Global Navigation Satellite System Meteorology Research Articles

Enhancing Atmospheric Monitoring Capabilities: A Comparison of Low- and High-Cost GNSS Networks for Tropospheric Estimations

Global Navigation Satellite System (GNSS) signals experience delays when passing through the atmosphere due to the presence of free electrons in the ionosphere and air density in the non-ionized part of the atmosphere, known as the troposphere. The Precise Point Positioning (PPP) technique demonstrates highly accurate positioning along with Zenith Tropospheric Delay (ZTD) estimation. ZTD estimation is valuable for various applications including climate modelling and determining atmospheric water vapor. Current GNSS network resolutions are not completely sufficient for the scale of a few kilometres that regional climate and weather models are increasingly adopting. The Centipede-RTK network is a low-cost option for increasing the spatial resolution of tropospheric monitoring. This study is motivated by the question of whether low-cost GNSS networks can provide a viable alternative without compromising data quality or precision. This study compares the performance of the low-cost Centipede-RTK network in calculating the Zenith Tropospheric Delay (ZTD) to that of the existing EUREF Permanent Network (EPN), using two alternative software packages, RTKLIB demo5 version and CSRS-PPP version 3, to ensure robustness and software independence in the findings. This investigation indicated that the ZTD estimations from both networks are almost identical when processed by the CSRS-PPP software, with the highest mean difference being less than 3.5 cm, confirming that networks such as Centipede-RTK could be a reliable option for dense precise atmospheric monitoring. Furthermore, this study revealed that the Centipede-RTK network, when processed using CSRS-PPP, provides ZTD estimations that are very similar and consistent with the EUREF ZTD product values. These findings suggest that low-cost GNSS networks like Centipede-RTK are viable for enhancing network density, thus improving the spatial resolution of tropospheric monitoring and potentially enriching climate modelling and weather prediction capabilities, paving the way for broader application and research in GNSS meteorology.

Comparing Discrete and Empirical Troposphere Delay Models: A Global IGS‐Based Evaluation

AbstractZenith tropospheric delay (ZTD) is an important atmospheric parameter in radio‐space‐geodetic techniques such as Global Navigation Satellite System (GNSS), which is pivotal for GNSS positioning, navigation and meteorology. The Vienna Mapping Function (VMF) data server is a widely utilized source for implementing ZTD, offering two types of models, that is, the empirical one and the discrete one with Grid‐wise and Site‐wise models. Therefore, to evaluate the accuracy of these models becomes the focus of this article. Specifically, this study investigates their performances in terms of calculation of ZTD, using the hourly values derived from the International GNSS Service data as references. The results show that the root mean square err (RMSE) of the Site‐wise, Grid‐wise and global pressure and temperature 3 model are 11.71/13.03/38.56 mm, respectively, indicating the discrete model performs generally better than the empirical model, and the Site‐wise model is the better of the two discrete models. From the perspective of spatial resolution, the performance of these three models in ZTD calculation shows obvious influences of latitude changes and elevation differences. From the temporal analysis, the accuracy of the discrete model shows differences over different UTC epochs, while the empirical model can only express the seasonal ZTD characteristics with the average RMSE at different epochs being similar, the specifically values are 39.67, 39.26, 39.38 and 39.18 mm at UTC 0:00, 6:00, 12:00 and 18:00, respectively. The histogram and boxplot well indicate the accuracy differences of the three models in different seasons. Additionally, the time series of three models at different latitudes were also explored in this research. These explorations are conducive to the selection of appropriate models for calculating ZTD based on specific requirements.

A global grid model for the estimation of zenith tropospheric delay considering the variations at different altitudes

Abstract. The accuracy of tropospheric delay correction heavily depends on the quality of the tropospheric model, and the zenith tropospheric delay (ZTD) is an important factor affecting the tropospheric delay. Therefore, it is essential to establish a precise ZTD empirical model. The existing ZTD models are constrained by a single fitting function, lack consideration for daily cycle variations, and rely solely on data with one resolution for modeling. To address these limitations, we proposed a global piecewise ZTD empirical grid (GGZTD-P) model. This model considers the daily cycle variation and latitude factor of ZTD, using the sliding window algorithm based on fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis data (ERA5). The ZTD data from 545 radiosonde stations and the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) atmospheric reanalysis data are used to validate the accuracy of the GGZTD-P model. The results indicate that the GGZTD-P model outperforms the global pressure and temperature 3 (GPT3) model, exhibiting 26 % and 53 % lower bias and rms, respectively, when using radiosonde stations as reference values. Furthermore, when evaluated using MERRA-2 atmospheric reanalysis data, the GGZTD-P model consistently exhibits superior performance across various latitude regions. It is expected that the application of this new model will provide improved services for high-precision global navigation satellite system (GNSS) positioning and GNSS meteorology.

A Precise Zenith Hydrostatic Delay Calibration Model in China Based on the Nonlinear Least Square Method

Abstract Zenith hydrostatic delay (ZHD) is a crucial parameter in Global Navigation Satellite System (GNSS) navigation and positioning and GNSS meteorology. Since the Saastamoinen ZHD model has a larger error in China, it is significant to improve the Saastamoinen ZHD model. This work first estimated the Saastamoinen model using the integrated ZHD as reference values obtained from radiosonde data collected at 73 stations in China from 2012 to 2016. Then, the residuals between the reference values and the Saastamoinen modeled ZHDs were calculated, and the correlations between the residuals and meteorological parameters were explored. The continuous wavelet transform method was used to recognize the annual and semiannual characteristics of the residuals. Because of the nonlinear variation characteristics of residuals, the nonlinear least squares estimation method was introduced to establish an improved ZHD model—China Revised Zenith Hydrostatic Delay (CRZHD)—adapted for China. The accuracy of the CRZHD model was assessed using radiosonde data and International GNSS Service (IGS) data in 2017; the radiosonde data results show that the CRZHD model is superior to the Saastamoinen model with a 69.6% improvement. The three IGS stations with continuous meteorological data present that the BIAS and RMSE are decreased by 2.7 and 1.5 (URUM), 5.9 and 5.3 (BJFS), and 9.6 and 8.8 mm (TCMS), respectively. The performance of the CRZHD model retrieving PWV was discussed using radiosonde data in 2017. It is shown that the CRZHD model retrieving PWV (CRZHD-PWV) outperforms the Saastamoinen model (SAAS-PWV), in which the precision is improved by 44.4%. The BIAS ranged from −1 to 1 mm and RMSE ranged from 0 to 2 mm in CRZHD-PWV account for 89.0% and 95.9%, while SAAS-PWV account for 46.6% and 58.9%. Significance Statement Zenith hydrostatic delay (ZHD) is one of the most important parameters in Global Navigation Satellite System (GNSS) navigation and positioning and GNSS meteorology, which can be derived from a precise ZHD model due to its stability. This research established an improved ZHD model for China to obtain accurate ZHD, which is a prerequisite for pinpoint precipitable water vapor (PWV) retrieval. And the PWV value is beneficial to analyze the change in precipitation in some regions, forecast the short-term rainfall, and monitor the climate.

Retrieving Accurate Precipitable Water Vapor Based on GNSS Multi‐Antenna PPP With an Ocean‐Based Dynamic Experiment

AbstractAs an attractive technique for measuring water vapor, the Global Navigation Satellite System (GNSS) faces additional challenges in dynamic applications such as in the open sea. We present a new method of retrieving precipitable water vapor (PWV) based on GNSS multi‐antenna precise point positioning (PPP), which uses GNSS data from multiple antennas and incorporates the constraints of known baseline vector and common tropospheric delay. The 4‐day shipborne dynamic experiment along the China coast demonstrates that the baseline vector constraint shortens the convergence time of positioning and atmospheric parameters, and also slightly improves their accuracies. The common tropospheric delay constraint helps to provide compromised, more robust, and sometimes more accurate PWV estimates. An evaluation with radiosonde‐derived PWVs shows that the combination of the two constraints achieves the best accuracy reaching 4.2 mm. This method helps to expand GNSS meteorology to the vast ocean and benefits satellite altimetry and weather forecasting.

Evaluation of the weighted mean temperature over China using multiple reanalysis data and radiosonde

Global Navigation Satellite Systems (GNSS) meteorology has received extensive attention for weather research in recent years. As a crucial conversion parameter, weighted mean temperature (Tm) is used to calculate precipitable water vapor (PWV) by the signal path wet delay in GNSS meteorology. Comparing with Tm calculated by radiosonde, Tm from reanalysis data is more convenient due to higher spatiotemporal resolution, such as the second Modern-Era Retrospective Analysis for Research and Applications (MERRA-2), the fifth-generation ECMWF Reanalysis (ERA5), Japanese 55-year Reanalysis Project (JRA-55), the global atmospheric reanalysis of China Meteorological Administration (CRA40). To investigate their different performances, a comprehensive assessment of multiple Tm datasets from 2011 to 2019 was conducted using the radiosonde data over China as references, in which the root mean square error (RMSE) and the mean absolute error (MAE) were selected as statistics. The results showed that the Tm derived from multiple reanalysis data exhibited excellent applicability in China, especially in the southeast region. The RMSE and MAE were 1.69/1.36, 1.70/1.36, 1.79/1.45, 1.99/1.61 K for ERA5, JRA-55, CRA40, MERRA-2, respectively. By comparing Tm from ERA5 with three different resolutions, it's found that the accuracy of Tm was getting better with the increase of the spatial resolution. Additionally, the accuracy of these Tm was of great relevance to season, moment, latitude, and elevation. By exploring the intercomparison of Tm from reanalysis data, their correlation coefficients were all above 0.98. This study indicated that the Tm derived from various reanalysis data can be utilized for climate research in China, and the ERA5 data with high resolution is recommended first.

GGTm-Ts: A global grid model of weighted mean temperature (Tm) based on surface temperature (Ts) with two modes

With the development of Global Navigation Satellite System (GNSS), the detection of precipitable water vapor (PWV) using the GNSS atmospheric sounding technique becomes a research interest in GNSS meteorology. In the conversion of zenith tropospheric delay (ZTD) to PWV, the weighted mean temperature (Tm) plays a crucial role. Generally, the Tm estimated by the linear regression models based on surface temperature (Ts) cannot meet the requirement for global use, and the accuracy of Tm derived from the empirical models is limited. In this study, a new Tm model, named GGTm-Ts model, was developed using the global geodetic observing system (GGOS) atmosphere Tm data and European Centre for Medium-Range Weather Forecasts (ECMWF) data from 2011 to 2015. Resting upon a global 2.5°*2° grid of coefficients of Tm-Ts linear function, the new model can provide Tm at any site in two modes, one for the case with measured Ts provided, i.e., the accurate mode, the other for the case that Ts provided by a subroutine, i.e., the normal mode. The performance of GGTm-Ts model was assessed against the Bevis formula, GPT2w and GPT2wh model using different data sources in 2016-the GGOS atmosphere and radiosonde data. The results show that the GGTm-Ts model in accurate mode achieves best performance with an improvement of 46.9 %/15.3 %, 37.8 %/19.5 % and 34.4 %/14.2 % over other three models in the GGOS atmosphere/radiosonde comparison. For the normal mode, the GGTm-Ts model outperforms the GPT2w model and achieves equivalence results with the GPT2wh model. Moreover, the impact of Tm on GNSS-PWV was analyzed to validate the performance of the GGTm-Ts model.

Analysis of Microtopography Atmospheric Precipitable Water Vapour over the Northeastern Margin of the Qinghai–Tibet Plateau

The Liupan Mountain (LPM) area is located on the northeast margin of the Qinghai–Tibet Plateau, which is the western part of the second ladder of China’s terrain. It is also an intersection area of two air currents, which are caused by the combined action of the Qinghai–Tibet Plateau, the middle and lower levels of the westerly belt and the edge of the monsoon area. LPM is one of the main air water vapour transport pathways in Northwest China as well as a main water conservation area for nearly ten million people. Research on atmospheric precipitable water vapour (PWV) variation characteristics in LPM is beneficial for understanding the mechanisms of orographic precipitation and improving the effects of weather modification. Based on the data from 10 Global Navigation Satellite System Meteorology (GNSS/MET) stations for 6 years and the data of automatic weather stations in the LPM, the temporal and spatial variation characteristics of PWV in the LPM were analysed, and the differences in PWV in the 24 h before and after precipitation were compared in this study. The results showed that the hourly, monthly and seasonal variations in PWV displayed obvious patterns. PWV increased rapidly 10 h ahead of precipitation, while it decreased rapidly within 10 h after precipitation, which was slower than that before precipitation. In terms of spatial distribution, PWV was larger in the south than in the north and larger in the east than in the west. Although the precipitation on the LPM peak was the highest in the whole LPM area, its PWV was always the lowest, indicating that the PWV was obviously affected by the air temperature. This showed that under the same water vapour condition, precipitation was more likely to form in the area with low temperature, and the precipitation was larger, which also provided a train of thought for improving the method of artificial precipitation enhancement by using the condensation catalyst.

Development of a ZTD Vertical Profile Model Considering the Spatiotemporal Variation of Height Scale Factor with Different Reanalysis Products in China

Tropospheric delay is one of the key factors that may affect high-precision satellite navigation and positioning and water vapor retrieval performance. Its variation in the vertical direction is much greater than that in the horizontal direction. Therefore, the vertical profile model of zenith total delay (ZTD) is important for the spatial interpolation of high-precision ZTD products and the development of ZTD models. However, in China, low spatial and temporal resolutions remain persistent in ZTD vertical profile models and limit their application. In this study, ZTD vertical profile grid models (CZTD-H model: CZTD-HM and CZTD-HE models) were developed by considering the time-varying height scale factor for China and employing ZTD layered profile information with high temporal-spatial resolution calculated using MERRA-2 data and ERA5 data based on the integration method during 2012–2016. The CZTD-H model accuracy was verified using the global navigation satellite system (GNSS) data acquired from the Crustal Movement Observation Network of China (CMONOC) and radiosonde data as reference and was compared with the canonical GPT3 model accuracy. The applicability of CZTD-HM and CZTD-HE models was discussed. The results showed that: (1) CZTD-HM and CZTD-HE models exhibited excellent performance for ZTD layered vertical interpolation in northwestern and southeastern China, respectively. Among ZTD layered profiles from 84 radiosonde stations, the RMSE of ZTD data interpolated using CZTD-HM and CZTD-HE models improved by 12.9/16.23% and 13.8/17.16% compared with GPT3-1 and GPT3-5 models, respectively. (2) The CZTD-H model maintained high performance for the spatial interpolation of GGOS grid ZTD data. Validation with ZTD data from 249 GNSS stations showed that the RMSEs of both CZTD-HM and CZTD-HE models improved by 2.8 mm (19.7%) and 2.6 mm (18.6%) compared with those of the GPT3-1 and GPT3-5 models, respectively. The CZTD-HE model showed excellent performance in summer among all the models. Only the location and day of the year were required for the application of the CZTD-H model, which showed excellent ZTD vertical correction performance in China. With the different performances of the CZTD-HE and CZTD-HM models in China, the ERA5 model can be recommended for practical applications. Therefore, these results can provide a reference for the data source selection of ZTD vertical profile model construction on the basis of high-precision reanalysis data, GNSS real-time high-precision positioning, and GNSS meteorology in China.

Applying Malawi Continuously Operating Reference Stations (CORS) in GNSS Meteorology

Global Navigation Satellite System (GNSS) signals in the L-band are affected by the non-dispersive neutral atmosphere. Regardless of their center frequency, the L-band code and phase observations are affected by the same measure of delay. GNSS receivers play a significant role in quantifying the zenith tropospheric delay (ZTD) from satellite signals. Malawi has a Continuously Operating Reference Stations (CORS) network which was established to support research in geophysical geodesy and geodynamics. However, the quality of the observations tracked by the CORS has never been tested in terms of its meteorological application. In this paper, the ZTD estimation approach and the evaluation of results from the Global Positioning System (GPS) measurements are presented. The optimal approach of precise point positioning (PPP) was used to estimate ZTD from one-week datasets which were collected from six CORS monuments distributed in the northern and southern regions of Malawi. In addition, the zenith wet delay (ZWD) and zenith hydrostatic delay (ZHD) were also estimated to determine their respective contributions to the total delay in all the stations. Alongside the meteorological parameters, the positioning repeatabilities were also established for all stations. Results indicate that the averaged ZTD, ZWD and ZHD can reach as high as 247mm, 47 mm, and 199 mm, respectively. The minimum ZTD, ZWD, and ZHD for the stations can drop to as low as 220 mm, 24 mm, and 181 mm, respectively. This indicates that the ZHD contributes to more than 90% of the total delay at the stations. For the positioning performance, there was no obvious disparity in the latitude (less than 0.5 cm), longitude (less than 1 cm), and ellipsoidal height repeatabilities (less than 1.5 cm). Thus, the results clearly demonstrate that the Malawi CORS network may be used for GNSS-based meteorological applications using the available geodetic receivers. However, for high precision meteorological applications, Malawi may consider densifying the available network with geodetic grade receivers.

Random Forest-Based Model for Estimating Weighted Mean Temperature in Mainland China

The weighted mean temperature (Tm) is a vital parameter for converting zenith wet delay (ZWD) into precipitation water vapor (PWV) and plays an essential part in the Global Navigation Satellite System (GNSS) inversion of PWV. To address the inability of current mainstream models to fit the nonlinear relationship between Tm and meteorological and spatiotemporal factors, whose accuracy is limited, a weighted mean temperature model using the random forest (named RFTm) was proposed to enhance the accuracy of the Tm predictions in mainland China. The validation with the Tm from 84 radiosonde stations in 2018 showed that the root mean square (RMS) of the RFTm model was reduced by 38.8%, 44.7%, and 35.5% relative to the widely used Global Pressure and Temperature 3 (GPT3) with 1° × 1°/5° × 5° versions and Bevis, respectively. The Bias and RMS of the new model in different latitude bands, various height intervals, and different times were significantly better than those of the other three comparative models. The accuracy of the new model presented a more stable adaptability. Therefore, this study provides a new idea for estimating Tm and can provide a more accurate Tm for GNSS meteorology.

A Review on Multi-GNSS for Earth Observation and Emerging Applications

Global Navigation Satellite System (GNSS) has drawn the attention of scientists and users all over the world for its wide-ranging Earth observations and applications. Since the end of May 2022, more than 130 satellites are available for fully global operational satellite navigation systems, such as BeiDou Navigation Satellite System (BDS), Galileo, GLONASS and GPS, which have been widely used in positioning, navigation, and timing (PNT), e.g., precise orbit determination and location-based services. Recently, the refracted, reflected, and scattered signals from GNSS can remotely sense the Earth’s surface and atmosphere with potential applications in environmental remote sensing. In this paper, a review of multi-GNSS for Earth Observation and emerging application progress is presented, including GNSS positioning and orbiting, GNSS meteorology, GNSS ionosphere and space weather, GNSS-Reflectometry and GNSS earthquake monitoring, as well as GNSS integrated techniques for land and structural health monitoring. One of the most significant findings from this review is that, nowadays, GNSS is one of the best techniques in the field of Earth observation, not only for traditional positioning applications, but also for integrated remote sensing applications. With continuous improvements and developments in terms of performance, availability, modernization, and hybridizing, multi-GNSS will become a milestone for Earth observations and future applications.

Weighted Mean Temperature Hybrid Models in China Based on Artificial Neural Network Methods

The weighted mean temperature (Tm) is crucial for converting zenith wet delay to precipitable water vapor in global navigation satellite system meteorology. Mainstream Tm models have the shortcomings of poor universality and severe local accuracy loss, and they cannot reflect the nonlinear relationship between Tm and meteorological/spatiotemporal factors. Artificial neural network methods can effectively solve these problems. This study combines the advantages of the models that need in situ meteorological parameters and the empirical models to propose Tm hybrid models based on artificial neural network methods. The verification results showed that, compared with the Bevis, GPT3, and HGPT models, the root mean square errors (RMSEs) of the new three hybrid models were reduced by 35.3%/32.0%/31.6%, 40.8%/37.8%/37.4%, and 39.5%/36.4%/36.0%, respectively. The consistency of the new three hybrid models was more stable than the Bevis, GPT3, and HGPT models in terms of space and time. In addition, the three models occupy 99.6% less computer storage space than the GPT3 model, and the number of parameters was reduced by 99.2%. To better evaluate the improvement of hybrid models Tm in the precipitable water vapor (PWV) retrieval, the PWVs calculated using the radiosonde Tm and zenith wet delay (ZWD) were used as the reference. The RMSE of PWV derived from the best hybrid model’s Tm and the radiosonde ZWD meets the demand for meteorological research and is improved by 33.9%, 36.4%, and 37.0% compared with that of Bevis, GPT3, and HGPT models, respectively. The hypothesis testing results further verified that these improvements are significant. Therefore, these new models can be used for high-precision Tm estimation in China, especially in Global Navigation Satellite System (GNSS) receivers without ample storage space.

Transformer-Based Global Zenith Tropospheric Delay Forecasting Model

Zenith tropospheric delay (ZTD) plays an important role in high-precision global navigation satellite system (GNSS) positioning and meteorology. At present, commonly used ZTD forecasting models comprise empirical, meteorological parameter, and neural network models. The empirical model can only fit approximate periodic variations, and its accuracy is relatively low. The accuracy of the meteorological parameter model depends heavily on the accuracy of the meteorological parameters. The recurrent neural network (RNN) is suitable for short-term series data prediction, but for long-term series, the ZTD prediction accuracy is clearly reduced. Long short-term memory (LSTM) has superior forecasting accuracy for long-term ZTD series; however, the LSTM model is complex, cannot be parallelized, and is time-consuming. In this study, we propose a novel ZTD time-series forecasting utilizing transformer-based machine-learning methods that are popular in natural language processing (NLP) and forecasting global ZTD, the training parameters provided by the global geodetic observing system (GGOS). The proposed transformer model leverages self-attention mechanisms by encoder and decoder modules to learn complex patterns and dynamics from long ZTD time series. The numeric results showed that the root mean square error (RMSE) of the forecasting ZTD results were 1.8 cm and mean bias, STD, MAE, and R 0.0, 1.7, 1.3, and 0.95, respectively, which is superior to that of the LSTM, RNN, convolutional neural network (CNN), and GPT3 series models. We investigated the global distribution of these accuracy indicators, and the results demonstrated that the accuracy in continents was superior to maritime space transformer ZTD forecasting model accuracy at high latitudes superior to that at low latitude. In addition to the overall accuracy improvement, the proposed transformer ZTD forecast model also mitigates the accuracy variations in space and time, thereby guaranteeing high accuracy globally. This study provides a novel method to estimate the ZTD, which could potentially contribute to precise GNSS positioning and meteorology.

Monitoring the Water Vapor Content at High Spatio-Temporal Resolution Using a Network of Low-Cost Multi-GNSS Receivers

The Global Navigation Satellite System (GNSS) has the capacity for remote sensing of water vapor content in the atmosphere. Post-processing of GNSS data can provide Integrated Water Vapor (IWV) with accuracies comparable to measurements of traditional sensors, i.e. water vapor radiometers. While GNSS meteorology benefits from thousands of permanent GNSS stations operating worldwide the spatial resolution of GNSS-derived IWV is limited to tens of kilometers. Further densification of GNSS networks is achievable with low-cost GNSS receivers. We investigated the feasibility of low-cost multi-GNSS receivers for monitoring IWV. The post-processing and the real-time solution are validated against 1) the results from a geodetic-grade GNSS receiver, 2) co-located water vapor radiometer, and 3) numerical weather model (NWM). Despite the high variability of the IWV during the validation period, the standard deviation of IWV differences with respect to the water vapor radiometer was 1.0 kg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 1.5 kg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in post-processing and real-time, respectively. The city-scale variability of water vapor content in the atmosphere was monitored by a network of 16 low-cost GNSS receivers deployed in the city of Wroclaw, Poland. During rapidly changing weather conditions the disagreement between the low-cost GNSS-derived IWV field and the NWM reached up to 5.4 kg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and inter-station IWV differences exceeded 5 kg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . It has been demonstrated that low-cost GNSS receivers are reliable tools for precise determination of IWV, also in real-time. This study is the first to measure the water vapor content with a spatial resolution of single kilometers and to present a significantly diversified city-scale IWV field.

Sensing Real-Time Water Vapor Over Oceans With Low-Cost GNSS Receivers

Water vapor over oceans is significant for numerical weather prediction (NWP) and climate research. Ocean platform-based global navigation satellite system (GNSS) which can sense the atmospheric water vapor is becoming an important supplement for water vapor measurements over oceans. However, the application of ocean platform-based GNSS meteorology is normally based on geodetic GNSS receivers, which implies the high cost of hardware. In this contribution, we investigate the potential of retrieving real-time water vapor over oceans with a low-cost receiver (u-blox F9P), and a geodetic GNSS receiver (Trimble NetR9) is also equipped in the experiment vessel. The post-processed Trimble NetR9 zenith total delay (ZTD) estimates and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 precipitable water vapor (PWV) products are used for the validation of real-time ZTDs and PWV values. The results show that the real-time ZTDs derived from the low-cost multi-GNSS (GPS + Galileo) observations obtain a difference of over 2.13 cm in root-mean-square (RMS) compared to the post-processed ZTDs with an averaged initialization time of approximately 40 mins. In addition, compared to ERA5 PWV, the real-time PWV derived from u-blox F9P multi-GNSS observations shows a difference in RMS of approximately 4 mm. Although u-blox F9P multi-GNSS performs relatively worse than Trimble NetR9 multi-GNSS in real-time ZTD/PWV estimates, the accuracy of low-cost GNSS receivers derived water vapor over oceans can still meet the requirements for NWP and nowcasting, which demonstrates promising prospects in supplementing the measurements of water vapor over oceans.

Development and evaluation of the refined zenith tropospheric delay (ZTD) models

The tropospheric delay is a significant error source in Global Navigation Satellite System (GNSS) positioning and navigation. It is usually projected into zenith direction by using a mapping function. It is particularly important to establish a model that can provide stable and accurate Zenith Tropospheric Delay (ZTD). Because of the regional accuracy difference and poor stability of the traditional ZTD models, this paper proposed two methods to refine the Hopfield and Saastamoinen ZTD models. One is by adding annual and semi-annual periodic terms and the other is based on Back-Propagation Artificial Neutral Network (BP-ANN). Using 5-year data from 2011 to 2015 collected at 67 GNSS reference stations in China and its surrounding regions, the four refined models were constructed. The tropospheric products at these GNSS stations were derived from the site-wise Vienna Mapping Function 1 (VMP1). The spatial analysis, temporal analysis, and residual distribution analysis for all the six models were conducted using the data from 2016 to 2017. The results show that the refined models can effectively improve the accuracy compared with the traditional models. For the Hopfield model, the improvement for the Root Mean Square Error (RMSE) and bias reached 24.5/49.7 and 34.0/52.8 mm, respectively. These values became 8.8/26.7 and 14.7/28.8 mm when the Saastamoinen model was refined using the two methods. This exploration is conducive to GNSS navigation and positioning and GNSS meteorology by providing more accurate tropospheric prior information.

Benefits of Using Galileo for Real-Time GNSS Meteorology

Remote sensing of water vapor using Global Navigation Satellite Systems (GNSS) is a well-established tool for weather and climate monitoring. The current challenges of GNSS meteorology are real-time performance and the inclusion of emerging GNSS, such as Galileo. We demonstrate that real-time GPS-only, Galileo-only, and GPS+Galileo solutions are consistent among each other. However, our results show that the Galileo-only solutions tend to underestimate Zenith Total Delay (ZTD) with respect to GPS. The Galileo-only real-time ZTD is less accurate as the one from GPS. The combination of both GNSS leads to a superior product. The daily solution availability increases by up to 50%, and the overall gain is 0.7% over the entire year. The accuracy improves by 3.7% to 8.5% and uncertainty is reduced by a factor of 1.5–2. A combined GPS and Galileo solution suppresses artifacts in a real-time ZTD product which otherwise would be attributed to high-frequency orbital effects.

Kinematic ZTD Estimation from Train-Borne Single-Frequency GNSS: Validation and Assimilation

Water vapour is one of the most important parameters utilized for the description of state and evolution of the Earth’s atmosphere. It is the most effective greenhouse gas and shows high variability, both in space and time. Thus, detailed knowledge of its distribution is of immense importance for weather forecasting, and therefore high resolution observations are crucial for accurate precipitation forecasts, especially for the short-term prediction of severe weather. Although not intentionally built for this purpose, Global Navigation Satellite Systems (GNSS) have proven to meet those requirements. The derivation of water vapour content from GNSS observations is based on the fact that electromagnetic signals are delayed when travelling through the atmosphere. The most prominent parameterization of this delay is the Zenith Total Delay (ZTD), which has been studied extensively as a major error term in GNSS positioning. On the other hand, the ZTD has also been proven to provide substantial benefits for atmospheric research and especially Numerical Weather Prediction (NWP) model performance. Based on these facts, the scientific area of GNSS Meteorology has emerged. The present study goes beyond the current status of GNSS Meteorology, showing how reasonable estimates of ZTD can be derived from highly-kinematic, single-frequency (SF) GNSS data. This data was gathered from trains of the Austrian Federal Railways (ÖBB) and processed using the Precise Point Positioning (PPP) technique. The special nature of the observations yields a number of additional challenges, ranging from appropriate pre-processing and parameter settings in PPP to more sophisticated validation and assimilation methodologies . The treatment of the ionosphere for SF-GNSS data represents one of the major challenges of this study. Two test cases (train travels) were processed using different strategies and validated using ZTD calculated from ERA5 reanalysis data. The validation results indicate a good overall agreement between the GNSS-ZTD solutions and ERA5-derived ZTD, although substantial variability between solutions was still observed for specific sections of the test tracks. The bias and standard deviation values ranged between 1 mm and 8 cm, heavily depending on the utilized processing strategy and investigated train route. Finally, initial experiments for the assimilation of GNSS-ZTD estimates into a NWP model were conducted, and the results showed observation acceptance rates of 30–100% largely depending on the test case and processing strategy.

Advanced Grid Model of Weighted Mean Temperature Based on Feedforward Neural Network Over China

AbstractWeighted mean temperature (Tm) is a key parameter in Global Navigation Satellite System meteorology. In this study, European Centre for Medium‐Range Weather Forecasts Re‐Analysis product with a spatial resolution of 0.5° × 0.5° from 1999 to 2018 was used to study the spatiotemporal behaviors of Tm in China. Decomposed by Fast Fourier Transformation, Tm and lapse rate (β) variations are highly latitude‐dependent and exhibit periodicities on annual, semi‐annual, and diurnal scales. Meanwhile, Tm keeps increasing at a rate of 0.25 K per decade across China. Based on these discoveries, this study build a new grid Tm model based on feedforward neural network (FNN) with a spatial resolution of 0.5° × 0.5°, known as Grid‐FNN model. FNN is applied to each grid point to compensate the residual error of the corresponding periodic functions. And the fitting accuracy at each grid is improved by the FNN algorithm. ERA‐Interim product with a spatial resolution of 0.4° × 0.4° and Radiosonde data in 2018 are used to validate the new model, and the accuracy of Grid‐FNN model is proved 8.6% and 10.9% better than GPT2w‐Tm model, respectively. The Grid‐FNN model also shows better performance than IGPT2w, GTm‐III, and GTrop model in autumn and winter and in high‐altitude regions.