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Missing data in precipitable water vapor derived from global navigation satellite systems (GNSS-PWV) is commonly a large hurdle in climatical applications, since continuous PWV is an important prerequisite. Interpolation using principal component analysis (PCA) is typically used to resolve this problem. However, the popular PCA-based interpolating methods, e.g., rank-deficient least squares PCA (RDPCA) and data interpolating empirical orthogonal function (DINEOF), often lead to unsatisfactory results. This study analyzes the relationship between missing data and PCA-based interpolation results and proposes an improved interpolation-based RDPCA (IRDPCA) that can take into account the PWV derived from ERA5 (ERA-PWV) as an additional aid. Three key steps are involved in the IRDPCA: initially interpolating missing data, estimating principal components through a functional model and optimizing the interpolation through an iterative process. Using a 6-year GNSS-PWV over 26 stations and ERA-PWV in Yunnan, China, the performance of the IRDPCA is compared with the RDPCA and DINEOF using simulation experiments based on both homogeneous data (i.e., interpolating ERA-PWV using available ERA-PWV) and heterogeneous data (i.e., interpolating GNSS-PWV using ERA-PWV). In the case of using homogeneous data, the root mean square (RMS) values of the interpolation errors are 3.45, 1.18 and 1.17 mm for the RDPCA, DINEOF and IRDPCA, respectively; while the values are 3.50, 2.50 and 1.55 mm in the heterogeneous case. These results demonstrate the superior performance of the IRDPCA in both the heterogeneous and homogeneous cases. Moreover, these methods are also applied to the interpolation of the real GNSS-PWV. The RMS, absolute bias and correlation of the GNSS-PWV are calculated by comparison with ERA-PWV. The results reveal that the interpolated GNSS-PWV using the IRDPCA is not impacted by the systematic discrepancies in the ERA-PWV and agrees well with the original data. [ABSTRACT FROM AUTHOR]

Precipitable water vapor (PWV) retrieved from Global Navigation Satellite Systems (GNSS) observations (GNSS-PWV) subjects to non-climatic changepoints (NCCs), mainly due to hardware (e.g., antenna and receiver) changes and geological events, while only the former is documented in station metadata. This study proposed a new strategy to verify the origins of the undocumented NCCs in GNSS-PWV and correct the inhomogeneity based on ERA5 and coordinate time series of GNSS stations. The new strategy initially applies a zero-difference method to detect changepoints in GNSS coordinate time series (coordinate changepoints) and undocumented NCCs in GNSS-PWV with the aid of PWV derived from ERA5 (ERA-PWV) and station metadata. The coordinate changepoints are treated as ancillary to verify the origins of the undocumented NCCs related to geological events in GNSS-PWV since coordinates are also influenced by geological events. After the verification, shifts and directions of all NCCs were robustly estimated and corrected for a final homogenization. The strategy was applied in the GNSS-PWV at 207 stations during 2008–2018 in China. Results showed that among 193 NCCs detected in 110 inhomogeneous GNSS-PWV, only 5 ones (3% of NCCs) were documented in metadata, and 44 ones (23% of NCCs) were related to geological events, indicating the good performance of the new strategy on the identification of the undocumented NCCs. After the homogenization, the RMS and absolute bias between GNSS-PWV and ERA-PWV were decreased by 5% and 22%, and the correlation coefficient of their linear trends was improved to 0.98 from 0.75. [ABSTRACT FROM AUTHOR]

Precipitable water vapor (PWV) is an important meteorological factor for predicting extreme weather events such as tropical cyclones, which can be obtained from zenith tropospheric delay (ZTD) by using a conversion. A time difference of ZTD arrival (TDOZA) model was proposed to monitor the movement of tropical cyclones, and the fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting (ERA5)-derived ZTD (ERA5-ZTD) was used to estimate the movement of tropical cyclones based on the model. The global navigation satellite system-derived ZTD and radiosonde data-derived PWV (RS-PWV) were used to test the accuracy of the ERA5-ZTD and analyze the correlation between ZTD and PWV, respectively. The statistics showed that the mean Bias, RMS and STD of the ERA5-ZTD were 6.4 mm, 17.1 mm and 16.5 mm, respectively, and the mean correlation coefficient of the ERA5-ZTD and RS-PWV was 0.951, which indicates that the ZTD can be used to predict weather events instead of PWV. Then, spatiao-temporal characteristics of ZTD during the four tropical cyclone (i.e., Merbok, ROKE, Neast and Hato) periods in 2017 were analyzed, and the result showed that the moving directions of ZTD and the tropical cyclones were consistent. Thus, the ZTD time series over the ERA5 grids around the tropical cyclones' paths were used to estimate the velocity of the tropical cyclones based on the TDOZA model, when the tropical cyclones are approaching or leaving. Compared with the result from the China Meteorological Administration, the mean absolute and relative deviations of the TDOZA model-derived velocity were 2.55 km/h and 10.0%, respectively. These results suggest that ZTD can be used as a new supplementary meteorological parameter for monitoring tropical cyclone events. [ABSTRACT FROM AUTHOR]

This paper presents a new model for ionospheric total electron content (TEC) over China. The new model is developed using a hybrid method composed of the particle swarm optimization (PSO) and artificial neural network and long‐term observations from 257 ground‐based global navigation satellite systems (GNSS) stations and space‐borne GNSS radio occultation systems (COSMIC and Fengyun) during the 14‐year period of 2008–2021. The PSO algorithm is used to optimize the traditional back‐propagation neural network (BP‐NN) model by reducing the effects of the local minimum problem. The new model is validated using out‐of‐sample data, and its results are compared to the BP‐NN, IRI‐2016 model, and global ionospheric maps provided by the International GNSS Service. Results show that TEC predicted from the new model agrees better with the reference TEC than the BP‐NN and IRI‐2016 models. The improvements made by the new model over the BP‐NN and IRI‐2016 models in the equinox, summer, and winter seasons of the solar maximum year (2015) are 4%–20%/20%–36%, 9%–21%/26%–42%, and 6%–22%/21%–43%, respectively, and their corresponding results in the solar minimum year (2019) are 12%–24%/41%–59%, 9%–24%/28%–56%, and 10%–26%/53%–72%. Furthermore, the new model well captures the diurnal evolution, seasonal variation, and variations in the ionospheric TEC under different solar activity levels. It also well captures the mid‐latitude summer nighttime anomaly over China, and the diurnal anomaly is more pronounced in the solar minimum year (2019) than in the solar maximum year (2015) in terms of the nighttime‐to‐noontime ratio and the range of months it lasts in a year. Plain Language Summary: With the continuous expansion of ground‐based and space‐borne global navigation satellite systems (GNSS) data sets, more and more high‐resolution, high‐accuracy, and long‐term measurements are available. These data can be used for characterizing, modeling, and forecasting the ionosphere. However, due to the limitation of data analysis tools and modeling techniques, only a fraction of the huge data set has been used. As proven by previous research, the back‐propagation neural network (BP‐NN) is an efficient approach in exploring hidden patterns from a large data set. Nevertheless, BP‐NN has shortcomings, for example, the possible local minimum and slow convergence rate problem. Therefore, this study uses the particle swarm optimization neural network and long‐term observations obtained from 257 ground‐based GNSS stations and space‐borne GNSS radio occultation systems (COSMIC and Fengyun) to develop a new model of ionospheric total electron content (TEC) over China. The new model captures the TEC diurnal evolution, seasonal variation, and variations under different solar activity levels. Moreover, the new model also well captures the mid‐latitude summer nighttime anomalies over China. This new model offers more opportunities for the investigation of large‐scale ionospheric phenomena over China, like the distribution of the mid‐latitude summer nighttime anomaly and its month‐to‐month variations. Key Points: A particle swarm optimization neural network based total electron content (TEC) model over China is developed using observations of ground‐based global positioning system, COSMIC, and Fengyun during 2008–2021The new TEC model performs better than the IRI‐2016 model and back‐propagation neural network model under high and low solar activity levelsMid‐latitude summer nighttime anomaly is well captured by the new model, and the diurnal anomaly is more pronounced in 2019 than in 2015 [ABSTRACT FROM AUTHOR]

Water vapor is a dominant greenhouse gas. It significantly impacts the atmosphere by trapping heat and infrared radiation. The greenhouse effect is essential for life on Earth but can also be harmful. Although the amount of water vapor in the atmosphere is not much during the water cycle, it is the most active element in rapid changes in both spatial and temporal domains. GNSS tomography's ability to model the high-resolution 3D distribution of water vapor is a promising means of measuring and monitoring the spatial-temporal variation of water vapor. This study developed and tested a new GNSS tomographic model using adaptive voxel parameterization. It uses a 3D traversal algorithm to dynamically determine the position of voxels at each tomographic sampling epoch. It means that the new algorithm can exclude the voxels that no GNSS signals pass through, reducing the influence of such voxels in the construction of the tomographic model. This study provides a new approach to investigating the inversion of atmospheric water vapor. The experiment used one-month data from the Hong Kong network in September 2020, and the results were compared with the general system. The local radiosonde data is a reference for verification of the two approaches. The mean root-mean-square error (RMSE) and IQR of the water vapor profiles derived from AAR are decreased by 55% and 48% with respect to the GFR results, respectively. The results show that the accuracy of the new method outperforms the general approach in the result statistics. The successful implementation of the research has significant potential to drive the development of GNSS tomography in the study of weather and climate change. [ABSTRACT FROM AUTHOR]

One of the main challenges of Global Navigation Satellite System (GNSS) tomography is in solving ill-conditioned system equations. Vertical constraint models are typically used in the solution procedure and play an important role in the quality of the GNSS tomography, in addition to helping resolve ill-posed problems in system equations. In this study, based on a water vapor (WV) parameter, namely IRPWV, a new vertical constraint model with six sets of coefficients for six different WV states was developed and tested throughout 2019 in the Hong Kong region with four tomographic schemes, which were carried out with the model and the traditional vertical constraint model using three different types of water vapor scale height parameters. Experimental results were numerically compared against their corresponding radiosonde-derived WV values. Compared with the tests that used the traditional model, our results showed that, first, for the daily relative error of WV density (WVD) less than 30%, the new model can lead to at least 10% and 49% improvement on average at the lower layers (below 3 km, except for the ground surface) and the upper layers (about 5–10 km), respectively. Second, the skill score of the monthly root-mean-square error (RMSE) of layered WVD above 10 accounted for about 83%, 87%, and 64%. Third, for the annual biases of layered WVD, the new model significantly decreased by 1.1–1.5 g/m3 at layers 2–3 (about 1 km), where all schemes showed the maximal bias value. Finally, for the annual RMSE of layered WVD, the new model at the lower (about 0.6–3 km) and upper layers improved by 13–42% and 5–47%, respectively. Overall, the new model performed better on GNSS tomography and significantly improved the accuracy of GNSS tomographic results, compared to the traditional model. [ABSTRACT FROM AUTHOR]

Potential evapotranspiration (PET) is generally estimated using empirical models; thus, how to improve PET estimation accuracy has received widespread attention in recent years. Among all the models, although the temperature-driven Thornthwaite (TH) model is easy to operate, its estimation accuracy is rather limited. Although previous researchers proved that the accuracy of TH-PET can be greatly improved by using a limited number of variables to conduct calibration exercises, only preliminary experiments were conducted. In this study, to refine this innovation practice, we comprehensively investigated the factors that affect the calibration performances, including the selection of variables, seasonal effects, and spatial distribution of Global Navigation Satellite System (GNSS)/weather stations. By analyzing the factors and their effects, the following conclusions have been drawn: (1) an optimal variable selection scheme containing zenith total delay, temperature, pressure, and mean Julian Date was proposed; (2) the most salient improvements are in the winter and summer seasons, with improvement rates over 80%; (3) with the changes in horizontal (2.771–44.723 km) and height (1.239–344.665 m) differences among ten pairs of GNSS/weather stations, there are no obvious differences in the performances. These findings can offer an in-depth understanding of this practice and provide technical references to future applications. [ABSTRACT FROM AUTHOR]

Global navigation satellite systems (GNSS) has been applied to the sounding of precipitable water vapor (PWV) due to its high accuracy and high spatiotemporal resolutions. PWV obtained from GNSS (GNSS-PWV) can be used to investigate extreme weather phenomena, such as the formation mechanism and prediction of rainfalls. In the study, a new, improved model for rainfall forecasting was developed based on GNSS data and rainfall data for the 9-year period from 2010 to 2018 at 66 stations located in the USA. The new model included three prediction factors—PWV value, PWV increase, maximum hourly PWV increase. The two key tasks involved for the development of the model were the determination of the thresholds for each prediction factor and the selection of the optimal strategy for using the three prediction factors together. For determining the thresholds, both critical success index (CSI) and true skill statistic (TSS) were tested, and results showed that TSS outperformed CSI for all rainfall events tested. Then, various strategies by combining the three prediction factors together were also tested, and results indicated that the best forecast result was from the case that any two of the prediction factors were over their own thresholds. Finally, the new model was evaluated using the GNSS data for the 2-year period from 2019 to 2020 at the above mentioned 66 stations, and the probability of detection (POD) and false-alarms rate (FAR) were adopted to measure the model performances. Over the 66 stations, the POD values ranged from 73% to 97% with the mean of 87%, and the FARs ranged from 26% to 77% with the mean of 53%. Moreover, it was also found that both POD and FAR values were related to the region of the station; e.g., the results at the stations that are located in humid regions were better than the ones located in dry regions. All these results suggest the feasibility and good performance of using GNSS-PWV for forecasting rainfall. [ABSTRACT FROM AUTHOR]

As a frequently-occurred phenomenon in the high-latitude region, ionospheric scintillations affect the stable service of the positioning navigation and timing service of the Global Navigation Satellite System (GNSS), calling for an urgent need of monitoring the scintillations accurately. The monitoring of scintillations usually adopts a special type of receiver, called an ionospheric scintillation monitoring receiver (ISMR), which cannot cover the whole high-latitude region due to its loss distribution. Geodetic receivers are densely distributed, but set at a 30s-sampling-interval usually. It is a controversial issue, namely, the accuracy of the scintillation index extracted from 30s-sampling-interval observations. This paper evaluates the accuracy of two 30s-sampling-interval indices in monitoring scintillations from both the time and space aspects using observations collected in the whole year of 2020. The accuracy in the time aspect is assessed with the phase scintillation index from ISMR as the reference through the following three-pronged approaches, i.e., the accuracy of the daily scintillation occurrence rates in the year 2020, the correlation with space weather parameters, and the variation pattern of the scintillation occurrence rate with the local time and day of the year 2020. The accuracy in space is studied based on the scintillation grid model considering the following two aspects, i.e., the scintillation monitoring performance in a Swarm satellite observation arc, and the statistical scintillation occurrence rate in the whole research region throughout the year 2020. The results of this paper reveal the efficiency of the 30s-sampling-interval scintillation indices in monitoring scintillations and detecting the occurrence patterns in the high-latitude region. The outcome of this paper can provide a basic idea for introducing the widely distributed geodetic receivers to monitor and model the scintillations in the high-latitude region. [ABSTRACT FROM AUTHOR]

This paper focuses on sea surface wind speed estimation using L1B level v3.1 data of reflected GNSS signals from the Cyclone GNSS (CYGNSS) mission and European Centre for Medium-range Weather Forecast Reanalysis (ECMWF) wind speed data. Seven machine learning methods are applied for wind speed retrieval, i.e., Regression trees (Binary Tree (BT), Ensembles of Trees (ET), XGBoost (XGB), LightGBM (LGBM)), ANN (Artificial neural network), Stepwise Linear Regression (SLR), and Gaussian Support Vector Machine (GSVM), and a comparison of their performance is made. The wind speed is divided into two different ranges to study the suitability of the different algorithms. A total of 10 observation variables are considered as input parameters to study the importance of individual variables or combinations thereof. The results show that the LGBM model performs the best with an RMSE of 1.419 and a correlation coefficient of 0.849 in the low wind speed interval (0–15 m/s), while the ET model performs the best with an RMSE of 1.100 and a correlation coefficient of 0.767 in the high wind speed interval (15–30 m/s). The effects of the variables used in wind speed retrieval models are investigated using the XGBoost importance metric, showing that a number of variables play a very significant role in wind speed retrieval. It is expected that these results will provide a useful reference for the development of advanced wind speed retrieval algorithms in the future. [ABSTRACT FROM AUTHOR]

The ionospheric total electron content (TEC) is an important ionospheric parameter, and it is widely utilized in research such as space weather prediction and precise positioning. However, it is still challenging to develop an ionospheric TEC prediction model with high accuracy. In this study, a new ionospheric TEC model over China was developed using the bidirectional long short‐term memory (bi‐LSTM) method and observations from 257 ground‐based global navigation satellite system (GNSS) stations in the Crustal Movement Observation Network of China from January 2018 to December 2021. The root mean square errors of the bi‐LSTM‐based model's 1 and 2 hr ahead predictions on the test data set (from June 2021 to December 2021) are 1.12 and 1.68 TECU, respectively, which are 75/50/32% and 72/48/22% smaller than those of the IRI‐2016, artificial neural network and LSTM‐based models, correspondingly. The bi‐LSTM‐based model shows the best performance, which is most likely due to the fact that the sequence information in both forward and backward directions is taken into consideration in the new model. In addition, the diurnal variation, seasonal variation of the ionospheric TEC, and variations under geomagnetic storm conditions are successfully captured by the bi‐LSTM‐based model. Moreover, the TEC maps resulting from the bi‐LSTM model agree well with those obtained from the final ionospheric product from the Chinese Academy of Sciences. Hence, the new model can be a good choice for the investigation of the spatiotemporal variation trend in the ionosphere and GNSS navigation. Plain Language Summary: The ionospheric total electron content (TEC) is the total number of free electrons along a signal propagation path in a cross section of one square meter, and the ionospheric delay error of the signal from the global navigation satellite system (GNSS) is proportional to the value of TEC. The variation of the ionospheric TEC can also be used to study the variations in space weather. In this study, a new ionospheric TEC model over China based on the bidirectional long short‐term memory (bi‐LSTM) method and long‐term ground‐based observations at 257 GNSS stations from the Crustal Movement Observation Network of China was developed and subsequently validated. The main reason for the selection of the bi‐LSTM model is for its consideration of the sequence information in both forward and backward directions. The prediction results of the new model show that in comparison with that of three other models, including the artificial neural network, LSTM, and traditional IRI‐2016 models, the new model is the best performer. Furthermore, the bi‐LSTM model also successfully captures the diurnal and seasonal variations of the ionospheric TEC and the TEC variations under geomagnetic storm conditions. Key Points: The bidirectional long short‐term memory (bi‐LSTM) method is adopted to predict the ionospheric total electron content maps over China using long‐term ground‐based global positioning system observations from the Crustal Movement Observation Network of ChinaThe bi‐LSTM‐based model outperforms the IRI‐2016, artificial neural network and long short‐term memory‐based modelsThe bi‐LSTM‐based model can predict the ionospheric response during geomagnetic storm periods, such as the 4–7 November 2021 G3 storm period [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSS) tomography is a well-recognized modeling technique for reconstruction, which can be used to investigate the spatial structure of water vapor with a high spatiotemporal resolution. In this study, a refined near real-time tomographic model is developed based on multi-source data including GNSS observations, Global Forecast System (GFS) products and surface meteorological data. The refined tomographic model is studied using data from Hong Kong from 2 to 11 October 2021. The result is compared with the traditional model with physical constraints and is validated by the radiosonde data. It is shown that the root mean square error (RMSE) values of the proposed model and traditional model are 0.950 and 1.763 g/m3, respectively. The refined model can decrease the RMSE by about 46%, indicating a better performance than the traditional one. In addition, the accuracy of the refined tomographic model is assessed under both rainy and non-rainy conditions. The assessment shows that the RMSE in the rainy period is 0.817 g/m3, which outperforms the non-rainy period with the RMSE of 1.007 g/m3. [ABSTRACT FROM AUTHOR]

A method is proposed to improve the GPS broadcast ionospheric time-delay correction accuracy, using GPS observation data from the globally distributed international GNSS service (IGS) observation stations and the Crust Movement Observation Network of China (CMONOC). A new set of Klobuchar-Self coefficients can be estimated using the method. Primary results demonstrate that the refined Klobuchar-Self coefficients developed may provide better ionospheric delay corrections for single-frequency GPS receivers and improve standard single point positioning accuracies.

The adverse effect of the ionospheric scintillation on Global Navigation Satellite System (GNSS) requires scintillation monitoring on a global scale. Ionospheric Scintillation Monitoring Receivers (ISMR) are usually adopted to monitor scintillation, while they are not suitable for global monitoring due to the 50 Hz data collecting rate, which restricts the distribution. This paper proposes a new method to extract the phase scintillation index from each GNSS carrier with 1s‐sampling‐interval, mainly based on the cycle slip detection, the geodetic detrending and the wavelet transform, in which the optimal symmetry parameter and the time‐bandwidth product are determined with trial calculation. Taken the σϕ ${\sigma }_{\phi }$ index provided by ISMR as the reference, 1‐year observations are utilized to evaluate the scintillation monitoring performance of the extracted index regarding the correlation of the magnitude in each observation arc, the detected daily scintillation occurrence rate, the diurnal variation pattern of the ionospheric scintillation, the correlation between the scintillation occurrence rate and the space weather parameter, and the complementary cumulative distribution of the magnitudes. Compared to the performance of Rate of Total electron content Index, a higher consistency can be achieved between the extracted index and the σϕ ${\sigma }_{\phi }$ index, indicating the rationality of applying the proposed method in monitoring scintillations. The extracted scintillation index can be expected to introduce geodetic receivers operating at 1s‐sampling‐interval into the field of ionospheric scintillation monitoring on a global scale. Plain Language Summary: Small scale structures in the ionosphere will interfere with the amplitude and phase of the Global Navigation Satellite System (GNSS) signals passing through them. This phenomenon is called ionospheric scintillation, which can affect the positioning navigation and timing services of GNSS, calling for the need of monitoring ionospheric scintillations on a global scale. A special type of receiver, called Ionospheric Scintillation Monitoring Receiver (ISMR), is usually needed in ionosphere monitoring. However, due to the high sampling rate and price, the distribution of ISMR is limited, making it impossible to realize large‐scale ionospheric scintillation monitoring using only ISMR. In order to solve this problem, this paper proposes a new method to extract the scintillation index from the widely distributed geodetic receivers. Although the existing Rate of Total electron content Index (ROTI) can also use geodetic receivers for ionospheric scintillation monitoring, experiments based on long‐period observations collected in the Arctic region of the North America show that the extracted scintillation index can be more accurate than ROTI and the scintillation information on each carrier can be provided. The extracted scintillation index provides a basis for realizing global ionospheric scintillation monitoring and modeling in the future. Key Points: A new method is proposed to extract the scintillation index by applying cycle slip detection, geodetic detrending and wavelet transformThe scintillation index can be extracted from each carrier of the 1s‐sampling‐interval Global Navigation Satellite System observationsThe extracted scintillation index can provide a consistent performance in monitoring scintillation as that given by Ionospheric Scintillation Monitoring Receiver [ABSTRACT FROM AUTHOR]

In recent years, tropospheric products obtained from ground-based global navigation satellite system (GNSS) measurements, especially the zenith total delay (ZTD) and precipitable water vapor (PWV) estimates, have advanced their usages in meteorological applications such as the detection of precipitation events. Generally, a cumulative anomaly (CA) time series of any atmospheric variable, which represents the long-term departure of the variable from its “normal” cycle, is widely used for quantitatively estimating the variable’s variations in response to a weather event. In this study, a new cumulative anomaly-based model (NCAM) containing 14 variables, including not only PWV and ZTD values but also their respective six types of derivatives, for detecting heavy precipitation was developed. The 6-h CA time series of the variables were calculated based on the data of hourly precipitation records and time series of ZTD and PWV collected at the co-located HKSC–King’s Park (KP) stations over the eight-year period 2010–2017. The model was evaluated using the 14 variables’ CA time series to detect heavy precipitation events happened in the summer months over the period 2018–2019, and precipitation records in the same period were used as the reference. Results demonstrated that 99.1% of heavy precipitation was correctly detected by the NCAM with a lead time of 2.87 h, and the false alarm ratio (FAR) score resulting from the model was reduced to 22.4%. In addition, two case studies were also conducted to verify the effectiveness of the NCAM. These results all provide a promising direction for the application of using the CA time series of GNSS tropospheric products to the detection of heavy precipitation events. [ABSTRACT FROM AUTHOR]

Geomagnetic storms on 7–8 September 2017 triggered severe ionospheric disturbances that had a serious effect on satellite navigation and radio communication. Multiple observations derived from Global Navigation Satellite System receivers, Earth's Magnetic Field and Environment Explorers (SWARM) and the Thermosphere‐Ionosphere ‐Electrodynamics General Circulation Model's simulations are utilized to investigate the spatial‐temporal ionospheric behaviors under storm conditions. The results indicate that the electron density in the Asia‐Australia, Europe‐Africa and America sectors suddenly changed with the Bz southward excursion, and the ionosphere over low‐middle latitudes under the sunlit hemisphere is easily affected by the disturbed magnetic field. The SWARM observations verified the remarkable double‐peak structure of plasma enhancements over the equator and middle latitudes. The physical mechanism of low‐middle plasma disturbances can be explained by a combination effect of equatorial electrojets, vertical E × B drifts, meridional wind and thermospheric O/N2 change. Besides, the severe storms triggered strong Polar plasma disturbances on both dayside and nightside hemispheres, and the Polar disturbances had a latitudinal excursion associated with the offset of geomagnetic field. Remarkable plasma enhancements at the altitudes of 100–160 km were also observed in the auroral zone and middle latitudes (>47.5°N/S). The topside polar ionospheric plasma enhancements were dominated by the O+ ions. Furthermore, the TIE‐GCM's simulations indicate that the enhanced vertical E × B drifts, cross polar cap potential and Joule heating play an important role in generating the topside plasma perturbations. Key Points: Positive disturbances over low‐middle latitudes only appear on the dayside, but polar disturbances occur on both dayside and nightsideStorms trigger bottomside plasma enhancements over middle‐high latitudes (>47.5 N/S) that have a latitudinal offset with geomagnetic fieldA combination of equatorial electrojet, E × B drifts, meridional wind and O/N2 change is responsible for low‐middle latitudinal disturbances [ABSTRACT FROM AUTHOR]

One of the challenges in the innovative application of global navigation satellite systems (GNSS) lies in real-time single-frequency precise point positioning (RT-SFPPP). The well-known problems associated with SFPPP are its slow convergence and lower positioning accuracy due to the effects of various errors inherited by the GNSS positioning, including the atmospheric error. In order to mitigate the two above-mentioned problems, several scenarios for the reduction of the atmospheric delays in RT-SFPPP, including the ionospheric constraint and tropospheric constraint, were investigated in this research. The performance of the RT-SFPPP utilizing the above-mentioned methods was evaluated using one-week observations from multi-GNSS experiment stations in a simulated kinematic mode. Results showed that the convergence time of the RT-SFPPP was significantly shortened when the slant ionospheric delays derived from the real-time ionospheric products provided by Centre National d'Études Spatiales were applied as the pseudo-observations. Results also showed that the convergence time of GPS + GLONASS RT-SFPPP can be reduced by modeling the frequency-dependent part of the GLONASS receiver uncalibrated code delay as a quadratic polynomial function of GLONASS frequency number. The a priori zenith wet delays (ZWDs) derived from forecast Vienna Mapping Functions 3 (VMF3_FC) were compared to the reference ZWDs from globally distributed radiosonde stations. The mean root mean square error of the a priori ZWDs resulting from VMF3_FC at 381 radiosonde stations in 2020 was 1.53 cm compared to the radiosonde-based ZWDs. When the ZWDs derived from VMF3_FC were used as pseudo-observations in the position estimation system, the convergence time for the vertical positioning reaching a 0.3 m accuracy was considerably shortened. [ABSTRACT FROM AUTHOR]

The quality of the zenith hydrostatic delay (ZHD) could significantly affect the accuracy of the zenith wet delay (ZWD) of the Global Navigation Satellite System (GNSS) signal, and from the ZWD precipitable water vapor (PWV) can be obtained. The ZHD is usually obtained from a standard model – a function of surface pressure at the GNSS station. When PWV is retrieved from the GNSS stations that are not equipped with dedicated meteorological sensors for surface pressure measurements, blind models, e.g., the global pressure and temperature (GPT) models, are commonly used to determine the pressures for these GNSS stations. Due to the limited accuracies of the GPT models, the ZHD obtained from the model-derived pressure value is also of low accuracy, especially in mid- and high-latitude regions. To address this issue, a new ZHD model, named GZHD, was investigated for real-time retrieval of GNSS-PWV in this study. The ratio of the ZHD to the zenith total delay (ZTD) was first calculated using sounding data from 505 globally distributed radiosonde stations selected from the stations that had over 5000 samples. It was found that the temporal variation in the ratio was dominated by the annual and semiannual components, and the amplitude of the annual variation was dependent upon the geographical location of the station. Based on the relationship between the ZHD and ZTD, the new model, GZHD, was developed using the back propagation artificial neural network (BP-ANN) method which took the ZTD as an input variable. The 20-year (2000–2019) radiosonde data at 558 global stations and the 9-year (2006–2014) COSMIC-1 (Constellation Observing System for Meteorology, Ionosphere, and Climate) data, which were also globally distributed, were used as the training samples of the new model. The GZHD model was evaluated using two sets of references: the integrated ZHD obtained from sounding data and ERA5 reanalysis data. The performance of the new model was also compared with GPT3, the latest version. Results showed the new model outperformed GPT3, especially in mid- and high-latitude regions. When radiosonde-derived ZHD was used as the reference, the accuracy, which was measured by the root mean square error (RMSE) of the samples, of the GZHD-derived ZHD was about 21 % better than the GTP3-derived ones. When ERA5-derived ZHD was used as the reference, the accuracy of the GZHD-derived ZHD was about 30 % better than GPT3-derived ZHD. In addition, the real-time PWV derived from 41 GNSS stations resulting from GZHD-derived ZHD was also evaluated, and the result indicated that the accuracy of the PWV was improved by 21 %. [ABSTRACT FROM AUTHOR]

The inhomogeneities, the so‐called changepoints, inevitably occur in the precipitable water vapor (PWV) time series derived from Global Navigation Satellite Systems (GNSS‐PWV). Currently, the two predominant types of homogenization methods, that is, absolute and relative methods, are limited in the poor performance and dependency on reference time series. In this study, a new absolute approach named adaptive absolute homogenization test (AAHT) by combining Seasonal‐Trend decomposition based on LOESS (STL) and Penalized Maximal F‐test to account for the red noise (PMFred) is proposed. The performance of the new approach was validated using Monte Carlo simulations. Results showed the success rate and false alarm rate were better than 74.9% and 12.2%; the detection accuracy of the changepoint epochs and the offset magnitudes were 8.0 days and 0.153 mm. AAHT was also applied to homogenizing the real GNSS‐PWV time series over 91 International GNSS Service stations, from which 63 time series were identified as inhomogeneous containing 126 changepoints. Based on a comparison with the PWV time series from the fifth‐generation European center for medium‐range weather forecasts (ECMWF) reanalysis (ERA5), 43 changepoints were connected to climatic drivers, leaving 83 artificial changepoints at 48 stations. The offset magnitudes at these artificial changepoints were estimated and corrected in the inhomogeneous time series. The long‐term trends of those homogenized time series were compared with that of the PWV time series derived from ERA5 data sets (ERA‐PWV) and the correlation coefficient between the two sets of trends was 0.75, which was significantly larger than 0.23 before homogenization. Plain Language Summary: The precipitable water vapor time series derived from Global Navigation Satellite Systems (GNSS‐PWV) can be contaminated by inhomogeneities. This study aims to propose a new reliable method to homogenize the global GNSS‐PWV time series. This method, named adaptive absolute homogenization test (AAHT), combines Seasonal‐Trend decomposition based on LOESS (STL) and Penalized Maximal F‐test to account for the red noise (PMFred) with an overall success rate and a false alarm rate better than 74.9% and 12.2%, respectively. An application on the GNSS‐PWV over 91 GNSS stations showed that homogenization using AAHT significantly improved the trend correlation between GNSS‐PWV and PWV time series retrieved from ERA5 reanalysis. Key Points: A new absolute method is proposed to homogenize a precipitable water vapor (PWV) time series derived from the Global Navigation Satellite Systems (GNSS)The success rate and false alarm rate were better than 74.4% and 12.2%; the accuracy of epochs and magnitudes were 8.3 days and 0.144 mmPWV time series at 91 global GNSS stations were homogenized, and the impact of the homogenization was also investigated [ABSTRACT FROM AUTHOR]

Global navigation satellite systems (GNSS) have been proved to be an excellent technology for retrieving precipitable water vapor (PWV). In GNSS meteorology, PWV at a station is obtained from a conversion of the zenith wet delay (ZWD) of GNSS signals received at the station using a conversion factor which is a function of weighted mean temperature (Tm) along the vertical direction in the atmosphere over the site. Thus, the accuracy of Tm directly affects the quality of the GNSS-derived PWV. Currently, the Tm value at a target height level is commonly modeled using the Tm value at a specific height and a simple linear decay function, whilst the vertical nonlinear variation in Tm is neglected. This may result in large errors in the Tm result for the target height level, as the variation trend in the vertical direction of Tm may not be linear. In this research, a new global grid-based Tm empirical model with a horizontal resolution of 1 ∘ × 1 ∘ , named GGNTm, was constructed using ECMWF ERA5 monthly mean reanalysis data over the 10-year period from 2008 to 2017. A three-order polynomial function was utilized to fit the vertical nonlinear variation in Tm at the grid points, and the temporal variation in each of the four coefficients in the Tm fitting function was also modeled with the variables of the mean, annual, and semi-annual amplitudes of the 10-year time series coefficients. The performance of the new model was evaluated using its predicted Tm values in 2018 to compare with the following two references in the same year: (1) Tm from ERA5 hourly reanalysis with the horizontal resolution of 5 ∘ × 5 ∘ ; (2) Tm from atmospheric profiles from 428 globally distributed radiosonde stations. Compared to the first reference, the mean RMSEs of the model-predicted Tm values over all global grid points at the 950 and 500 hPa pressure levels were 3.35 and 3.94 K, respectively. Compared to the second reference, the mean bias and mean RMSE of the model-predicted Tm values over the 428 radiosonde stations at the surface level were 0.34 and 3.89 K, respectively; the mean bias and mean RMSE of the model's Tm values over all pressure levels in the height range from the surface to 10 km altitude were -0.16 and 4.20 K, respectively. The new model results were also compared with that of the GTrop and GWMT_D models in which different height correction methods were also applied. Results indicated that significant improvements made by the new model were at high-altitude pressure levels; in all five height ranges, GGNTm results were generally unbiased, and their accuracy varied little with height. The improvement in PWV brought by GGNTm was also evaluated. These results suggest that considering the vertical nonlinear variation in Tm and the temporal variation in the coefficients of the Tm model can significantly improve the accuracy of model-predicted Tm for a GNSS receiver that is located anywhere below the tropopause (assumed to be 10 km), which has significance for applications requiring real-time or near real-time PWV converted from GNSS signals. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSS) have been proved to be an excellent technology for retrieving precipitable water vapor (PWV). In GNSS meteorology, PWV at a station is obtained from a conversion of the zenith wet delay (ZWD) of GNSS signals received at the station using a conversion factor which is a function of weighted mean temperature (Tm) along the vertical direction in the atmosphere over the site. Thus, the accuracy of Tm directly affects the quality of the GNSS-derived PWV. Currently, the Tm value at a target height level is commonly modelled using the Tm value at a specific height and a simple linear decay function, whilst the vertical nonlinear variation in Tm is neglected. This may result in large errors in the Tm result for the target height level, as the variation trend in the vertical direction of Tm may not be linear. In this research, a new global grid-based Tm empirical model with a horizontal resolution of 1°×1°, named GGNTm, was constructed using ECMWF ERA5 monthly mean reanalysis data over the 10-year period from 2008 to 2017. A three-order polynomial function was utilized to fit the vertical nonlinear variation in Tm at the grid points, and the temporal variation in each of the four coefficients in the Tm fitting function was also modelled with the variables of the mean, annual and semi-annual amplitudes of the 10-year time series coefficients. The performance of the new model was evaluated using its predicted Tm values in 2018 to compare with the following two references in the same year 1) Tm from ERA5 hourly reanalysis with the horizontal resolution of 5°×5°; 2) Tm from atmospheric profiles from 428 globally distributed radiosonde stations. Compared to the first reference, the mean RMSEs of the model predicted Tm values over all global grid points at the 950 hPa and 500 hPa pressure levels were 3.35 K and 3.94 K respectively. Compared to the second reference, the mean bias and mean RMSE of the model predicted Tm values over the 428 radiosonde stations at the surface level were 0.34 K and 3.89 K respectively; the mean bias and mean RMSE of the model's Tm values at all pressure levels in the height range from the surface to 10 km altitude were -0.16 K and 4.20 K respectively. The new model results were also compared with that of the GPT3, GTrop and GWMT_D models in which different height correction methods were also applied. Results indicated that significant improvements made by the new model were at high-altitude pressure levels; in all five height ranges, GGNTm results were generally unbiased, and their accuracy varied little with height. The impact of Tm on GNSS-PWV was evaluated in terms of relative error, and significant improvement was found compared to the widely used GPT3 model. These results suggest that considering the vertical nonlinear variation in Tm and the temporal variation in the coefficients of the Tm model can significantly improve the accuracy of model-predicted Tm for a GNSS receiver that is located in anywhere below the tropopause (assumed to be 10 km), which has significance for applications needing real-time or near real-time PWV converted from GNSS signals. [ABSTRACT FROM AUTHOR]

The goal of this study is to present a new model for global topside electron temperature (Te) using a deep neural network (DNN) that is trained using measurements from incoherent scatter radars (ISRs). This study is also an investigation into whether this model can be used to generate the electron temperature in the topside ionosphere using GNSS ionospheric radio occultation (GNSS‐IRO) data as the input. ISR is one of the most reliable and long‐term sources to measure topside ionospheric electron density and plasma temperature information simultaneously. However, a drawback of ISR databases is the relatively poor spatial coverage due to the low number of ISR stations around the world. In contrast, GNSS‐IRO can be used to measure the global distributed electron density, but Te information is not directly detected. The relationship between the electron density and the electron temperature has been investigated by many researchers, but these studies have not explicitly considered the parameters that are known to influence the electron temperature, such as solar and geomagnetic activity level, and the features of electron density profile (hmF2, NmF2, and scale height). This study uses a DNN technique to create a new global topside electron temperature model from three submodels that have been trained using data from three ISR stations: Arecibo (low latitude), Millstone Hill (midlatitude), and Poker Flat (high latitude). This global model is trained using electron density profile information (e.g., vertical scale height [VSH], hmF2, and NmF2) and solar and geomagnetic activity (F10.7 and Kp, respectively) in addition to traditional spatial and temporal variables (e.g., local time, month, and latitude) as the independent variables. After the Te model is developed, Te information can be generated from the GNSS‐IRO electron density profiles using a newly created Ne−Te model. This model's outputs are assessed with regard to out‐of‐sample ISR data and compared to the latest International Reference Ionosphere model. It is found that the electron temperature profiles from the DNN have a root‐mean‐square deviation of 259 K in the low‐latitude region (i.e., against Arecibo data), 254 K in the midlatitude region (i.e., against Millstone Hill data), and 314 K in high‐latitude region (i.e., against Poker Flat data), and all of them are smaller than the root‐mean‐square deviation from International Reference Ionosphere. An additional comparison between the model results versus the Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model outputs is also conducted. A statistical analysis of the diurnal electron temperature profiles obtained from GNSS‐IRO is shown to agree with the Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model outputs. The full codes and outputs can be found at the Zenodo (10.5281/zenodo.3637617). Key Points: A new global topside Te model using deep neural network from incoherent scatter radar (ISR) with newly added Ne‐related variables is proposedThe new model agrees better with the out‐of‐sample ISR measurements than IRI‐2016The Te results predicted from the proposed model and ionospheric radio occultation measurements agree well with TIEGCM in the region with sufficient IRO data [ABSTRACT FROM AUTHOR]

Vertical total electron content (VTEC) global ionospheric maps (GIM) are commonly used to correct the ionospheric delay of global navigation satellite system (GNSS) signals for single-frequency positioning and other ionospheric studies. The measurements observed by inhomogeneously distributed ground reference stations are the only data used to generate the GIMs. Thus the accuracy of the GIMs over ocean and polar regions is relatively poor due to the lack of measurements over these regions. In this study, space-borne VTECs obtained from ocean-altimetry and GNSS radio occultation measurements are incorporated into the modelling process. Since the three types of VTEC data have different qualities, the weight for each type of data is determined using the Helmert-variance component estimation (Helmert-VCE) method. In addition, unlike the traditional weighted least squares (WLS) estimation method in which the design matrix of observation equations is fixed, in this study, the design matrix, especially those elements in design matrix that are derived from the coordinates of either tangent point or ionospheric pierce point, are considered to be inaccurate. Thus they are adjusted together with the unknown coefficient parameters of the fitting model using the fast-weighted total least squares (fast-WTLS) technique. The proposed approach, named Helmert-WTLS, was tested using the data in the period of day of year (DOY) 217–224, 2016 and validated using GIMs produced by the research team for ionosphere and precise positioning based on BDS/GNSS (GIPP) at the Academy of Opto-Electronics, Chinese Academy of Sciences (CAS). Comparison results showed that the GIMs (with a 2 h temporal resolution) generated using the new approach can improve the determination of ionospheric TEC by 0.28 TEC units (TECU) over those from the Helmert-VCE-aided WLS approach (w.r.t CAS references, respectively) and by 1.61 TECU better than those from WLS, in terms of the mean of all root-mean-squares errors of all 2 h time slots in the 8-day testing period. In addition, in comparison with out-of-sample Jason-3 observations, results from the proposed method also outperformed Helmert-VCE-aided WLS, CAS and CODE models by 1.5, 2.4 and 2.4 TECU, respectively. [ABSTRACT FROM AUTHOR]

An artificial neural network (ANN) method for the modeling of global topside ionospheric vertical scale height (VSH) using electron density profiles retrieved from Global Navigation Satellite Systems radio occultation (RO) data is proposed in this study. The data for this study are 80,124 VSHs derived from the events randomly selected from 9 years of Constellation Observing System for Meteorology, Ionosphere, and Climate RO measurements and 144,530 VSHs derived from the events randomly selected from 16 years of topside sounder measurements of both Aluoette‐1/2 and ISIS‐1/2 satellites during 1962–1978 are used for comparison. VSHs from the International Reference Ionosphere are also used for the comparison. Results showed that: (1) the median of the relative residuals of the new ANN regression approach/model (which was based on RO measurements) was 8.5% less than that of the traditional approach/model (which was based on the topside sounder data); (2) the median of the relative residuals of the ANN model when longitude was used as a variable was 1.1% less than the one without longitude; and substantial error in the polar region was shown to be mitigated by taking the variable longitude into consideration; (3) compared to International Reference Ionosphere, the accuracy of the new ANN model was improved by around 14%; (4) the new ANN model outperforms the traditional base vector‐based least squares model by around 10% when incoherent scatter radar measurements are used as a reference; and (5) the characteristics of global VSHs generated from the new model during geomagnetic storms better agree with measurements than that of the base vector‐based least squares. Key Points: A new approach for vertical scale height modeling from ionospheric radio occultation data using artificial neural network is proposedThe new approach significantly outperforms the traditional model in accuracyThe new approach agrees with the characteristic of VSH in equatorial region better than traditional approach during geomagnetic storm times [ABSTRACT FROM AUTHOR]

Abstract With the rapid increase in the numbers of new generation Global Navigation Satellite Systems (GNSS) satellites, signal frequencies and ground tracking stations, the burden on data processing increases significantly, especially for those real-time or near real-time applications, e.g. generating ultra-rapid satellite orbit and Earth rotation parameters (ERP) products. In order to reduce the number of observations used to estimate the orbit and ERP unknown parameters for better computational efficiency, this study first introduced a parameter called orbit and ERP dilution of precision (OEDOP) factor and a method in "optimally" selecting multi-GNSS tracking stations based on the OEDOP factor is investigated to minimize the data processing burden without significantly sacrificing the accuracy and precision of the satellite orbit and ERP determination. The trade-off between computational efficiency and quality of results is primary focus of this research. The contribution of each tracking station to the precision of the parameter estimates is investigated first, according to the location and multi-GNSS data measurement capacity of the station as well as the length of observations, then those stations that contribute least will be identified and excluded in the estimation system. It aims to use as a fewer number of tracking stations as possible but the degradation in the precision of the solution is still under a desired level. The method was tested using GNSS observations from 409 International GNSS service (IGS) stations over a one-month period. Results showed that when the "degradation" factor of the precision of satellite orbit and ERPs solutions is 5%, 10%, 15% and 20% the accuracy of the satellites orbit and polar motion parameters estimated from an optimal minimum number of stations (in comparison with the results from all stations) reduced about 0.33–9.92 cm and 5.77–41.53 μas respectively, and the accuracy of UT1–UTC reduced 10.63–15.50 μs; while their computational speed was improved by 196%, 332%, 527% and 617% respectively. This suggests that our method is a good trade-off method and an ideal option in cases that rapid solutions are required, e.g. ultra-rapid determination of orbit and ERP using multi-GNSS measurements from global ground tracking stations. [ABSTRACT FROM AUTHOR]

Troposphere tomography, using multi-constellation observations from global navigation satellite systems (GNSSs), has become a novel approach for the three-dimensional (3-D) reconstruction of water vapour fields. An analysis of the integration of four GNSSs (BeiDou, GPS, GLONASS, and Galileo) observations is presented to investigate the impact of station density and single- and multi-constellation GNSS observations on troposphere tomography. Additionally, the optimal horizontal resolution of the research area is determined in Hong Kong considering both the number of voxels divided, and the coverage rate of discretized voxels penetrated by satellite signals. The results show that densification of the GNSS network plays a more important role than using multi-constellation GNSS observations in improving the retrieval of 3-D atmospheric water vapour profiles. The root mean square of slant wet delay (SWD) residuals derived from the single-GNSS observations decreased by 16 % when the data from the other four stations are added. Furthermore, additional experiments have been carried out to analyse the contributions of different combined GNSS data to the reconstructed results, and the comparisons show some interesting results: (1) the number of iterations used in determining the weighting matrices of different equations in tomography modelling can be decreased when considering multi-constellation GNSS observations and (2) the reconstructed quality of 3-D atmospheric water vapour using multi-constellation GNSS data can be improved by about 11 % when compared to the SWD estimated with precise point positioning, but this was not as high as expected. [ABSTRACT FROM AUTHOR]

EI Niño–Southern Oscillation (ENSO) is a complex ocean-atmosphere interaction phenomenon occurring in nature that has a profound impact on global atmospheric circulation. As ENSO is a coupled ocean-atmosphere phenomenon, in addition to the commonly used sea surface temperature (SST), water vapor in the atmosphere can be used to monitor the evolution of ENSO and to investigate its consequences (e.g., droughts and flooding). The Global Navigation Satellite System (GNSS), in addition to its applications for positioning, timing, and navigation, is another established atmospheric observing system used to remotely sense precipitable water vapor (PWV) in the atmosphere. The accuracy of the GNSS-derived PWV measurements was assessed from 12 stations based on observations made at co-located radiosonde stations as a reference. The results show that mean values of the root-mean-square error (RMSE) and biases of 6-hourly GNSS-derived PWV derived from all 12 stations are valued at 1.48 mm and −0.30 mm, respectively. Regarding monthly means, mean values of the RMSE and biases of the GNSS-derived PWV are valued at 0.66 mm and −0.23 mm, respectively. The variability in PWV estimated from 56 GNSS stations positioned close to the sea indicates that it is significantly affected by ENSO events. Generally, a 1-K increase in SST will lead to an 11% increase in PWV across all of the stations. A case study conducted at the TOW2 station in Australia shows that the non-linear trend of the PWV depicts the evolution of two severe flood events and one severe drought event occurring in this region. Comparative results derived from TOW2 and from another 24 stations show a good agreement between PWV and total precipitation. These results suggest that GNSS-derived PWV together with other climatic variables (e.g., SST) can be used as an indication of the evolution of ENSO events and as a possible indicator of drought and flood occurrence. [ABSTRACT FROM AUTHOR]

The determination of the distribution of water vapor in the atmosphere plays an important role in the atmospheric monitoring. Global Navigation Satellite Systems (GNSS) tomography can be used to construct 3-D distribution of water vapor over the field covered by a GNSS network with high temporal and spatial resolutions. In current tomographic approaches, a pre-set fixed rectangular field that roughly covers the area of the distribution of the GNSS signals on the top plane of the tomographic field is commonly used for all tomographic epochs. Due to too many unknown parameters needing to be estimated, the accuracy of the tomographic solution degrades. Another issue of these approaches is their unsuitability for GNSS networks with a low number of stations, as the shape of the field covered by the GNSS signals is, in fact, roughly that of an upsidedown cone rather than the rectangular cube as the pre-set. In this study, a new approach for determination of tomographic fields fitting the real distribution of GNSS signals on different tomographic planes at different tomographic epochs and also for discretization of the tomographic fields based on the perimeter of the tomographic boundary on the plane and meshing techniques is proposed. The new approach was tested using three stations from the Hong Kong GNSS network and validated by comparing the tomographic results against radiosonde data from King's Park Meteorological Station (HKKP) during the one month period of May 2015. Results indicated that the new approach is feasible for a three-station GNSS network tomography. This is significant due to the fact that the conventional approaches cannot even solve a network tomography from a few stations. [ABSTRACT FROM AUTHOR]

The Global Navigation Satellite System (GNSS) radio occultation (RO) technique is widely used to observe the atmosphere for applications such as numerical weather prediction and global climate monitoring. The ionosphere is a major error source to RO at upper stratospheric altitudes, and a linear dual-frequency bending angle correction is commonly used to remove the first-order ionospheric effect. However, the higher-order residual ionospheric error (RIE) can still be significant, so it needs to be further mitigated for high-accuracy applications, especially from 35 km altitude upward, where the RIE is most relevant compared to the decreasing magnitude of the atmospheric bending angle. In a previous study we quantified RIEs using an ensemble of about 700 quasi-realistic end-to-end simulated RO events, finding typical RIEs at the 0.1 to 0.5 µrad noise level, but were left with 26 exceptional events with anomalous RIEs at the 1 to 10 µrad level that remained unexplained. In this study, we focused on investigating the causes of the high RIE of these exceptional events, employing detailed alongray- path analyses of atmospheric and ionospheric refractivities, impact parameter changes, and bending angles and RIEs under asymmetric and symmetric ionospheric structures. We found that the main causes of the high RIEs are a combination of physics-based effects - where asymmetric ionospheric conditions play the primary role, more than the ionization level driven by solar activity - and technical ray tracer effects due to occasions of imperfect smoothness in ionospheric refractivity model derivatives. We also found that along-ray impact parameter variations of more than 10 to 20m are possible due to ionospheric asymmetries and, depending on prevailing horizontal refractivity gradients, are positive or negative relative to the initial impact parameter at the GNSS transmitter. Furthermore, mesospheric RIEs are found generally higher than upper-stratospheric ones, likely due to being closer in tangent point heights to the ionospheric E layer peaking near 105 km, which increases RIE vulnerability. In the future we will further improve the alongray modeling system to fully isolate technical from physicsbased effects and to use it beyond this work for additional GNSS RO signal propagation studies. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSS) water vapor tomography has been proved to be an effective method for retrieving three-dimensional (3D) water vapor distribution. Currently, due to the limitation on the availability and the poor distribution of observations, the ill-posed problem of the tomographic system still needs to be solved. In this study, a refined extended tomographic model with combining virtual signals is proposed to address the observational geometry defect and improve the performance of tomographic solutions. The new refined model was generated by adding auxiliary voxels of the same size as the original voxels around the original tomographic model and its tomographic body is with an inverted cone shape. Then, "virtual" signals that pass through the sides of the original tomographic body and the top boundary of the extended region are introduced. The slant wet delays (SWDs) of these virtual rays were obtained from the tropospheric parameters estimated from GNSS data processing and pre-defined elevation and azimuth angles. Three experimental schemes based on GNSS data from the Hong Kong reference network during the 30-day period in July 2019 were implemented to evaluate the proposed tomographic model. Statistical results showed that, compared with the tomographic models of other schemes, the new model shows strong robustness in terms of observational geometry and the accuracy of the 3D water vapor field inferred. The mean value of the root-mean-square errors (RMSEs) of the tomographic solutions during the period studied obtained from the proposed method was improved by 12% and 11% compared with Schemes I and II, when radiosonde data were used as the reference. Furthermore, compared to other schemes, the condition number of the design matrix reduced after the side-crossing virtual signals were added, implying improvements in the ill-condition of a tomographic system for the proposed method. All these results suggest the good performance of the proposed method. • An improved tomographic model is proposed to address the observational geometry defect and improve the performance. • The virtual signals penetrating from the side-faces of the tomographic model are incorporated into the observation eqs. • The new model with an inverted cone shape is built to utilize these virtual signals. [ABSTRACT FROM AUTHOR]

10.3390/rs13132644 6 Li S., Xu T., Jiang N., Yang H., Wang S., Zhang Z. Regional Zenith Tropospheric Delay Modeling Based on Least Squares Support Vector Machine Using GNSS and ERA5 Data. Several papers [[1], [3]] discuss different aspects in dealing with the estimation of long-term GNSS-derived water vapor trends and intercomparisons with external sources and NWP models. 10.3390/rs12244101 10 Li Y., Yuan Y., Wang X. Assessments of the Retrieval of Atmospheric Profiles from GNSS Radio Occultation Data in Moist Tropospheric Conditions Using Radiosonde Data. [Extracted from the article]

In this study, radiosonde observations during the period of 2012-2013 from three stations in the Hunan region, China, were used to establish regional Tm models (RTMs) that are a fitting function of multiple meteorological factors (Ts, Es, and Ps). One-factor, two-factor, and three-factor RTMs were assessed by comparing their Tm against the radiosonde-derived Tm (as the truth) during the period of 2013-2014. Statistical results showed that the bias and RMS of the one-factor RTM, in comparison to the BTM result, were reduced by 88% and 28%, respectively. The two-factor and three-factor RTMs showed similar accuracy and both outperformed the one-factor RTM, with an improvement of 7% in RMS. The bias and RMS of all the four seasonal two-factor RTMs were smaller than the yearly two-factor RTM, with the improvements of 3%, 10%, 2%, and 3% in RMS. The improvement of the conversion factors in mean bias and RMS resulting from the seasonal two-factor RTM is 92% and 31%. The bias and RMS of the PWV resulting from the seasonal two-factor RTM are improved by 37% and 12%, respectively. Therefore, the seasonal two-factor RTMs are recommended for the research and applications of GNSS meteorology in the Hunan region, China. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSS) tropospheric tomography can reflect a three-dimensional distribution of water vapour. However, the common ill-posed problem for GNSS tropospheric tomography needs to be addressed. The relatively newly launched Chinese Fengyun-4A (FY-4A) satellite water vapour data have a high spatiotemporal resolution and may be used to assist in resolving the ill-posed problem. In this study, a new tropospheric tomographic method using both GNSS and FY-4A data was proposed. Tomographic schemes for the new method with the coverage rate of GNSS sites (CRGS, which is defined as the ratio of the number of GNSS sites to the number of horizontal grids in the tropospheric tomography) of 0.2, 0.28, 0.36 and 0.44 were compared with traditional tomographic schemes using the same CRGS. Test results showed that adding FY-4A data, in addition to GNSS data, to the tomographic system could effectively increase the number of voxels crossed by signals, which benefits the ill-posed problem. When CRGS equated 0.28, the new method had the largest improvement compared with the traditional method, in which the mean of the root mean squared error (RMSE) of the water vapour density (WVD) was reduced from 2.44 and 1.38 g/m3 to 2.05 and 1.22 g/m3 using radiosonde and ERA5 data as the references, respectively. Statistical results showed that the new method that used both GNSS and FY-4A data outperformed the traditional method that only used GNSS data. • New tropospheric tomographic method using FY-4A and GNSS data is proposed. • Adding FY-4A data to the GNSS tomographic system benefits the ill-posed problem. • New method has a better performance than the traditional method in the WVD. • The fewer GNSS stations, the bigger improvement for the new method. [ABSTRACT FROM AUTHOR]

Rapid developments in satellite positioning, navigation, and timing have revolutionized surveying and mapping practice and significantly influenced the way people live and society operates. The advent of new generation global navigation satellite systems (GNSS) has heralded an exciting future for not only the GNSS community, but also many other areas that are critical to our society at large. With the rapid advances in space-based technologies and new dedicated space missions, the availability of large scale and dense contemporary GNSS networks such as regional continuously operating reference station (CORS) networks and the developments of new algorithms and methodologies, the ability of using space geodetic techniques to remotely sense the atmosphere (i.e., the troposphere and ionosphere) has dramatically improved. Real time GNSS-derived atmospheric variables with a high spatio-temporal resolution have become an important new source of measurements for meteorology, particularly for extreme weather events since water vapour (WV), as the most abundant element of greenhouse gas and accounting for $\sim $70% of global warming, is under-sampled in current meteorological and climate observing systems. This study investigates the emerging area of GNSS technology for near real-time monitoring and forecasting of severe weather and climate change research. This includes both ground-based global positioning system (GPS)-derived precipitable water vapour (PWV) estimation and four-dimensional (4-D) tomographic modeling for wet refractivity fields. Two severe weather case studies were used to investigate the signature of GPS-derived PWV and wet refractivity derived from the 4-D GPS tomographic model under the influence of severe mesoscale convective systems (MCSs). GPS observations from the Victorian state-wide CORS network, i.e., GPSnet, in Australia were used. Results showed strong spatial and temporal correlations between the variations in the ground-based GPS-derived PWV and the passage of the severe MCS. This indicates that the GPS method can complement conventional meteorological observations for the studying, monitoring, and potentially predicting of severe weather events. The advantage of using the ground-based GPS technique is that it can provide continuous observations for the storm passage with high temporal and spatial resolution. Results from these two case studies also suggest that GPS-derived PWV can resolve the synoptic signature of the dynamics and offer precursors to severe weather, and the tomographic technique has the potential to depict the three-dimensional (3-D) signature of wet refractivity for the convective and stratiform processes evident in MCS events. This research reveals the potential of using GNSS-derived PWV to strengthen numerical weather prediction (NWP) models and forecasts, and the potential for GNSS-derived PWV and wet refractivity fields to enhance early detection and sensing of severe weather. [ABSTRACT FROM PUBLISHER]

The Global Positioning System (GPS) is nowadays widely used for navigation of spacecraft in low Earth orbit (LEO). Both real-time positioning information (navigation solutions) and raw GPS measurements (pseudo-range, carrier-phase, Doppler shift and signal-to-noise ratio) are usually transmitted to ground control centres for post-processing. However, for certain LEO missions, only navigation solutions are available due to the limited capability of space-borne GPS receivers. In this case, navigation solutions are commonly used as pseudo-observations in dynamic orbit determination (referred to as dynamic filtering) to obtain a smooth and continuous orbit; however, the achievable orbit accuracy is limited since broadcast ephemeris data from the GPS navigation messages are used and the more accurate GPS orbit and clock information are only available in post-processing. In this study, a method of reconstructing space-borne pseudo-range measurements from real-time positioning information is developed, thus, the more accurate GPS orbit and clock products from the International GNSS Service (IGS) are able to be used in dynamic orbit determination. Real GPS data from the TerraSAR-X mission are used to validate this approach and its performance in dynamic orbit determination is assessed as well. Results show that the orbit accuracy of navigation solutions can be significantly improved from 15 m to 5 m after a dynamic filtering process is performed. A remarkably better orbit accuracy of about 0.5 m is achieved in dynamic orbit determination using the reconstructed pseudo-range measurements and IGS rapid orbit and clock products; both the requirements of 24-h timeframe and an orbit accuracy of 2 m for generating proper SAR images are well satisfied. [ABSTRACT FROM PUBLISHER]

Based on MSIS90 atmospheric and 3D NeUoG ionospheric models, three types of ionospheric disturbances, i. e. ionospheric storm, traveling ionospheric disturbance (TID) and ionospheric trough were simulated, and their effects on bending angle residual ionospheric errors (RIEs) and temperature accuracy were analyzed. The results show that under high solar activity level, the bending angle RIE and its standard deviation can reach 1.5 μrad and 0.9 μrad, in the impact height range of 35~50 km; 4 μrad and 1.4 μrad, respectively, in the impact height range of 15~35 km. The ionopsheric storm and trough can enlarge the bending angle RIE standard deviations by more than 20 times, and TID can enlarge the bending angle RIE standard deviations by several times. Under the influence of ionospheric disturbances, temperature errors can reach 8 K and can pollute the GPS RO daily-mean and month-mean observations. Hence, it is desirable to develop ionospheric correction algorithms; the temperature outliers caused by ionospheric disturbances should be removed according to ionospheric monitoring results. [ABSTRACT FROM AUTHOR]

A fundamental principle of local interaction of radio waves with a refractive spherical medium is formulated and illustrated using the radio occultation (RO) method of remote sensing of the atmosphere and the ionosphere of the Earth and the planets. In accordance with this principle, the main contribution to variations of the amplitude and the phase of radio waves propagating through a medium makes a neighborhood of a tangential point, where the gradient of the refractive index is perpendicular to the radio wave trajectory. A necessary and sufficient condition (a criterion) is established to detect the displacement of the tangential point from the radio ray perigee using analysis of the RO experimental data. This criterion is applied to the identification and the location of layers in the atmosphere and the ionosphere by the use of Global Positioning System RO data. RO data from the CHAllenge Minisatellite Payload (CHAMP) are used to validate the criterion introduced when significant variations of the amplitude and the phase of the RO signals are observed at the RO ray perigee altitudes below 80 km. The detected criterion opens a new avenue in terms of measuring the altitude and the slope of the atmospheric and ionospheric layers. This is important for the location determination of the wind shear and the direction of internal wave propagation in the lower ionosphere and possibly in the atmosphere. The new criterion provides an improved estimation of the altitude and the location of the ionospheric plasma layers compared with the backpropagation radio-holographic method previously used. [ABSTRACT FROM AUTHOR]

The number of global positioning system (GPS) tracking stations is increasing, primarily because the stations are multifunctional. In civil engineering, they can be used for precision positioning; in the earth sciences, they can be used to monitor faults and earthquakes; and in the atmospheric sciences, they can be applied to predict perceptible water vapor. Currently, there are more than 400 GPS stations in Taiwan; however, the data obtained through such stations are not being assessed carefully. Experienced scientists and engineers examine the data in advance to see if they qualify for research purposes, but inexperienced users can adopt poor quality data that eventually lead to inaccurate research results. Of the observation stations with receivers that were renewed between 2006 and 2008 in the International GNSS Service Network, four stations (ZIMM, BOR1, NRC1, and NICO) were selected to be the subjects of this research. Six indexes of data quality were observed to calculate the quality of data obtained before and after receiver renewal. Then, analyses were conducted to understand the relationship between the quality indexes and positioning precision. The results showed that after receiver renewal, the positioning precision of the four stations was improved by 1-19%. Therefore, positioning precision is positively affected by the six data quality indexes. It was also discovered that receiver clock error was the most critical factor among the six indexes. In conclusion, if data quality control can be applied to GPS tracking stations, the data obtained will be more reliable for research purposes, and the accuracy of subsequent engineering and science measurements will be improved. [ABSTRACT FROM AUTHOR]

The ionosphere and atmosphere may have significant impacts on the high-stable navigational signals of the Global Positioning System (GPS) in the communication link satellite to satellite. The classification of the different types of the ionospheric impact on the phase and amplitude of the GPS signals at altitudes of 40–90 km is introduced using the CHAllenging Minisatellite Payload (CHAMP) radio occultation (RO) data. An analytical model is elaborated for the description of the radio wave propagation in the stratified ionosphere and atmosphere. The propagation medium consists of sectors having the spherically symmetric distributions of refractivity. The newly developed model presents analytical expressions for the phase path and refractive attenuation of radio waves. The model explains significant amplitude and phase variations at altitudes of 40–90 km of the RO ray perigee associated with the influence of the inclined ionospheric layers. An innovative eikonal acceleration technique is described and applied to the identification and location of the inclined ionospheric layers using the comparative analysis of the amplitude and phase variations of the RO signals. [ABSTRACT FROM PUBLISHER]

A method, which can invert the ionospheric electron content above the CHAMP satellite, is presented. The daytime behavior of the equatorial ionospheric anomaly (EIA) is monitored using the CHAMP global positioning system data and the ionosphere map exchange format data supplied by the Center for Orbit Determination in Europe during the period at quiet geomagnetic conditions between November 7 and December 12, 2003. Results show that the plasma congregates and rises above the geomagnetic equator due to the daytime upward vertical E \times B drift during the early variations of EIA. After being lifted to higher altitudes, the topside ionospheric equatorial plasma above the CHAMP orbit (over 400 km above the ground) controlled mainly by the electromagnetic field starts to diffuse downward to higher latitudes along magnetic field lines, and the double-humped structure begins to appear in the topside ionosphere above the CHAMP orbit. However, a single crest is still observed for the entire ionosphere above the ground, and this single peak moves slightly to the winter hemisphere as a result of the transequatorial neutral wind. With the continuous diffusion of the plasma, the two crests also occur for the entire ionosphere at both sides of the geomagnetic equator. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System (GNSS) is a critical space-borne geospatial infrastructure providing essential positioning supports to a range of location-sensitive applications. GNSS is currently dominated by the US Global Positioning System (GPS) constellation. The next generation GNSS is expected to offer more satellites, better positioning provision, and improved availability and continuity of navigation support. However, GNSS performance in 3-D urban environments is problematic because GNSS signals are either completely blocked or severely degraded by high-rising geographic features like buildings. The aim of this study is to gain an in-depth understanding of the changing spatial patterns of GNSS performance, measured by the number of visible satellites (NVS) and position dilution-of-precision (PDOP), in the urban canyons of Melbourne, Australia. The methodology used includes the following steps: (1) determination of the dynamic orbital positions of current and future GNSS satellites; (2) development of a 3-D urban model of high geometric quality for Melbourne Central Business District (CBD); (3) evaluation of GNSS performance for every specified location in the urban canyons; and (4) visualisation and characterisation of the dynamic spatial patterns of GNSS performances in the urban canyons. As expected, the study shows that the integration of the GPS and Galileo constellations results in higher availability and stronger geometry, leading to significant improvement of GNSS performance in urban canyons of Melbourne CBD. Some conclusions are drawn and further research currently undertaken is also outlined. [ABSTRACT FROM AUTHOR]

This study investigated the relationship between variations in the atmospheric boundary layer height (ABLH) and typhoons over the Northwest Pacific using global navigation satellite system (GNSS) radio occultation (RO) data during the local summer typhoon season (July–October in the Northern Hemisphere) from 2007 to 2020. The minimum gradient of refractivity derived from COSMIC and COSMIC-2 was used to determine the ABLH. The RO profiles were co-located with the position of a typhoon track base within a 600 km space window and different time windows. ABLH climatology with a 2.5° × 2.5° horizontal resolution was developed, which can be used to obtain the interpolated mean ABLH at any target position. The mean ABLH at the central typhoon position in a specific year was compared with the results interpolated from the climatology of the same location (excluding the year in which the investigated typhoon occurred). In this paper, the results indicate that the ABLH is lower in the vicinity of typhoons relative to the undisturbed atmosphere by a significant amount, and that the reduction in ABLH ranges from 0.13 km to 0.39 km. It was also found that the ABLH was negatively correlated with wind speed, and that the mean correlation coefficient was −0.607. Moreover, similar results can be obtained via the RO water vapor partial pressure profile compared to the refractivity results. [ABSTRACT FROM AUTHOR]

Real-time precise point positioning (RT-PPP) has become a powerful technique for the determination of the zenith tropospheric delay (ZTD) over a GPS (global positioning system) or GNSS (global navigation satellite systems) station of interest, and the follow-on high-precision retrieval of precipitable water vapor (PWV). The a priori zenith hydrostatic delay (ZHD) and the mapping function used in the PPP approach are the two factors that could affect the accuracy of the PPP-based ZTD significantly. If the in situ atmospheric pressure is available, the Saastamoinen model can be used to determine ZHD values, and the model-predicted ZHD results are of high accuracy. However, not all GPS/GNSS are equipped with an in situ meteorological sensor. In this research, the daily forecasting ZHD and mapping function values from VMF1 forecasting (VMF1_FC) and VMF3 forecasting (VMF3_FC) products were used for the determination of the GPS-derived PWV. The a priori ZHDs derived from VMF1_FC and VMF3_FC were first evaluated by comparing against the reference ZHDs from globally distributed radiosonde stations. GPS observations from 41 IGS stations that have co-located radiosonde stations during the period of the first half of 2020 were used to test the quality of GPS-ZTD and GPS-PWV. Three sets of ZTDs estimated from RT-PPP solutions using the a priori ZHD and mapping function from the following three VMF products were evaluated: (1) VMF1_FC; (2) VMF3_FC (resolution 5° × 5°); (3) VMF3_FC (resolution 1° × 1°). The results showed that, when the ZHDs from 443 globally distributed radiosonde stations from 1 July 2018 to 30 June 2021 were used as the reference, the mean RMSEs of the ZHDs from the three VMF products were 5.9, 5.4, and 4.3 mm, respectively. The ZTDs estimated from RT-PPP at 41 selected IGS stations were compared with those from IGS, and the results showed that the mean RMSEs of the ZTDs of the 41 stations from the three PPP solutions were 8.6, 9.0, and 8.6 mm, respectively, and the mean RMSEs of the PWV converted from their corresponding ZWDs were 1.9, 2.4, and 1.7 mm, respectively, in comparison with the reference PWV from co-located radiosonde stations. The results suggest that the a priori ZHD and mapping function from VMF1_FC and VMF3_FC can be used for the precise determination of real-time GPS/GNSS-PWV in most regions, especially the VMF3_FC (resolution 1° × 1°) product. [ABSTRACT FROM AUTHOR]

Since the full system deployment of BDS-3 in July 2020, it has provided stable and reliable positioning, navigation and timing (PNT) services for users around the world. It is an urgent task to explore the potential performance of precise point positioning (PPP) based on the full constellation of BDS-2/BDS-3. This research presented the global satellite visibility of BDS-2/BDS-3, analyzed the stability and accuracy of BDS-2 and BDS-3 rubidium clocks and hydrogen clocks, jointly estimated the BDS-2 and BDS-3 fractional cycle biases (FCB) and evaluated the PPP performance with ambiguity resolution (AR). Due to the fact that International GNSS Service (IGS) precise products have a great impact on narrow-lane (NL) FCB estimation, the FCB obtained by using the precise products from CODE (Center for Orbit Determination in Europe) and GBM (Deutsches GeoForschungsZentrum) in two different analysis centers and the FCB obtained by using GBM precise products with different calculation strategies are analyzed. In addition, the experimental data from Multi-GNSS Experiment (MGEX), EUREF (EPN) and Germany (GPN) observation networks are used to evaluate the influence of orbital errors on BDS FCB estimation. For wide-lane (WL) FCB, MGEX WL FCB has the highest accuracy, followed by EPN and GPN. On the contrary, for NL FCB, MGEX NL FCB has the worst accuracy, followed by EPN, and GPN is the best. Eventually, numerous experiment results showed that compared with the static float solution, the PPP fixed solutions in East (E), North (N) and Up (U) directions are improved by 32.9%, 27.7%, 13.8% for BDS-3 and 10.0%, 7.5%, 3.8% for BDS-2/BDS-3. Compared with the kinematic float solution, the PPP fixed solutions in E, N and U directions are improved by 27.9%, 23.4%, 16.6% for BDS-3 and 15.9%, 8.9%, 5.3% for BDS-2/BDS-3. [ABSTRACT FROM AUTHOR]

Currently, the 25th solar activity peak has commenced, driving frequent geomagnetic storms and causing irregular disturbances in the global ionosphere, leading to the scintillation of Global Positioning System (GPS) signals, consequently decreasing the accuracy of GPS positioning. Analyzing the patterns of global ionospheric scintillation and changes in GPS positioning accuracy caused by geomagnetic storms is crucial to mitigate the adverse effects of GPS positioning. Current research has primarily concentrated on analyzing the effects of geomagnetic storms on ionospheric scintillation disturbances and GPS positioning interference. However, there is a notable gap in studying the holistic impact of coronal mass ejections (CMEs)-driven geomagnetic storms on GPS performance as an integrated event complex. This study investigates the conditions under which a CME drives a severe geomagnetic storm to elucidate the space weather phenomena associated with CME-driven geomagnetic storms. The ionospheric scintillation induced by the geomagnetic storm is analyzed, while we also examine the accuracy variability in kinematic precision point positioning (PPP) and its possible leading reasons. The research findings suggest that factors such as a high-speed full-halo CME, a southward interplanetary magnetic field (IMF) Bz, and high-speed solar wind contribute to the onset of this geomagnetic storm. Ionospheric scintillation and the decrease in positioning accuracy in low-latitude regions are less pronounced compared to mid- and high-latitude areas. Geomagnetic-storm-induced scintillation increases cycle slips, leading to a decrease in PPP accuracy. Even in the cases where geomagnetic storms do not induce ionospheric scintillation, positioning accuracy can still be affected by cycle slips, deterioration of the precision of the GPS measurements, data outages, and the decrease in the number of available satellites. [ABSTRACT FROM AUTHOR]

Water vapor is one of the most important parameters in climatic studies. MODerate-resolution Imaging Spectroradiometer (MODIS) is a key instrument and can provide spatially continuous precipitable water vapor (PWV) products. This study was focused on the performance evaluation of the MODIS near-infrared PWV product (MOD-NIR-PWV) over China. For a comprehensive assessment of the performance of MOD-NIR-PWV, PWV retrieved from the measurements at the global navigation satellite systems (GNSS) stations (i.e., GNSS-PWV) and the ERA5 reanalysis dataset (ERA-PWV) from 2013 to 2018 were used as the reference. To investigate the suitability of using ERA-PWV as the reference for the evaluation, ERA-PWV was compared to the high-accuracy GNSS-PWV at 246 GNSS stations and PWV retrieved from radiosonde observations (RS-PWV) at 78 radiosonde stations over China. The results showed that the mean bias and mean root-mean-square (RMS) of the differences between ERA-PWV and GNSS-PWV across all the stations were 0.5 and 1.7 mm, respectively, and the mean correlation coefficient of the two datasets was above 0.96. The values were 0.4 and 1.9 mm and 0.97, respectively, for the differences between ERA-PWV and RS-PWV. This suggests the suitability of ERA-PWV as the reference for the evaluation of MOD-NIR-PWV. In addition, MOD-NIR-PWV was compared with both GNSS-PWV and ERA-PWV, and their mean bias and mean RMS were 2.9 and 3.8 mm (compared to GNSS-PWV) and 2.1 and 3.0 mm (compared to ERA-PWV), respectively. The positive bias values and the non-normal distribution of the differences between MOD-NIR-PWV and both reference datasets imply that a considerable systematic overestimation of MOD-NIR-PWV over China may exist. To mitigate the systematic bias, ERA-PWV was utilized as the sample data due to its spatial continuities, and a grid-based calibration model was developed based on the annual and semiannual periodicities in the differences between MOD-NIR-PWV and ERA-PWV at each grid point. After applying the calibration model to correct MOD-NIR-PWV, the calibrated MOD-NIR-PWV was compared with ERA-PWV and GNSS-PWV for precision and accuracy analysis, respectively. The comparison showed that the model could significantly improve the precision by 94% and accuracy by 53%, which manifested the effectiveness of the calibration model in improving the performance of MOD-NIR-PWV over China. [ABSTRACT FROM AUTHOR]

Mapping the ionosphere over low-latitude regions with a high accuracy is a challenging task due to the complex nature of the ionospheric structure. In this study, the artificial neural network (ANN) technique was used to investigate the determination of the total electron content (TEC) in low latitude regions using measurements from 187 ground-based International global navigation satellite system (GNSS) Service (IGS) stations and the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-2) mission in the period from 1 October 2019 to 30 September 2021. To reduce the systematic discrepancies between the space-borne and ground-based observational systems, the ANN technique was also utilized to calibrate the COSMIC-2 derived TECs to the ground level. The RMSE of the calibrated COSMIC-2 TECs was 1.78 TECU. This, in comparison with the RMSE value of 4.10 TECU for the uncalibrated COSMIC-2 TECs, demonstrated 57% improvement. It is found that the performance of the new model was strongly associated with the value of TEC and low-latitude ionospheric structure, such as the equatorial ionization anomaly (EIA). Compared with the IRI-2016 model, the Technical University of Catalonia (UPC) rapid global ionospheric maps (GIMs) (UQRG), the Center for Orbit Determination in Europe (CODE) final GIMs (CODG), and the Chinese Academy of Sciences (CAS) final GIMs (CASG), the new model outperformed the IRI-2016 model and was competitive with the GIMs. All results suggest that the new model developed using both ground-based and space-borne measurements together with the ANN technique can model the ionospheric structure, such as the EIA. Therefore, the new model developed may become a good reference for the studies of the structure and variation of the ionosphere over low latitudes. [ABSTRACT FROM AUTHOR]

Nowadays, precipitable water vapor (PWV) retrieved from ground-based Global Navigation Satellite Systems (GNSS) tracking stations has heralded a new era of GNSS meteorological applications, especially for severe weather prediction. Among the existing models that use PWV timeseries to predict heavy precipitation, the "threshold-based" models, which are based on a set of predefined thresholds for the predictors used in the model for predictions, are effective in heavy precipitation nowcasting. In previous studies, monthly thresholds have been widely accepted due to the monthly patterns of different predictors being fully considered. However, the primary weakness of this type of thresholds lies in their poor prediction results in the transitional periods between two consecutive months. Therefore, in this study, a new method for the determination of an optimal set of diurnal thresholds by adopting a 31-day sliding window was first proposed. Both the monthly and diurnal variation characteristics of the predictors were taken into consideration in the new method. Then, on the strength of the new method, an improved PWV-based model for heavy precipitation prediction was developed using the optimal set of diurnal thresholds determined based on the hourly PWV and precipitation records for the summer over the period 2010–2017 at the co-located HKSC–KP (King's Park) stations in Hong Kong. The new model was evaluated by comparing its prediction results against the hourly precipitation records for the summer in 2018 and 2019. It is shown that 96.9% of heavy precipitation events were correctly predicted with a lead time of 4.86 h, and the false alarms resulting from the new model were reduced to 25.3%. These results suggest that the inclusion of the diurnal thresholds can significantly improve the prediction performance of the model. [ABSTRACT FROM AUTHOR]

Nowadays, the Global Navigation Satellite Systems (GNSS) have become an effective atmospheric observing technique to remotely sense precipitable water vapor (PWV) mainly due to their high spatiotemporal resolutions. In this study, from an investigation for the relationship between GNSS-derived PWV (GNSS-PWV) and heavy precipitation, it was found that from several hours before heavy precipitation, PWV was probably to start with a noticeable increase followed by a steep drop. Based on this finding, a new model including five predictors for heavy precipitation prediction is proposed. Compared with the existing 3-factor model that uses three predictors derived from the ascending trend of PWV time series (i.e., PWV value, PWV increment and rate of the PWV increment), the new model also includes two new predictors derived from the descending trend: PWV decrement and rate of PWV decrement. The use of the two new predictors for reducing the number of misdiagnosis predictions is proposed for the first time. The optimal set of monthly thresholds for the new five-predictor model in each summer month were determined based on hourly GNSS-PWV time series and precipitation records at three co-located GNSS/weather stations during the 8-year period 2010–2017 in the Hong Kong region. The new model was tested using hourly GNSS-PWV and precipitation records obtained at the above three co-located stations during the summer months in 2018 and 2019. Results showed that 189 of the 198 heavy precipitation events were correctly predicted with a lead time of 5.15 h, and the probability of detection reached 95.5%. Compared with the 3-factor method, the new model reduced the FAR score by 32.9%. The improvements made by the new model have great significance for early detection and predictions of heavy precipitation in near real-time. [ABSTRACT FROM AUTHOR]