The ionosphere is a crucial component of the Earth’s atmosphere and a focal point for various space application activities. This study proposes an improved method for function-based ionospheric tomography with plasmasphere correction and virtual rays augmentation. Firstly, a cubic polynomial is used to fit the electron content variations in the plasmasphere above 2000 km concerning time, latitude, and longitude. The contribution of the plasmasphere to total electron content (TEC) is then corrected rather than being treated as a constant. The results indicate that the electron content above 2000 km varies from a maximum of 1.6 TECU to a minimum of 0.75 TECU. Secondly, spherical harmonics and empirical orthogonal functions (EOFs) are employed to describe the horizontal and vertical distribution of ionospheric electron density (IED). To address the insufficient distribution of ground-based observation stations, a method is proposed to calculate the vertical TEC of virtual rays by the IRI-plas model with the Global Ionosphere Map (GIM) assimilation, thereby enhancing ray coverage. In the study area, the performance of the tomography model is improved with the increase in the order of the spherical harmonic function, while the improvements are not significant for orders above 8. Finally, the inverted slant total electron content (STEC) is compared to the original STEC observations to extract the modeling residuals. Corrections are made to the inverted IED profiles based on the STEC residuals. The modeling utilizes data from 243 GNSS stations located in the China region. The daily average internal accuracy of the function-based tomography model ranges from a minimum of 1.15 TECU to a maximum of 1.60 TECU, with an overall 30-day average internal accuracy of 1.34 TECU. The external accuracy has a maximum of 1.85 TECU, a minimum of 1.4 TECU, and a 30-day average of 1.55 TECU. Comparison with ionosonde data shows that the RMSE at different altitudes averaged 7.36×10¹⁰ el/m³ for the first-step model and 6.82×10¹⁰ el/m³ for the second. Additionally, comparison with 30-day radio occultation data yielded average RMSEs of 6.09×10¹⁰ and 5.40×10¹⁰ el/m³, respectively, confirming improved accuracy in regions with limited ray penetration.

In this letter, we investigated the potential spatiotemporal characteristics of ionospheric anomalies before the Taiwan earthquake occurred on April 2, 2024, by utilizing the total electron content (TEC) and electron density (${N}_{e}$) observations. These anomalies of global ionospheric map (GIM)-TEC, global positioning system (GPS)-TEC, tomographic${N}_{e}$, and Swarm${N}_{e}$were identified with the GIM, the GPS, and Swarm satellites, respectively. After eliminating the influence of solar and geomagnetic activities, positive anomalies were exhibited by both GIM-TEC and GPS-TEC parameters on March 27 and 29, followed by negative anomalies from April 1 to 3, indicating a noticeable spatiotemporal overlap. The negative${N}_{e}$anomaly 2 h before the shock at the height of 450 km was consistently confirmed by tomographic${N}_{e}$and Swarm${N}_{e}$. The spatiotemporal features of increased intensity and expanded range of ionospheric anomalies on April 2 coincided precisely with the meta-instability phase before the mainshock.

GNSS tomography is acknowledged as one of the most attractive techniques to accurately retrieve three-dimensional distribution of atmospheric water vapor with high-resolution. Here the development of a wide-area tomography technique for the retrieval of water vapor fields by jointly using GNSS observations and numerical weather prediction forecasts is described. We present an improved node parameterization tomography to retrieve the high-resolution wet refractivity fields over the continent of USA. This method does not depend on numerical integration by Newton-Cotes quadrature and considerably reduces computational burden in linearization. To refine the tomographic modeling, vertical variation parameter of water vapor for each voxel is estimated dynamically from the updated wet refractivity profiles after each iteration, towards achieving a self-adaptive design matrix. Global Forecast System products from NCEP are applied to initialize the tomographic solution for a simulation of real time mode. Tomography experiment is demonstrated with GPS data collected from 1440 stations over a one-month period of June 2020. Compared with the traditional node parameterization method, the improved method can enhance the performance by 6% and reduce the computational burden by 30%, respectively.

The potential of precipitable water vapor (PWV) maps retrieved by remote sensing satellites can address the geometry defect of global navigation satellite system (GNSS) observations in tropospheric tomography. The second-generation geostationary meteorological satellite Fengyun-4A (FY-4A) of China can provide PWV products with high spatial (4 km) and temporal (15 min) resolutions. This article presents the first study on water vapor tomography by integrating GNSS measurements and FY-4A products using the node-based parameterized method. Layer PWV (LPW) products of FY-4A instead of the total PWV are adopted, which can increase the rank of the tomographic equation significantly. The integrated tomography model is validated with observational data collected over the three-month period of June to August 2020 from 124 GNSS stations in Hunan province, China. Assessments using radiosonde and European Centre for Medium-Range Weather Forecasts ReAnalysis 5 (ERA5) data demonstrate the better performance of the integrated model against the traditional model using GNSS data alone. In the assessment with radiosonde profiles, the integrated model improves the tomographic solutions upon the traditional model by 40.58% and 36.33% for 30- and 15-min resolutions, respectively. Root mean square errors (RMSEs) of density differences between ERA5 and the integrated model vary from 1.24 to 2.82 g/$\text{m}^{3}$throughout the study area. RMSEs vertically decrease from$\sim 5$g/$\text{m}^{3}$at the bottom to$\sim 0.5$g/$\text{m}^{3}$at the top layer of about 10 km. This work demonstrates the benefit of high-quality FY-4A products to GNSS tomography because they can effectively mitigate the ill-posed problem of inverse process.

Precipitable water vapor (PWV) products from the second generation of China’s geostationary meteorological satellite Fengyun-4A (FY-4A) have the advantage of high spatiotemporal resolution and can play an increasingly important role in the study of atmosphere and climate. Using the radiosonde, the global navigation satellite system (GNSS), and the European Centre for Medium-Range Weather Forecasts (ECMWF) ReAnalysis 5 (ERA5) reanalysis data, this study presented a comprehensive evaluation of PWV products from FY-4A for a one-year period from January 2019 to January 2020. Results indicated that FY-4A PWV data have a good agreement with radiosonde and GNSS measured ones with the same correlation coefficient of 0.976, and the root mean square errors (RMSEs) are 3.95 and 3.73 mm, respectively. Compared with the radiosonde and GNSS, the FY-4A Advanced Geostationary Radiation Imager (AGRI) was found to underestimate the water vapor during humid conditions when the PWV is greater than 50 mm. The magnitude of underestimation increases with the growth in water vapor content. In terms of spatial variability, the RMSE of FY-4A PWV decreases with the increase in latitude, while the relative RMSE (R-RMSE) displays an opposite pattern. RMSE from the comparison between FY-4A and ERA5 PWV varies from 0 to 6 mm depending upon the location. Statistics showed that 55.08%, 59.79%, and 83.13% RMSE values are less than 4 mm in the evaluation by radiosonde, GNSS, and ERA5, respectively. The seasonal and diurnal variations of RMSE showed that: 1) summer exhibited larger RMSE than winter and 2) daytime obtained slightly worse performance than nighttime.

The advanced Himawari imager (AHI) onboard Himawari-8 can provide full-disk observations with high temporal resolution (10 min), which has outstanding advantages for dynamic real-time aerosol monitoring in East Asia. In this study, a new aerosol retrieval algorithm for AHI by integrating regional PM2.5concentrations (IRPAR) was proposed. The IRPAR algorithm constructed the surface reflectance library by integrating regional PM2.5levels as a quantitative indicator of atmospheric aerosol loadings. The IRPAR algorithm was used to obtain the aerosol optical depth (AOD) retrievals over Beijing–Tianjin–Hebei (BTH) region from March 2019 to February 2020, and its performance was preliminarily evaluated by aerosol robotic network (AERONET) measurements. The results showed that the IRPAR algorithm was able to obtain more highly accurate AOD retrievals compared to the JAXA L2 algorithm during the autumn in BTH region, with a large$R$of 0.87 (0.71 for JAXA L2 AOD) and a global climate observing system fraction (GCOSF) percentage of 28% (21% for JAXA L2 AOD). During different daytime hours, the IRPAR AOD showed a stable retrieval performance, while the JAXA L2 AOD exhibited a worst performance from 12:00 to 14:00 Beijing standard time (BST). These results demonstrated that the IRPAR algorithm was relatively less affected by the viewing angle. Future work will require a comprehensive evaluation of the IRPAR algorithm on a larger spatial scale.

Tropospheric path delay is an important error source in range measurements of many Earth observation systems. In this paper, the accuracies of 9 zenith hydrostatic delay (ZHD) and 18 zenith wet delay (ZWD) models are assessed using benchmark values derived from 10 years (2003–2012) of radiosonde data recorded at 92 stations in the China region. Our study confirms that ZHD can be well modeled with an accuracy of several millimeters by using surface meteorological observations. ZHD derived from the European Center for Medium-Range Weather Forecasts (ECMWF) has the best agreement of 2.8 mm with the radiosonde data in the China region, while the Baby ZHD model achieves the second best with an accuracy of 6.0 mm. All of the ZWD models can only estimate the ZWD with an accuracy of a few centimeters. ECMWF can provide ZWD estimation with the best accuracy of 21.4 mm, followed by the Baby semiempirical, Hopfield, Goad and Goodman, Askne and Nordius, Saastamoinen, Callahan, and Berman 74 ZWD models whose errors are below 40 mm. We find that in the China region all of the ZWD models perform better in winter than in summer and have higher accuracy in high latitudes than low latitudes. The performances of the 18 ZWD models are further validated in a Global Positioning System (GPS) precise point positioning (PPP) computation at 6 GPS stations in China. The PPP results also confirm that ECMWF is the best model. Considering its performance and simplicity, we conclude that Saastamoinen is the optimal ZWD model for the China region.