The prevalence of real-time, low-cost, single-frequency, decimeter-level positioning has increased with the development of global navigation satellite systems (GNSSs). Ionospheric delay accounts for most errors in real-time single-frequency GNSS positioning. To eliminate ionospheric interference in real-time single-frequency precise point positioning (RT-SF-PPP), global ionospheric vertical total electron content (VTEC) product is designed in the next stage of the International GNSS Service (IGS) real-time service (RTS). In this study, real-time generation of a global ionospheric map (GIM) based on IGS RTS is proposed and assessed. There are three crucial steps in the process of generating a real-time global ionospheric map (RTGIM): estimating station differential code bias (DCB) using the precise point positioning (PPP) method, deriving slant total electron content (STEC) from PPP with raw observations, and modeling global vertical total electron content (VTEC). Experiments were carried out to validate the algorithm’s effectiveness. First, one month’s data from 16 globally distributed IGS stations were used to validate the performance of DCB estimation with the PPP method. Second, 30 IGS stations were used to verify the accuracy of static PPP with raw observations. Third, the modeling of residuals was assessed in high and quiet ionospheric activity periods. Afterwards, the quality of RTGIM products was assessed from two aspects: (1) comparison with the Center for Orbit Determination in Europe (CODE) global ionospheric map (GIM) products and (2) determination of the performance of RT-SF-PPP with the RTGIM. Experimental results show that DCB estimation using the PPP method can realize an average accuracy of 0.2 ns; static PPP with raw observations can achieve an accuracy of 0.7, 1.2, and 2.1 cm in the north, east, and up components, respectively. The average standard deviations (STDs) of the model residuals are 2.07 and 2.17 TEC units (TECU) for moderate and high ionospheric activity periods. Moreover, the average root-mean-square (RMS) error of RTGIM products is 2.4 TECU for the one-month moderate ionospheric period. Nevertheless, for the high ionospheric period, the RMS is greater than the RMS in the moderate period. A sub-meter-level horizontal accuracy and meter-level vertical accuracy can be achieved when the RTGIM is employed in RT-SF-PPP.

Short-term rainfall forecast using GNSS-derived tropospheric parameters has gradually become a research hotspot in GNSS meteorology. Nevertheless, the occurrence of rainfall can be attributed to the impact of various weather factors. With only using tropospheric parameters retrieved from GNSS (such as ZTD or PWV) for linear forecast, it could be challenging to describe the process of rainfall occurrence accurately. Unlike traditional linear algorithms, machine learning can construct better the relationship between various meteorological parameters and rainfall. Therefore, a combined linear–nonlinear short-term rainfall forecast method is proposed in this paper. In this method, the PWV time series is first linearly fitted using least squares, and rainfall events are determined based on the PWV value, PWV variation, and PWV variation rate. Then, a support vector machine (SVM) is used to establish a nonlinear rainfall forecasting model using the PWV value, air temperature, air pressure, and rainfall. Finally, the previous two rainfall forecast methods are combined to obtain the final rainfall event. To evaluate the accuracy of the proposed method, experiments were conducted utilizing the temperature, pressure, and rainfall data from ERA5. The experimental results show that, compared to existing short-term rainfall forecast models, the proposed method could significantly lower the false alarm rate (FAR) of rainfall forecasts without compromising the true detection rate (TDR), which were 26.33% and 98.66%, respectively. In addition, the proposed method was verified using measured GNSS and meteorological data from Yunmao City, Guangdong, and the TDR and FAR of the verified results were 100% and 20.2%, respectively, which were proven to apply to actual rainfall forecasts.

The determination of ionospheric total electron content (TEC) is crucial for various applications in space weather. However, due to the uneven distribution of stations, complex data processing and reconstruction algorithms are usually required to obtain a seamless global TEC map. To reduce the difficulty of this process, we show the possibility of reconstructing a high-resolution global TEC map based on the image inpainting method Aggregated Contextual-Transformation Generative Adversarial Nets (AOT-GAN). First, the AOT-GAN model is used to learn the data fitting process and TEC spatial distribution characteristics in the UQRG, and then the data completion performance and generalization ability of the model are verified under different data-missing conditions and geomagnetic activity levels. The verification results show that the model has relatively reliable completion results under different conditions. The average RMSE between the filled results and UQRG is mainly concentrated in 2~3 TECU, and the average SSIM index is mainly concentrated in 0.95~0.97. Even during the geomagnetic storm periods and the land data missing rate exceeds 30%, more than 92% of the biases are still within ±5 TECU. In addition, the model can also achieve considerable results when completing CODG, with more than 63% of the biases within ±1 TECU and more than 92% of the biases within ±5 TECU. Finally, the Massachusetts Institute of Technology (MIT)-TEC map is filled using the trained model. When part of MIT-TEC is removed, the biases between the completed result and the original MIT-TEC are relatively small. For the ocean area, the completed MIT-TEC map has the lowest RMSE and the STD level is similar to the ESAG product. The completed MIT-TEC maps not only maintain the global structure of TEC but also have rich details.

While space weather indices (e.g., F10.7, Dst index) are commonly employed to characterize ionospheric activity levels, the Global Mean Electron Content (GMEC) provides a more direct and comprehensive indicator of the global ionospheric state. This metric demonstrates greater potential than space weather indices for calibrating empirical ionospheric models such as IRI-2020. The COSMIC-2 constellation enables continuous, all-weather global ionospheric monitoring via radio occultation, unimpeded by land–sea distribution constraints, with over 8000 daily occultation events suitable for GMEC modeling. This study developed two lightweight GMEC models using COSMIC-2 data: (1) a POD GMEC model based on slant TEC (STEC) extracted from Level 1b podTc2 products and (2) a PROF GMEC model derived from vertical TEC (VTEC) calculated from electron density profiles (EDPs) in Level 2 ionPrf products. Both backpropagation neural network (BPNN)-based models generate hourly GMEC outputs as global spatial averages. Critically, GMEC serves as an essential intermediate step that addresses the challenges of utilizing spatially irregular occultation data by compressing COSMIC-2’s ionospheric information into an integrated metric. Building on this compressed representation, we implemented a convolutional neural network (CNN) that incorporates GMEC as an auxiliary feature to calibrate IRI-2020’s global ionospheric maps. This approach enables computationally efficient correction of systemic IRI TEC errors. Experimental results demonstrate (i) 48.5% higher accuracy in POD/PROF GMEC relative to IRI-2020 GMEC estimates, and (ii) the calibrated global IRI TEC model (designated GCIRI TEC) reduces errors by 50.15% during geomagnetically quiet periods and 28.5% during geomagnetic storms compared to the original IRI model.