of delay-Doppler Map (DDM) and the Long Short-Term Memory (LSTM) module which extracts the temporal features over a time series. The performance of the ocean wind speed derived from CNN-LSTM is assessed with the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) products. The results show that the wind speed derived from CNN-LSTM reveals an accuracy of 1.34 m/s in terms of root mean square error (RMSE) values, showing an improvement of about 36.8%, 14.6%, 6.3%, when compared to the official retrieval algorithm called Minimum Variance Estimator (MVE), Multilayer Perceptron (MLP) net, and the CNN, respectively, confirming the feasibility and effectiveness of the designed method. Among all the experiments in this study which apply machine learning-based algorithms, the wind speed achieved by CNN-LSTM presents the smallest RMSE value. Furthermore, the error analyses of the wind speed retrieval in spatial and temporal scale are also discussed, which indicate the robust performance of CNN-LSTM model. The results show that the CNN-LSTM model proposed in this study contributes to offering efficient processing of Global Navigation Satellite Systems Reflectometry (GNSS-R) observations and fully exploits the capabilities of high-accurate ocean wind speed retrieval on a global scale.

Water vapor over oceans is significant for numerical weather prediction (NWP) and climate research. Ocean platform-based global navigation satellite system (GNSS) which can sense the atmospheric water vapor is becoming an important supplement for water vapor measurements over oceans. However, the application of ocean platform-based GNSS meteorology is normally based on geodetic GNSS receivers, which implies the high cost of hardware. In this contribution, we investigate the potential of retrieving real-time water vapor over oceans with a low-cost receiver (u-blox F9P), and a geodetic GNSS receiver (Trimble NetR9) is also equipped in the experiment vessel. The post-processed Trimble NetR9 zenith total delay (ZTD) estimates and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 precipitable water vapor (PWV) products are used for the validation of real-time ZTDs and PWV values. The results show that the real-time ZTDs derived from the low-cost multi-GNSS (GPS + Galileo) observations obtain a difference of over 2.13 cm in root-mean-square (RMS) compared to the post-processed ZTDs with an averaged initialization time of approximately 40 mins. In addition, compared to ERA5 PWV, the real-time PWV derived from u-blox F9P multi-GNSS observations shows a difference in RMS of approximately 4 mm. Although u-blox F9P multi-GNSS performs relatively worse than Trimble NetR9 multi-GNSS in real-time ZTD/PWV estimates, the accuracy of low-cost GNSS receivers derived water vapor over oceans can still meet the requirements for NWP and nowcasting, which demonstrates promising prospects in supplementing the measurements of water vapor over oceans.

Atmospheric water vapor plays an essential role in climate change and weather forecasting. However, monitoring water vapor with high spatial and temporal resolutions remains a challenge, especially over ocean regions where observations are insufficient. Shipborne global navigation satellite systems (GNSSs) contribute to enriching water vapor measurements over oceans and also can help validate satellite observations. Due to the lack of long-time serial observations, the performance of shipborne GNSS-derived precipitable water vapor (PWV) is inadequately evaluated on the global ocean scale. In this study, an overall assessment of shipborne GNSS PWV over global oceans is performed based on six voyages from 2014 to 2018. In coastal areas, the PWV differences of shipborne GNSS with respect to (w.r.t.) ground-based GNSS and ground-launched radiosonde data are 2.64 and 2.85 mm in the root mean square (rms), respectively. In open oceans, compared to ship-launched radiosonde profiles and satellite measurements, shipborne GNSS PWV shows the rms of differences of 2.54 and 2.53 mm, respectively. In addition, the rms of PWV differences between the whole track of shipborne GNSS PWV and National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2) products is 2.96 mm. The intertechnique validations demonstrate that the accuracy of shipborne GNSS PWV is superior to 3 mm, which meets the requirements of climate research and numerical weather prediction (NWP).

The rapid development of the European Galileo system brings a great opportunity for the real-time retrieval of atmospheric parameters. In this contribution, Galileo observations are employed to retrieve real-time water vapor based on the precise point positioning (PPP) technique, where the benefit of ambiguity resolution on water vapor sensing is also investigated. The obtained atmospheric parameters, including zenith tropospheric delay (ZTD) and precipitable water vapor (PWV), are validated with respect to the postprocessing Global Positioning System (GPS) ZTD products and the PWV products derived from the European Centre for Medium-Range Weather Forecasts (ECMWF). The results show that the real-time ZTDs, derived from the Galileo PPP solutions, agree well with the postprocessing GPS ZTDs. An averaged root-mean-square (rms) value of 8.5 mm for the ZTD differences is achieved for the float solution after an averaged initialization process of about 27.4 min. In terms of the fixed solution, the averaged rms value is decreased to 7 mm and the initialization time is shortened to 20.8 min, showing an improvement of 17.6% and 24.1%, respectively, when compared to the float solution. Furthermore, the derived Galileo-PWVs display good agreement with the ECMWF PWVs, with an accuracy of 1.9 and 1.7 mm for the float and the fixed solution, respectively.

The rapid development of multi-Global Navigation Satellite Systems (GNSSs, e.g., BeiDou, Galileo, GLONASS, and GPS) and the International GNSS Service (IGS) Multi-GNSS Experiment (MGEX) brings great opportunities and challenges for real-time determination of tropospheric zenith total delays (ZTDs) and integrated water vapor (IWV) to improve numerical weather prediction, particularly for nowcasting or severe weather event monitoring. In this paper, we develop a multi-GNSS model to fully exploit the potential of observations from all currently available GNSSs for enhancing real-time ZTD/IWV processing. A prototype multi-GNSS real-time ZTD/IWV monitoring system is also designed and realized at the Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences (GFZ) based on the precise point positioning technique. The ZTD and IWV derived from multi-GNSS stations are carefully analyzed and compared with those from collocated Very Long Baseline Interferometry and radiosonde stations. The performance of individual GNSS is assessed, and the significant benefit of multi-GNSS for real-time water vapor retrieval is also evaluated. The statistical results show that accuracy of several millimeters with high reliability is achievable for the multi-GNSS-based real-time ZTD estimates, which corresponds to about 1- to 1.5-mm accuracy for the IWV. The ZTD/IWV with improved accuracy and reliability would be beneficial for atmospheric sounding systems, particularly for time-critical geodetic and meteorological applications.

With the availability of real-time high-rate GPS observations and precise satellite orbit and clock products, the interest in the real-time precise point positioning (PPP) technique has greatly increased to construct displacement waveforms and to invert for source parameters of earthquakes in real time. Furthermore, PPP ambiguity resolution approaches, developed in the recent years, overcome the accuracy limitation of the standard PPP float solution and achieve comparable accuracy with relative positioning. In this paper, we introduce the real-time PPP service system and the key techniques for real-time PPP ambiguity resolution. We assess the performance of the ambiguity-fixed PPP in real-time scenarios and confirm that positioning accuracy in terms of root mean square of 1.0-1.5 cm can be achieved in horizontal components. For the 2011 Tohoku-Oki (Japan) and the 2010 El Mayor-Cucapah (Mexico) earthquakes, the displacement waveforms estimated from ambiguity-fixed PPP and those provided by the accelerometer instrumentation are consistent in the dynamic component within few centimeters. The PPP fixed solution not only can improve the accuracy of coseismic displacements but also provides a reliable recovery of earthquake magnitude and of the fault slip distribution in real time.