Three-dimensional ionospheric tomography is an essential method for understanding spatial variations in electron density and for providing physical interpretations of anomaly evolution. Global ionospheric tomography enables more comprehensive analysis of electron content variations across different altitudinal layers and the propagation of ionospheric disturbances. This study utilizes observational data from Global Navigation Satellite System, FORMOSAT-7/COSMIC-2 (COSMIC-2), and JASON-3, in conjunction with the International Reference Ionosphere (IRI-2020) model and global ionospheric map from the Center for Orbit Determination in Europe (CODE-GIM) data, to develop a function-based global tomography model (F-GCIT). The results of this model are continuously disseminated in Ionosphere Map Exchange format. The F-GCIT model is divided into four layers: 0-250 km, 250-350 km, 350-550 km, and above 550 km, with each layer represented using spherical harmonics, covering latitudes from 40°S to 40°N and longitudes from 180°W to 180°E, with a temporal resolution of 1 hour. Using product data spanning from October 2021 to October 2023, using CODE-GIM, ionosonde, IRI-2020, COSMIC-2 and incoherent scattering sounding data as references, we analyzed the accuracy of the F-GCIT model. Compared to the regional pixel-based computerized ionospheric tomography, F-GCIT demonstrates closer agreement with ISR profiles.

In urban canyon environments, the positioning performance of Global Navigation Satellite Systems (GNSS) often degrades significantly due to multipath effects. However, these multipath signals also carry information about the geometric characteristics of surrounding reflectors. This study reports, for the first time, the detection of systematic interference patterns in GNSS signal-to-noise ratio (SNR) data caused by reflections from building vertical facades. Analysis suggests that these patterns originate from coherent interference effects from single-bounce reflections. We verified this phenomenon and accurately extracted its spectral features using GNSS Interferometric Reflectometry (GNSS-IR). In developing the interference model, this research breaks through the limitations of conventional ground-reflection models by proposing a generalized three-dimensional phase difference model suitable for arbitrary reflecting surfaces. By integrating the satellite line-of-sight vector and the wall normal vector, the model achieves precise geometric characterization of the incidence angle and reflection path. Comparative experiments using high-precision laser rangefinders demonstrate strong agreement between the inverted reflector distances and ground truth, achieving centimeter-level accuracy (RMSE: 0.03–0.05 m) under smooth facade conditions. This study not only confirms the existence and detectability of single-bounce multipath signals in urban canyons but also provides a physical model and interpretation, opening new avenues for urban environment sensing using GNSS multipath signals.

Apart from the applications of navigation, positioning, and timing, the Global Navigation Satellite System (GNSS) plays an important role in improving the quality and reliability of numerical weather prediction (NWP) models. However, the difference and contribution of assimilating GNSS-derived Zenith Total Delay (ZTD) and Precipitable Water Vapor (PWV) to forecast result are less investigated, which becomes the focus of this study. A unified method of assimilating GNSS-derived ZTD/PWV is first proposed, and their difference and contribution to the forecasting performance of Weather Research and Forecasting (WRF) model are quantitatively evaluated by focusing on the multiple meteorological parameters, such as precipitation, relative humidity, temperature, and pressure. In addition, the effects of magnitude and seasonal characteristics of GNSS-derived ZTD/PWV on the WRF model are further analyzed during a case of severe convective weather. Central and eastern China is selected as the study area, and 287 meteorological stations, 452 GNSS/Met stations, and 11 radiosonde stations are selected over the whole year of 2018. Results indicate that the assimilation of GNSS-derived ZTD/PWV, particularly ZTD, enhances the forecast accuracy of different meteorological parameters, and the positive contribution degree increases as the magnitude of GNSS-derived ZTD/PWV increases. Compared with the traditional method, the root mean square error reductions of precipitation, relative humidity, temperature, and pressure generated by a unified method of assimilating GNSS-derived ZTD/PWV are 31.9%/22.7%, 54.6%/44.0%, 44.7%/35.7%, and 37.1%/24.2%, respectively. These results show the feasibility and effectiveness of the proposed data assimilation method and verify the positive contribution of GNSS-derived tropospheric products in improving the performance of WRF model, especially for severe convective event nowcasting.

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-GANs). 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 root mean squared error (RMSE) between the filled results and UQRG is mainly concentrated in$2\sim 3$TECU, and the average structural similarity index measure (SSIM) index is mainly concentrated in$0.95\sim 0.97$. 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.

Atmospheric weighted mean temperature (Tm) is a crucial parameter that links precipitable water vapor (PWV) and zenith wet delay (ZWD). To address the challenge of balancing the quality and timeliness of Tm data, this study introduced infrared remote-sensing technology based on meteorological satellites for the first time to retrieve Tm. We developed separate Tm estimation models for FengYun-4A (FY4A) observations under both clear and cloudy conditions and combined them to enable real-time retrieval of all-weather Tm. This combined model, called the all-weather Tm estimation model, is based on the linear relationship between Tm and surface temperature as well as remote sensing retrieval theories related to surface temperature and cloud-top properties. This grouping modeling approach allows continuous spatiotemporal Tm data to be estimated at minute intervals, even under cloudy conditions. Radiosonde-derived and ERA5-derived Tm data from 2022 were used to assess the accuracy of FY4A-derived Tm for Australia. Compared to radiosonde-derived Tm, the root mean square error (RMSE)/bias values for FY4A-derived Tm were 1.37/0.05, 1.45/0.06, and 1.38/0.06 K for all-time, daytime, and nighttime, respectively. Compared to the ERA5-derived Tm, the RMSE/bias values for FY4A-derived Tm were 1.26/0.01, 1.33/0.01, and 1.37/0.03 K under all-weather, clear, and cloudy conditions, respectively. The validation results indicated that the satellite-based Tm retrieval model possesses the advantages of real-time monitoring, all-weather capability, high accuracy, and high spatiotemporal resolution. Thus, it has tremendous potential for deepening interdisciplinary collaboration between the meteorology and navigation fields.

The weighted mean temperature (Tm) is a crucial variable in mapping zenith wet delays from the global navigation satellite system (GNSS) to precipitable water vapor (PWV). Existing empirical models for Tm estimation mainly utilize a single data source, either radiosonde (RS) data or reanalysis data. And these models assume that the Tm follows a predefined linear periodic function, failing to capture the detailed nonlinear Tm variations, and yielding low accuracy. This study developed a Tm forecast model (GRNN-F) using the generalized regression neural network (GRNN) and fused Tm data. The performance of GRNN-F was evaluated using Tm from the RS sites not involved in modeling. The results demonstrate strong agreement between GRNN-F and RS, with a bias of 0 K, a standard deviation (STD) of 3.26 K, and a root mean square (RMS) error of 3.26 K. Compared with the traditional predefined function-based model (GPT3) and the linear model (GTm), GRNN-F exhibits a 28.35% and 34.67% reduction in STD and a 30.93% and 36.58% reduction in RMS. Compared to the single data source-based models, GRNN-F demonstrates a significant advantage in Tm forecast, especially at moments with more and larger sudden Tm variations. Moreover, GRNN-F outperforms two comparative models based on random forest and backpropagation neural network on the same fused data across different cases. The theoretical mean PWV relative error derived from GRNN-F is only 1.15%, with less than 1% of the sites accounting for 19%, whereas other models fail to achieve this level of accuracy at any site.

In this study, the high temporal resolution ionospheric product uqrg provided by Universitat Politècnica de Catalunya (UPC) is downsampled into eight products with different temporal resolutions (15, 30 min, 1, 2, 4, 6, 12, and 24 h) in the Antarctic. Then, the accuracy of different products is analyzed using Global Navigation Satellite System (GNSS) total electron content (TEC) from approximately 40 stations’ data between 2015–2016 and 2022–2023. The results indicate that there is little difference in the accuracy of products with time resolution$\le 60$min, with a difference in root mean square (rms) <0.2 Tecu. Then, as the temporal resolution decreased, the rms of products gradually became larger, from 4.2 to 6.0 Tecu in 2015–2016 and 5.1 to 7.0 Tecu in 2022–2023, respectively. The differential TEC between UPC TEC and GNSS TEC is further used to fit a model for correcting UPC products based on the spherical crown harmonic (SCH) function. The results show that the bias and rms decreased from −1.93 to −0.07 and 4.39 to 2.89 Tecu after the correction, and the correction effect is better in polar day than in polar night. Finally, the polynomial model and the long short-term memory (LSTM) model are used to predict the SCH coefficients, respectively, to obtain the predicted TEC. The results suggest that the polynomial model performs better in short-term prediction, with an accuracy improvement of 20.38% compared with the product before corrected, and the LSTM model performs better in long-term prediction, with an accuracy improvement of 13.3%.

The ionospheric mapping function (MF) is used to achieve mutual conversion between the vertical total electron content (VTEC) and the slant total electron content (STEC) and is vital to the application of ionospheric products. Currently, the typically used MFs consider only the effect of the ionospheric thin-layer height and signal elevation angle, whereas the effects of ionospheric spatiotemporal changes and the azimuth angle, which severely restrict the accuracy of the MF, are not considered. In this study, an ionospheric MF model with the modified Julian day (MJD), local time (LT), elevation angle, and azimuth angle of the ionospheric pierce point (IPP) as inputs is proposed. The MF model, which is named long short-term memory (LSTM)-MF, is constructed using data from the United States provided by the Massachusetts Institute of Technology (MIT)/Haystack Observatory from September 1, 2021 to April 30, 2022, and ionospheric grid products named Model VTEC are established based on the LSTM-MF model. On the test set, the root-mean-square errors (RMSEs) of the STEC projected using the LSTM-MF model in the elevation-angle ranges of 20°–40°, 40°–70°, and 70°–90° are 48.66%, 33.79%, and 11.36% higher than that of the single-layer MF (SLMF) model, respectively. On December 5, 2021, the STEC obtained by projecting the Model VTEC product using the LSTM-MF model is 43.8% higher in accuracy than that obtained by projecting MIT VTEC products using the SLMF model at the low elevation angles of 20°–50°. The LSTM-MF model proposed herein and the established VTEC product improved the STEC accuracy obtained from low-elevation-angle conversion.

To improve the bottomside ionospheric electron density profile (EDP) described by the International Reference Ionosphere (IRI), a global bottomside EDP updated model for geographic latitudes between ±40° is constructed. This model is based on the spherical harmonic analysis (SHA) method and utilizes radio occultation (RO) data from Formosa Satellite 7/Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (Formosat-7/COSMIC-2) to improve IRI. Using incoherent scatter radar (ISR) and ionosonde data as references, the corrected parameters and the bottomside EDP of the SHA model were compared with the IRI data. The results show that the time-series curve trends of hmF2 and NmF2 corrected by the SHA model are closer to those of the ISR/ionosonde curve. The standard deviation (STD) of hmF2 and NmF2 at different time segments improved, with the highest improvement ranges of 41.41% and 57.58%, respectively. The total time period statistical results for individual stations also showed an average reduction in STD of 18.80% and 10.11% for hmF2 and NmF2, respectively. Furthermore, the bottomside EDPs of the IRI and SHA models were compared with the ISR data, and we found that owing to the improvements in thickness parameter${B}0$and shape parameter${B}1$, the bottomside profile shape was also significantly improved. Finally, the bottomside EDPs generated by SHA model were used as vertical constraints to improve the existing computerized ionosphere tomography (CIT) algorithm. The 3-D evolution process of a moderate geomagnetic storm on February 2–3, 2022, was quantified. The 3-D ionospheric morphological changes and evolution characteristics in the region of 18°–36°N and 98°–123°E are clearly demonstrated.

Precipitable water vapor (PWV) retrieved from Global Navigation Satellite System (GNSS) has been successfully applied in rainfall forecast. This article shifts to a new focus, aiming at nowcasting lightning, for its relatively scant GNSS-PWV investigation. Unlike previous studies that mainly explored statistical correlations between PWV and lightning, this approach integrates GNSS-PWV and other meteorological parameters with advanced automated machine-learning algorithms to accurately predict lightning occurrences with a lead-time up to 30 min. In this article, the relationship between lightning occurrences and PWV variations is first examined through comprehensive statistical analysis. Next, a machine-learning-based lightning nowcasting model is established in this study, with the input of GNSS-PWV and common meteorological parameters. The training and test datasets are sampled every 10 min from seven collocated GNSS stations and automatic weather stations (AWSs) along with the lightning location information during the period from 2018 to 2022 in Hong Kong. A comprehensive evaluation is conducted on the performance of the lightning nowcasting model. Results indicate that the proposed model possesses convincingly more excellent performances over four evaluation metrics: probability of detection (POD, 89%), false alarm ratio (FAR, 30%), threat score (TS, 0.64), and Heidke skill score (HSS, 0.77). It is also revealed that this model achieves impressive innovativeness, performance, and competitive advantages, compared with other existing methods and previous lightning forecast models.

The slant total electron content (STEC) values of signal paths from global navigation satellite system satellites to observation stations are an important data source for voxel-based computerized ionosphere tomography (CIT). However, the height range of the satellite signal rays is much larger than that of the ionospheric-tomography region. Therefore, it is commonly using an empirical model to obtain a fixed proportional coefficient of the STEC of each ray that is outside the tomography region, and then eliminating the influence of the plasmasphere electron content (PEC) to CIT fixed proportional factor for the PEC (CIT-FPPEC); however, it has been found that this is unreasonable and can negatively affect the accuracy of CIT. Herein, we propose an improved CIT method based on adaptive estimation of the PEC (CIT-AEPEC). Numerical experiments were conducted using global positioning system observation data over Europe from May 8 to May 15, 2019, and the inversion results were compared with the electron density profiles from ionosonde data and in situ measurements of Swarm satellites. In contrast to the CIT method without removal of the PEC (CIT-STEC) and CIT-FPPEC, statistical analysis showed that the root-mean-square error (RMSE) values between the CIT-AEPEC and ionosonde observations were improved by 34.54% and 26.78%, and the RMSE values between the CIT-AEPEC and Swarm measurements were improved by 39.77% and 36.97%, respectively. Finally, CIT-AEPEC inversion results were used to detect ionospheric perturbations during two magnetic storms from May 8 May 15, 2019. The effects of negative ionospheric storms and daily variations were clearly visible in peak value maps of the ionospheric electron density (IED). These results reveal that the proposed method is a useful tool for research examining space weather.

The global ionospheric model is the most effective way to study the structure and variation of the global ionosphere. However, the current global ionosphere maps (GIMs) have the defect of low spatiotemporal resolution and cannot reflect the short-term nonlinear changes and small-scale structures of the vertical total electron content (VTEC). This article proposes a new method for establishing a global ionospheric model with high spatiotemporal resolution. The spherical harmonic (SH) expansions are used to model the VTEC observations, and the Kalman filter is used to estimate the model’s coefficients with high accuracy. The method calculates SH coefficients every 5 min, increasing the spatial resolution to 7.2°. Precise determination methods for the state noise covariance matrix and the observation noise covariance matrix in the Kalman filter are also proposed. The model with a high spatiotemporal resolution was established using the observations of 300 global navigation satellite system (GNSS) tracking stations worldwide. The results show that the model with high spatiotemporal resolution can more finely reflect the nonlinear changes of VTEC in a short period and the small-scale structure of the ionosphere. The accuracy of the high-resolution model is validated using high-precision differential slant total electron content (dSTEC) observations from three sets of tracking stations and compared with the final product of three international GNSS service ionosphere associate analysis centers (IGS IAACs). The results show that the accuracy of the high spatiotemporal resolution model in this article is better than the products of IGS IAACs. The research in this article provides a new idea for establishing GIMs with higher spatiotemporal resolution and accuracy.

The latest batch of the Chinese Fengyun-2 (FY-2) geostationary satellites (i.e., FY-2F, FY-2G, and FY-2H) provides total precipitable water (TPW) products at high spatial and temporal resolutions. However, due to the lack of accuracy and performance evaluation for these products, a vast amount of valuable TPW data remains unused in atmospheric science research. To address this issue, this study aimed to propose and apply a validation strategy that incorporated a hemispheric vertical correction model (VCM) to obtain reliable evaluation results. With the help of reliable radiosonde and the state-of-the-art fifth generation of the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA5), this study was the first to assess the quality of the full-disk TPW products retrieved via the three FY-2 satellites from January 2019 to December 2020. In addition, this study analyzed the water vapor content, latitude, and elevation dependencies of FY-2 TPW retrieval error and explored the potential for improving the quality of each satellite TPW product through a linear calibration in the three test areas of northern and southern temperate zones and tropics. The results of this study were threefold. First, the accuracy of FY-2F and FY-2H TPW was superior to that of FY-2G. The root mean square error (RMSE) values of FY-2F, FY-2G, and FY-2H were 3.84, 4.46, and 3.73 mm and 2.30, 2.55, and 2.14 mm relative to the radiosonde and ERA5 data, respectively. Second, the TPW retrieval error of the FY-2 satellites decreased with the increasing latitude or decreasing elevation. Overall, FY-2G underestimated TPW, whereas FY-2F and FY-2H only underestimated TPW under wet conditions (i.e., TPW > 55 mm). Finally, the calibration potential of FY-2F and FY-2H was higher than that of FY-2G, and the bias, slope, and potential index (PI) values of FY-2G were lower than those of FY-2H and FY-2F in all the three test areas.

The power-law relationship between precipitable water vapor (PWV)/convective available potential energy (CAPE), and extreme rainfall (ER) has been explored. However, the retrieval of PWV is reliant on the zenith total delay (ZTD) of the Global Navigation Satellite System (GNSS), and errors are introduced when converting ZTD to PWV. In this study, we propose the ZCER model, a comprehensive analysis model that integrates ZTD, CAPE, and ER, for investigating long-term variation in ER intensity and frequency, which expresses their close relationships in a novel approach and expands the application area of GNSS-derived ZTD. Daily ZTD, CAPE, and rainfall data were collected from 219 GNSS stations in China from 2011 to 2020 (10 years). Time- and frequency-domain information encapsulated in the three variables was extracted using wavelet coherence, proving that both ZTD and CAPE contributed to rainfall. The relationships between ZTD/loge(CAPE) and loge(ER) were investigated at the annual, seasonal, and monthly scales. The results revealed that the contribution of ZTD/CAPE to ER varied spatially and temporally. Furthermore, the synergistic contributions of ZTD and CAPE to ER were further investigated. Statistical results showed that CAPE and ZTD not only complemented each other on the geographical scales to ER in China but also on the seasonal and monthly scales. Moreover, the qualitative relationships between the ZTD, CAPE, and ER frequency were elucidated. Our findings validate the strong links between high ZTD, CAPE, and ER intensity and frequency in China on geographical and temporal scales.

Water vapor is an essential element in the hydrologic and energy cycles, as well as in the climate and atmospheric circulation on Earth. Though various techniques have been developed, water vapor is still difficult to retrieve with both high accuracy and resolution. To calibrate the biases and restrain large errors in the moderate resolution imaging spectroradiometer (MODIS) near-infrared (NIR) precipitable water vapor (PWV) in Western Europe, we propose a hybrid model that combines the spherical cap harmonic analysis (SCHA) model and the Extreme Gradient Boosting (XGBoost) model. This model includes two main steps 1) initial calibration of MODIS PWV using an SCHA model and 2) advanced calibration of the interim PWV using an XGBoost model. The results show that the hybrid model achieves an average bias of 0.0 mm, STD of 2.0 mm, and rms of 2.0 mm, increasing the MODIS PWV accuracy by 55.6% in terms of rms in Western Europe in 2020. We further demonstrate that the hybrid model outperforms the SCHA model and the XGBoost model in calibrating biases and restraining large errors. We find that the MODIS PWV typically exceeds the GNSS PWV by 0.7 mm on annual average and their difference fluctuates seasonally, varying from −4.0 mm in winter to 4.0 mm in summer. This study provides a powerful method to optimize the MODIS PWV and obtain high-quality PWV products for meteorological research and Earth observation systems.

GNSS coordinate time series reflects the combined influence of geophysical factors on stations around the land surface. Although some traditional parameterized methods are helpful to determine the magnitude of the seasonal signal at GNSS stations, the annual variation characteristics of the stations are not static, thus it is quite necessary to extract finer periodic signals with time-varying coefficients (PSTC) from stations’ position time series. This paper focuses on the height time series of 243 stations from the Crustal Movement Observation Network of China (CMONOC) and employs singular spectrum analysis (SSA) to extract PSTC. The results show that SSA method can effectively extract the time-varying trend and periodic terms from the original time series, which cannot be perfectly achieved by parameterized methods. SSA method reduces the RMSE value of the residual time series at 90.5% CMONOC stations, compared with the results of maximum likelihood estimation (MLE). Its function in extracting the PSTC from CMONOC stations is significant for further explaining the generation mechanism of the land surface nonlinear deformation in China. Different from MLE method which only considers the given epochs of offsets, SSA method can effectively fit the original time series through singular value decomposition (SVD) and signal reconstruction, despite there are unrecognized offsets contained in GNSS time series. It still works well when there is an offset up to 20 mm, which would reduce the traditional workload of offset detection by sight. SSA method manages to distinguish large unknown offsets, showing as negative improvement rates.

This work models and predicts anomalous jumps in GNSS total electron content (TEC) caused by geomagnetic disturbance or solar radiation by introducing F10.7 and regional Dst indices, which are used to characterize solar radiation and geomagnetic disturbance status, respectively. We then introduced long-distance constraint stations to restrain ionospheric variations during high solar activity, which reduced the influence of unmodelled factors on prediction accuracy. In our experimental work, Global Ionosphere Map (GIM) TEC data from 11 stations located at different latitudes were used for comparison during maximum (2014) and minimum (2018) of solar cycle 24. Sample data for >27 days were used for learning, and then ionospheric changes for the whole year were predicted using the sliding window method. The results showed that, the standard deviation for the global ionospheric prediction products could be guaranteed to be within 2 TECU. The proximity station constraint method proposed in this paper and the Dst high-order polynomial correction method during geomagnetic disturbance control the prediction error within -4.4 TECU, while the change of adjacent days is -51.4 TECU in 2014. The new algorithm reduced the TEC jump effect in predictions, and the prediction of ionospheric products provided by this algorithm can meet the basic requirements of ionospheric delay correction in high-precision positioning.

Aiming at the urgent demands on (near) real-time ionosphere products, we study the near real-time (NRT) modeling of the global ionospheric total electron content (TEC) by IGS hourly data and introduce the Kalman filter (KF) to solve the model parameters. The main objective of this article is to propose an adaptive method for determining the KF process noise covariance matrix. This method can reflect the change regularity of spherical harmonic (SH) between epochs and consider the impact of the current ionosphere level on SH. It can adaptively adjust the KF process noise covariance matrix of each epoch to improve the accuracy of NRT global ionosphere maps (GIMs). We analyze the effects of different initial values of the state vector and its covariance matrix on the SH coefficients and propose a method to avoid repeated filter initialization. The results show that for different initial values, the filter can reach the state of convergence within 6 h, but a high-precision initial value can significantly accelerate the KF convergence speed. Compared with Global Navigation Satellite System (GNSS) differential slant TEC (dSTEC) observables, the rms of our NRT products xrtg during quiet and magnetic storms are 1.47 and 1.56 TECU, respectively, larger than the postprocessed GIMs, but significantly smaller than those of real-time GIMs. Compared with Jason VTEC, the results also show that the accuracy of xrtg is even better than European Space Agency (ESA) final products during the magnetic storm.

Precipitable water vapor (PWV), derived from the Global Navigation Satellite System (GNSS), has contributed significantly to rainfall forecasting. However, another key parameter, convective available potential energy (CAPE), is strongly correlated with increases in extreme rainfall under the background of global warming but has rarely been investigated for rainfall forecasting. Therefore, a real-time rainfall nowcast (RRN) model that combines CAPE and PWV is proposed in this study. In addition, seasonal factors and the time autocorrelation of the predictors were considered. Here, the previous hourly PWV, CAPE, temperature, and rainfall were used to establish the RRN model and simulate/nowcast the next hourly rainfall based on the support vector regression, which was performed in a time span of five years at 23 GNSS stations in Taiwan Province under four designed schemes to validate the performance of the proposed RRN model. The average root mean square (RMS) and correlation coefficients of the proposed RRN model reached 0.34 mm/h and 0.96, respectively. Additionally, the linear relationships between the daily CAPE/PWV and extreme rainfall were investigated, revealing that PWV may contribute more to trigger extreme rainfall than CAPE. Finally, compared to existing quantitative rainfall forecast studies, the RRN model achieved significantly improved rainfall nowcast accuracy and thus has more potential applications in rainfall forecasting.

Estimating precipitable water vapor (PWV) with high accuracy and spatial resolution is important in many disciplines. Water vapor absorption and non-absorption channels can be observed in the near-infrared (NIR) ray of the Moderate Resolution Imaging Spectroradiometer (MODIS), which can be used to retrieve PWV. However, traditional algorithms overestimate the NIR PWV in North America. This study proposes a novel NIR retrieval algorithm based on machine learning that considers land-cover types to estimate high-accuracy PWV. To do this, nonlinear models between MODIS NIR transmittance, based on the two-and three-channel ratio, and global navigation satellite system (GNSS) PWV, recorded by the SuomiNet GNSS network, are established using a backpropagation neural network (BPNN). Verification shows that the root mean square error (RMSE)/standard deviation (STD)/bias of the two-channel ratio PWV is 1.29/1.29/0.02 mm, respectively, and the improvements of RMSE and STD are 66.32% and 37.98%, respectively. The RMSE/STD/bias values of the three-channel ratio PWV are 1.29/1.29/0.02 mm, respectively, and the improvements in RMSE and STD are 68.67% and 42.31%, respectively. In addition, the surface verification of the proposed method in six land-cover types shows that both the two-and three-channel ratio methods can yield satisfactory PWV estimates. Compared with the MODIS PWV products, the proposed method yields remarkable progress.

To solve the ill-posed and accuracy problems experienced by global navigation satellite system (GNSS) computerized ionosphere tomography (CIT), this study proposes the use of the ionospheric profile data of COSMIC-2 as the initial scale factor to constrain GNSS data. At present, studies are lacking on long-term data volume statistics and accuracy assessment of COSMIC-2 ionospheric profile products. Therefore, we calculated the data volume statistics and assessed the ionospheric quality of the COSMIC-2 data for the whole year of 2020. We used incoherent scattering radar (ISR) and ionosonde data to evaluate the quality of the COSMIC-2 ionospheric profile data. To verify the accuracy and reliability of the CIT algorithm for COSMIC-2 ionosphere profile-constrained GNSS data, the American region was selected. On the plane, the tomographic results were superimposed and compared with the global ionospheric map (GIM). The root mean square (RMS) of the VTEC difference in the six periods was 0.68, 0.97, 0.63, 0.86, 0.76 and 0.82 TECU, respectively. In the vertical direction, the scale factor that was not involved in the CIT was compared with the ratio of TECs in each layer to the total TEC. The average difference of the ratio factors in the four periods was 3.72%, 2.79%, 1.80%, 3.05%, 1.99%, and 2.37%, respectively. Finally, an intermediate-level geomagnetic storm that occurred on July 25, 2020 was selected for analysis, and the three-dimensional ionospheric morphological changes and evolution characteristics of the Australian region during this geomagnetic storm were studied.

Precipitable water vapor (PWV) is one of the key parameters in the evolution of extreme weather and climate change. However, current data fusion methods (such as Gaussian processes, spherical cap harmonics, and polynomial fitting) can hardly obtain simultaneously the PWV map with high precision and high spatiotemporal resolution. To solve this problem, a two-step-based PWV fusion (TPF) method is proposed, in which a hybrid PWV fusion model (HPFM) and a spatial and temporal fusion model (STFM) are introduced separately. In the first step, HPFM is established by combining the global pressure and temperature 2 wet (GPT2w) model, spherical harmonic functions, and polynomial fitting to obtain the PWV value with high precision at an arbitrary location in the study area. In the second step, STFM is proposed to generate the PWV map with high temporal resolution taking advantage of site-based global navigation satellite system (GNSS)-derived PWV. To validate the performance of the proposed method, GNSS observations, ERA-Interim, and ERA5 reanalysis products are selected in Yunnan Province, China, to carry out the experiment. Statistical results show that: 1) HPFM has the ability to obtain atmospheric water vapor with a root mean square (rms) of less than 3 mm in an arbitrary location of the PWV map and 2) STFM can generate PWV maps with the same temporal resolution as GNSS observations, and the accuracy of the obtained PWV values can be guaranteed. Therefore, the proposed TPF method is proven to have the ability to simultaneously retrieve PWV maps with high accuracy and spatiotemporal resolution.

Previous studies on short-term rainfall forecast using precipitable water vapor (PWV) and meteorological parameters mainly focus on rain occurrence, while the rainfall forecast is rarely investigated. Therefore, an hourly rainfall forecast (HRF) model based on a supervised learning algorithm is proposed in this study to predict rainfall with high accuracy and time resolution. Hourly PWV derived from Global Navigation Satellite System (GNSS) and temperature data are used as input parameters of the HRF model, and a support vector machine is introduced to train the proposed model. In addition, this model also considers the time autocorrelation of rainfall in the previous epoch. Hourly PWV data of 21 GNSS stations and collocated meteorological parameters (temperature and rainfall) for five years in Taiwan Province are selected to validate the proposed model. Internal and external validation experiments have been performed under the cases of slight, moderate, and heavy rainfall. Average root-mean-square error (RMSE) and relative RMSE of the proposed HRF model are 1.36/1.39 mm/h and 1.00/0.67, respectively. In addition, the proposed HRF model is compared with the similar works in previous studies. Compared results reveal the satisfactory performance and superiority of the proposed HRF model in terms of time resolution and forecast accuracy.

As one of the important factors in atmospheric physical and chemical processes, aerosol optical depth (AOD) has an important impact on regional and global climate. Therefore, monitoring and predicting the temporal and spatial changes of AOD is of considerable significance. Existing methods mainly use a large number of meteorological parameters and ground observations to forecast AOD. However, modeling data are numerous and difficult to obtain practically. In this study, an adaptive AOD forecasting (AAF) model is proposed using the zenith total delay (ZTD) derived from global navigation satellite system (GNSS). This model only uses the ZTD as the external input parameter and considers the time autocorrelation of AOD for the previous epoch. In addition, AAF can adaptively adjust the model coefficients and has high accuracy. The AOD data derived from the Second Modern-era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Aerosol Robotic Network in the Beijing–Tianjin–Hebei (BTH,$113^{\circ } 27^{\prime }$E–$119^{\circ } 50^{\prime }$E,$36^{\circ } 05^{\prime }$N–$42^{\circ } 40^{\prime }$N) region over the period of 2015–2017 are used to perform the experiment. In addition, ZTD data of 16 GNSS stations in BTH region from the Crustal Movement Observation Network of China are selected to establish the AAF model. Experimental result reveals good performance of the proposed AAF model for internal and external validations. The difference in root mean square (rms), mean absolute error, and Bias of AOD between the AAF model and MERRA-2 are 0.11, 0.08, and 0.03, respectively. Compared with the existing AOD forecast models, the proposed AAF model is superior in terms of time resolution, rms, and correlation.

Aerosol optical depth (AOD) is one of the basic parameters for determining the total aerosol content, and it exerts an important impact on regional environment pollution. To investigate the spatiotemporal variations of AOD, this study analyzes the relationship of AOD with precipitable water vapor (PWV) derived from a global navigation satellite system (GNSS) and meteorological parameters and proposes an adaptive AOD forecasting (AAF) model. In this model, the initial AOD value is determined using an empirical AOD model that considers annual periodicity, and the AOD difference is fitted using PWV, temperature ($T$), and surface pressure ($P$). In addition, this model also considers the time autocorrelation of the AOD difference; the model coefficients can be adaptively updated with training data. AOD data at 550 nm derived from the aerosol robotic network (AERONET), second modern-era retrospective analysis for research and applications (MERRA-2), and Copernicus atmosphere monitoring service (CAMS) for the Beijing–Tianjin–Hebei area are utilized to validate the proposed AAF model. Numerical results show that: 1) the accuracy of AOD derived from MERRA-2 is superior to that obtained from CAMS; 2) AOD is negatively correlated with$P$, is positively correlated with PWV and$T$, and has a high time autocorrelation with the AOD difference at consecutive times; and 3) the proposed AAF model demonstrates better performance than the traditional multiple linear regression (MLR) model. The average root mean square error (RMSE), mean absolute error (MAE), and bias of the AAF model are 0.17, 0.14, and −0.04, respectively, and those of the MLR model are 0.31, 0.25, and 0.06, respectively. These results reveal that the proposed AAF model can estimate AOD with high precision and has considerable potential for application in AAF research.

Global Navigation Satellite System (GNSS) ionospheric tomography is a typical ill-posed problem. Joint inversion with external observation data is one of the effective ways to mitigate the problem. In this article, by fusing 3-D multisource ionospheric data, and improving the stochastic model, an improved GNSS tomographic algorithm MFCIT [computerized ionospheric tomography (CIT) using mapping function] is presented. The accuracy of the algorithm is validated by selected data under different geomagnetic and solar conditions acquired in Europe. The results show that the estimated, statistically significant uncertainty for each of the layers is about 0.50-3.0TECU, with the largest absolute error within 6.0TECU. The advantage of the MFCIT is that it is based on the Kalman filter, which enables efficient near real-time 3-D monitoring of ionosphere. The temporal resolution can reach ~1 min level. Here, we apply the ionospheric tomography inversion to the magnetic storm on January 7, 2015, in the European region, and quantified the evolution of the storm. The results show that the difference of the core region between the MFCIT and CODE GIM is less than 1TECU. More importantly, during the initial phase of the storm, when the ionospheric disturbance is not evident in the single layer CODE GIM model, the MFCIT shows obvious positive disturbances in the upper ionosphere, although there is no disturbance in the F2 layer. The MFCIT further tracks the evolution of the magnetic storm that the ionospheric disturbance expands from the upper to the lower ionosphere layers, and at UT12:00, the disturbance continues to spread to the F2 layer.

Using the Global Navigation Satellite System (GNSS) differenced total electron content (dTEC) series, the traveling ionosphere disturbances (TIDs) of 22 typhoons registered in Taiwan/Japan between 2013 and 2016 were studied. The horizontal speed of the first TID during a typhoon landing can be estimated by a two-station method with the ionosphere anomaly indicator in total electron count units (TECUs) (|dTEC| ≥ 0.15 TECU). The horizontal speed of the TIDs was from 155 to 210 m/s and with an average speed of 168.70 m/s. The estimated TID speeds of Typhoons Soudelor (205.93 m/s) and Megi (158.47 m/s) are not consistent with each other, even though they had very similar trajectories when cross through Taiwan Island. Moreover, the propagation velocity of the typhoon ionospheric anomaly showed a significant positive correlation ( r = 0.78, α = 0.05) with the change rate of the typhoon central air pressure and a negative correlation ( r = -0.52, α = 0.05) with the central pressure before landing. Gravity waves were generated by land friction, terrain blocking, and strong wind shear transport energy into the atmosphere from the near surface to the mesosphere and thermosphere, which is the main cause of ionosphere disturbances during typhoon landing.

In order to improve the accuracy of the International Reference Ionosphere (IRI)-2016 model for application in the Antarctic region, total electron content (TEC) data from the Global Navigation Satellite Systems (GNSS) observation data in 2018 are assimilated into the IRI-2016 model by updating the effective ionospheric parameter, IG12 index on a daily basis. The functional relationship between the IG12 index and the longitude, latitude, and the day of year (DOY) is fitted by using the spherical crown harmonic function and the polynomial, and finally establish an updated experiential model of IG12 indices over the Antarctic region. Conclusions that were reached were: 1) the updated IG12 index varies greatly over different geographical locations and 2) it is also apparent that the accuracy of the IRI-2016 model is worse in the perpetual night than that in the perpetual day. In order to verify our method, the TEC calculated by the IRI-2016 model driven by the updated IG12 index and that calculated by the original IRI-2016 model are compared with the GNSS-TEC, and the results show that the updated IRI-2016 model has improved the accuracy of the BIAS and root mean square (RMS) of the TEC calculation by 97% and 87%, respectively, on the fitting moments, while 75% and 54% on the predicting moments. In addition, compared with the original IRI-2016 model, it is found that the updated IRI-2016 model improves the accuracy of the NmF2 calculation by approximately 23% on average for the fitting time and 8% for the predicting time.

Computerized ionospheric tomography is an important technique for ionosphere investigation. However, it is an ill-posed problem owing to an insufficient amount of available data, because of which the distributions of ionospheric electron density (IED) cannot be reconstructed accurately. In light of this, the ordered subsets-constrained algebraic reconstruction technique (OS_CART) is developed here using vertical total electron content (VTEC) data to solve this problem, where the VTEC derived from the slant total electron content (STEC) of Global Navigation Satellite System (GNSS) signal paths is used to compensate for the lack of data provided by GNSS observations in inversion regions, and the OS_CART is also used to improve the spatial resolution and inversion efficiency. The proposed method was validated by conducting numerical experiments using GNSS and independent ionosonde data in both quiescent and disturbed ionospheric conditions. In contrast to classical methods of ionospheric tomography, the proposed method exhibited significantly higher reconstruction accuracy. While delivering a comparable accuracy to that of traditional methods in terms of self-consistency validation using STEC data and without overfitting, the proposed method yielded a more than 90% improvement over the self-consistency validation using VTEC data. In addition, a better daily description of the ionosphere was obtained using the proposed method, where an increase in the peak height and irregular changes to the IED, associated with variations in the number of epochs and the occurrence of magnetic storms, were observed. Overall, the results reveal that the proposed method is a useful tool for research on space weather.

Except for its known aspects of positioning, navigation, and timing (PNT), the Global Navigation Satellite System (GNSS) has extended its application to the rainfall forecasting. GNSS-derived zenith total delay (ZTD) or precipitable water vapor (PWV) has been used as a single factor to predict the occurrence of rainfall; however, the rainfall is highly correlated with myriad atmospheric parameters, which cannot be perfectly reflected by a single predictor. In this article, an improved rainfall forecasting model (IRFM) is proposed to forecast the rainfall. The IRFM considers five predictors: monthly PWV value, seasonal PWV/ZTD variations, and their first derivatives: it can forecast rainfall using a single predicator or an arbitrary combination of those predicators. The merit of IRFM is reducing the false forecasted rainfall (FFR) events and missed detected rainfall (MDR) events as much as possible while guaranteeing the true detected rainfall (TDR) events. An optimized selecting principle of predictors' threshold has been determined using the percentile method. The test experiment has been performed using five GNSS stations derived from continuously operating reference system (CORS) network of Zhejiang province, China. The analysis reveals that the IRFM considering five predictors provides a better performance than that only using a single predictor or a combination of arbitrary predictors. The statistical result shows the average TDR value of more than 95%, FFR value of less than 30%, and MDR of less than 5%, respectively. Compared to the existing rainfall forecasting methods using ZTD or PWV, the IRFM reduces the FFR and MDR, respectively, with the lowest values, while the TDR value is the highest.

We present the observational evidence of Eand Fregion field-aligned irregularities (FAIs), nighttime medium-scale traveling ionospheric disturbances (MSTIDs), and a sporadic E (ES)-layer using the Wuhan very-high-frequency (VHF) coherent scatter radar, Wuhan Global Navigation Satellite System (GNSS) network, and Wuhan ionosonde. We observed simultaneously E and F FAIs by VHF radar and the ES-layer and spread-F by Wuhan ionosonde. We also observed MSTIDs using the Wuhan GNSS network in a southwestward direction of propagation and horizontal propagation velocity of less than 180 m/s, for a period of ~33 min. Simultaneous observations of an ES-layer and FAIs in the E-region and FAIs in the F-region suggested the existence of an electrodynamic coupling between the E and F regions in the midand low-latitude nighttime ionosphere. A polarized electric field associated with nighttime MSTIDs can generate the uplift movements of the F-region's electron density. This uplift effect consequently can excite the gradient drift instability (GDI) with an accompanying enhanced vertical gradient of the F-layer's electron density. Our results indicated that the F-region MSTIDs excited by Perkins instability might be further modulated by the E × B effect and subsequently can evolve to spread-F like FAIs. Our observational investigation provided the first evidence of the full dynamics and links among different ionospheric disturbances in a midand low-latitude nighttime region of China.

GPS-based water vapor tomography has been proved to be a cost-effective means of obtaining spatial and temporal distribution of atmospheric water vapor. In previous studies, the tomography height is empirically selected without considering the actual characteristics of the local water vapor distribution, and most existing studies only consider the signals passing from the top boundary of the tomography area. Therefore, the observed signals coming out from the side face of the tomography area are excluded as ineffective information, which not only reduces the utilization rate of signals used but also decreases the number of voxels crossed by rays. This becomes the research point of this paper, which studies the possibility of selecting a reasonable tomography boundary and using signals passing from the side face of the tomography area. This paper first tries to determine the tomography height based on the local atmospheric physical property using many years of radiosonde data, and 8 km is selected as the tomography boundary in Hong Kong. The second part focuses on superimposing the signals penetrating from the side face of the tomography area to tomography modeling by introducing a scale factor that is able to determine the water vapor content of each signal with the part that belongs in the tomography area. Finally, a tomography experiment is carried out based on data provided by the Satellite Positioning Reference Station Network (SatRef) in Hong Kong to validate the proposed method. Experimental result demonstrates that the utilization rate of the signal used and the number of voxels crossed by rays are both increased by 30.32% and 12.62%, respectively. The comparison of tomographic integrated water vapor (IWV) derived from different schemes with that from radiosonde and ECMWF data shows that the RMS error of the proposed method (4.1 and 5.1 mm) is smaller than that of the previous method (5.1 and 5.6 mm). In addition, the tomographic water vapor densities derived from different schemes is also compared with those of by radiosonde and ECMWF; the statistical result over the experimental period shows that the proposed method has an average RMS error of 1.23 and 2.12 g/m3, respectively, which is superior to the previous method at 1.60 and 2.43 g/m3, respectively.

In this letter, we investigate the ionospheric variations associated with the Yaan earthquake that occurred on April 20, 2013 in China by using the total electron content (TEC) derived from ground-based Global Positioning System observations and a global ionosphere map (GIM). Geomagnetic and solar activities are taken into account. First, we focus on the coseismic ionospheric disturbances of the earthquake. The time period of the variations is about 15 min after the seismic rupture, and the maximum amplitude is about 0.1 TEC units. We then examine the preseismic ionospheric anomalies by the TEC values from the GIM and the electron density (Ne) values reconstructed by computerized ionospheric tomography. Temporal variations show that the TEC and Ne values simultaneously increased on April 5-8, 2013, which are 12-15 days before. This increase is possibly related to the earthquake. Spatial analysis shows that anomalies tend to appear around the epicenter and their conjugate points.

Ionosphere tomography is a typical ill-posed problem, and using the ionosphere priori information as the constraints to improve the state of normal equation is an effective approach to solve this problem. In this paper, we impose priori constraints by increasing the virtual observations in $n$-dimensional space. Then, after the inversion region to be gridded, we can form a stable structure between the grids with loose constraints, which greatly improve the state of normal equation. Based on that, to obtain the real-time velocity information of ionosphere electron density, we introduce the grid electron density velocity parameters, which can be estimated with electron density parameters simultaneously. The authors use the new algorithm and the global navigation satellite system data in Europe to inverse the 12 ionosphere electron density and velocity images on August 15, 2003, which reflects the ionosphere changes of electron density and velocity in whole day; in addition, we compare the results with the traditional algorithm multiplicative algorithm reconstruction technique's results. Furthermore, we compare the results with the changes time series of plasma frequency observed by ionosphere ionosonde among different layers, and many types of analysis and comparison verify the effectiveness and reliability of the new algorithm. The related research provides a new way for the real-time detection and prediction of ionosphere changes.

The computerized ionospheric tomography usually involves solving an ill-posed inversion problem. The sparsity of Global Positioning System (GPS) stations and the limitation of projection angles lead to insufficient data acquisition, thereby preventing the accurate reconstruction of ionospheric-electron-density distributions. In this paper, we investigate and propose a 3-D iterative reconstruction algorithm based on the minimization of total variation under quiescent and disturbed ionospheric conditions. Numerical experiments on GPS simulation data and real data are discussed. In contrast to the improved algebraic reconstruction technique, the proposed algorithm exhibits significantly reconstruction accuracy.