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.

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 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.

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.

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.

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.

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.