Accurate and high-spatiotemporal-resolution predictions of precipitable water vapor (PWV) play a crucial role in numerous atmospheric processes, including global navigation satellite system (GNSS) ...meteorological evaluation, weather forecasting, water cycle analysis, climate trend detection, and related fields. The calculation of PWV typically relies on meteorological parameters and conversion coefficients, leading to low accuracy and dependence on the availability of meteorological data. This research has formulated a high-accuracy, spatiotemporal-resolution PWV retrieval model that leverages non-meteorological parameters through the implementation of a multi-layer perceptron (MLP) neural network. The new retrieval model utilizes spatiotemporal information and the zenith tropospheric delay (ZTD) datasets from each GNSS station as training input parameters, with numerical weather model PWV serving as the training output parameters. The MLP neural network was trained using datasets from 229 stations of the Crustal Movement Observation Network of China (CMONOC) and numerical weather model PWV data collected from 2005 to 2015. The experimental results indicate that the root mean square (RMS) fitting accuracy between MLP PWV and ERA PWV is 1.36 mm. The MLP PWV model demonstrates an accuracy of approximately 1.6 mm on both temporal and spatial scales, with a spatiotemporal RMS accuracy indicator of 1.92 mm. The testing accuracy of the MLP PWV, approximately 2 mm when compared with the numerical meteorological parameters model PWV, underscores the reliability of this work. The MLP PWV retrieval model, relying on non-meteorological parameters, has demonstrated exceptional accuracy. By incorporating high spatiotemporal zenith tropospheric delay (ZTD), station spatial data, and temporal information, this model achieves superior spatiotemporal PWV accuracy, showcasing the effectiveness of the methodology presented in this study.
The precise correction of atmospheric zenith tropospheric delay (ZTD) is significant for the Global Navigation Satellite System (GNSS) performance regarding positioning accuracy and convergence time. ...In the past decades, many empirical ZTD models based on whether the gridded or scattered ZTD products have been proposed and widely used in the GNSS positioning applications. But there is no comprehensive evaluation of these models for the whole China region, which features complicated topography and climate. In this study, we completely assess the typical empirical models, the IGGtropSH model (gridded, non-meteorology), the SHAtropE model (scattered, non-meteorology), and the GPT3 model (gridded, meteorology) using the Crustal Movement Observation Network of China (CMONOC) network. In general, the results show that the three models share consistent performance with RMSE/bias of 37.45/1.63, 37.13/2.20, and 38.27/1.34 mm for the GPT3, SHAtropE and IGGtropSH model, respectively. However, the models had a distinct performance regarding geographical distribution, elevation, seasonal variations, and daily variation. In the southeastern region of China, RMSE values are around 50 mm, which are much higher than that in the western region, approximately 20 mm. The SHAtropE model exhibits better performance for areas with large variations in elevation. The GPT3 model and the IGGtropSH model are more stable across different months, and the SHAtropE model based on the GNSS data exhibits superior performance across various UTC epochs.
•GNSS observation data collected in urban and rural areas in Los Angeles.•GNNS observation data processed with Precise Point Positioning (PPP) technique.•Algorithm developed to estimate temperature ...from Zenith Tropospheric Delay (ZTD).•Algorithm has been validated using temperature data from Los Angeles, California.•The Urban Heat Island in Los Angeles California has been monitored with GNSS data.
The Urban Heat Island (UHI) effect occurs when an urban area experiences higher temperatures than its rural surrounding because of heat being absorbed by built structures and heat being released by anthropogenic sources. UHIs can cause adverse effects to human health and increase energy consumption used for cooling buildings. Therefore, it is important to monitor accurately the UHI effect. The intensity of UHIs are usually monitored using satellite imagery, airborne sensors, and surface temperature sensors. Satellite imagery can cover a large area but requires a clear sky to obtain good images. Moreover, airborne sensors are expensive and also require a clear sky to obtain good data. A large network of surface temperature sensors is required to monitor the UHI of an entire region, which can also be expensive. In this paper, we present a three-step algorithm to monitor UHI intensity using data from Global Navigation Satellite Systems (GNSS). The advantages of using GNSS data to monitor the UHI effect are the increased availability of observation data, high temporal resolution and high geographical resolution. The first step of the algorithm is the calculation of a priori environmental parameters (i.e., water vapour partial pressure, troposphere height, surface pressure, and the vertical profile of refractivity) from radiosonde data. The second step is the calculation of temperature from GNSS data. The last step is the UHI intensity computation. The algorithm presented in this paper has been tested and validated using publicly available GNSS and meteorological data from Los Angeles, California, USA. The validation of the algorithm is done by comparing the UHI intensity estimated from the algorithm with temperature data obtained from weather stations. In the validation, the proposed algorithm can achieve an accuracy of 1.71 °C at 95 % confidence level.
The rapid development of multi-Global Navigation Satellite Systems (GNSSs, e.g., BeiDou, Galileo, GLONASS, and GPS) and the International GNSS Service (IGS) Multi-GNSS Experiment (MGEX) brings great ...opportunities and challenges for real-time determination of tropospheric zenith total delays (ZTDs) and integrated water vapor (IWV) to improve numerical weather prediction, particularly for nowcasting or severe weather event monitoring. In this paper, we develop a multi-GNSS model to fully exploit the potential of observations from all currently available GNSSs for enhancing real-time ZTD/IWV processing. A prototype multi-GNSS real-time ZTD/IWV monitoring system is also designed and realized at the Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences (GFZ) based on the precise point positioning technique. The ZTD and IWV derived from multi-GNSS stations are carefully analyzed and compared with those from collocated Very Long Baseline Interferometry and radiosonde stations. The performance of individual GNSS is assessed, and the significant benefit of multi-GNSS for real-time water vapor retrieval is also evaluated. The statistical results show that accuracy of several millimeters with high reliability is achievable for the multi-GNSS-based real-time ZTD estimates, which corresponds to about 1- to 1.5-mm accuracy for the IWV. The ZTD/IWV with improved accuracy and reliability would be beneficial for atmospheric sounding systems, particularly for time-critical geodetic and meteorological applications.
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.
Weighted mean temperature (Tm) and pressure (Ps) are two parameters of great relevance to precipitable water vapor (PWV) retrieval from global positioning system (GPS) data. However, information ...about the Tm and Ps cannot be available for those GPS stations that are not colocated with meteorological sensors. To investigate the optimal GPS‐PWV retrieval method for China, two enhanced Tm models, GM‐Tm (temperature dependent) and GH‐Tm (temperature independent), are developed. Additionally, the potentials of the Ps data from the two reanalysis data sets, the National Centers for Environmental Prediction (NCEP)‐Department of Energy (DOE) Reanalysis II (NCEP II) and ERA‐Interim, and from the empirical model GPT2w for GPS‐PWV retrieval are investigated over China. To evaluate the performances of multisources Tm and Ps data for GPS‐PWV retrieval, GPS data (2011–2013) collected from 22 stations of the Crustal Movement Observation Network of China (CMONOC) were processed by using the precise point positioning (PPP) technique, estimating the zenith tropospheric delay (ZTD) so as to be subsequently converted to GPS‐PWV. The retrieved GPS‐PWVs are compared with their counterparts derived from NCEP II and radiosonde data over China. The results show that (1) the GM‐Tm model consistently shows the highest accuracy (with root mean square error of 2.3 K), and the GH‐Tm model should be selected when temperature observations are not available, and that (2) the performances of Ps from NCEP II and ERA‐Interim differ marginally for GPS‐PWV retrieval, and significant seasonal variations are found in the agreement between the GPS‐PWVs and the PWVs derived from NCEP II and radiosonde data over China.
Key Points
Two enhanced Tm models are developed for the Chinese region
The potentials of the Ps from NCEP‐DOE Reanalysis II, ERA‐Interim, and the GPT2w model for GPS‐PWV retrieval are investigated over China
The detailed comparisons between the GPS‐PWVs and the PWVs derived from NCEP‐DOE Reanalysis II and radiosonde data are conducted
Tropospheric delay is one of the main errors affecting high-precision positioning and navigation and is a key parameter of water vapor detection in the Global Navigation Satellite System (GNSS). The ...second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) is the latest generation of reanalysis data collected by the National Aeronautics and Space Administration (NASA), which can be used to calculate tropospheric delay products with high spatial and temporal resolution. However, there is no report analyzing the accuracy of the zenith tropospheric delay (ZTD) and zenith wet delay (ZWD) calculated from MERRA-2 data. This paper evaluates the performance of the ZTD and ZWD values derived from global MERRA-2 data using global radiosonde data and International GNSS Service (IGS) precise ZTD products. The results are as follows: (1) Taking the precision ZTD products of 316 IGS stations from around the world from 2015 to 2017 as the reference, the average root mean square (RMS) of the ZTD values calculated from the MERRA-2 data is better than 1.35 cm, and the accuracy difference between different years is small. The bias and RMS of the ZTD values show certain seasonal variations, with a higher accuracy in winter and a lower accuracy in summer, and the RMS decreases from the equator to the poles. However, those of the ZTD values do not show obvious variations according to elevation. (2) Relative to the radiosonde data, the RMS of the ZWD and ZTD values calculated from the MERRA-2 data are better than 1.37 cm and 1.45 cm, respectively. Furthermore, the bias and RMS of the ZWD and ZTD values also show some temporal and spatial characteristics, which are similar to the test results of the IGS stations. It is suggested that MERRA-2 data can be used for global tropospheric vertical profile model construction because of their high accuracy and good stability in the global calculation of the ZWD and ZTD.
The latest reanalysis of the European Center for Medium-Range Weather Forecasts (ECMWF), ERA5, can provide atmospheric data for calculating Zenith Tropospheric Delay (ZTD) with hourly temporal ...resolution, which is a key factor in Global Navigation Satellite System (GNSS) high-precision application. This paper is aimed at evaluating the performance of ZTD derived from ERA5 reanalysis data over China using 219 GNSS stations of the Crustal Movement Observation Network of China (CMONOC) covering the period from 2015 to 2016. The site-specific hourly ZTD at these stations is obtained by integration method and Saastamoinen model method on ERA5 pressure-level and surface-level reanalysis data with the temporal resolution of 1 h and the spatial resolution of 0.25° × 0.25°. Firstly, the atmospheric temperature and pressure that derived from ERA5 are compared with temperature and pressure obtained from meteorological sensors available at 193 GNSS stations. The biases are 2.31 °C and 1.26 mbar implying the accuracy and feasibility of ERA5 pressure and temperature for calculating ZTD over China. Secondly, the performance of ERA5 ZTD is systematically evaluated using ZTD from 219 GNSS sites. The average bias and Root Mean Square (RMS) of ERA5 pressure-level ZTD at all test stations in integration method are approximately 2.97 mm and 11.49 mm respectively, and those of ERA5 surface-level ZTD in model method are 7.97 mm, 39.25 mm, which indicates that ERA5 pressure-level ZTD has a higher accuracy over China. Further analysis indicates that the accuracies of ZTD derived from ERA5 pressure-level and surface-level data are approximately 13.8% and 10.9% higher than those from of ERA-Interim pressure-level and surface-level data. Moreover, ERA5 is able to accurately capture the short-term (hourly) variation of ZTD, which further indicates the better performance of ERA5. Thirdly, the temporal and spatial variation characteristics of ERA5 ZTD accuracy are further analyzed over China. The results show that the ZTD in the southern region has the lower accuracy compared with that in the northern region over China due to the influence of latitude and altitude. Furthermore, it is found that the ERA5 ZTD over China has obvious seasonality, with higher accuracy in winter and lower accuracy in summer.
Accessibility and precise modelling of tropospheric delay play a significant role in the precise Global Navigation satellite system (GNSS) positioning applications as well as meteorological studies ...and weather forecasting. However, if in the event that a GNSS Continuously Operating Reference Station (CORS) is inaccessible due to power outages, poor internet connectivity, equipment failure, and firmware issues, gaps are created in the data archive, and the quality of the tropospheric delay estimation is degraded. Over the years, several modelling approaches and methodologies have been proposed towards the precise estimation of tropospheric delay, owing to the spatiotemporal variability of water vapour content in the atmosphere. This study employs Deep learning (DL) approach with TensorFlow and Keras to develop a predictive model (DLztd) for predicting daily IGS final ZTDs over four selected IGS stations in West Africa. Daily surface meteorological parameters (Pressure (P), Temperature (T), and Water vapour partial pressure (e)), as well as daily ZTD and stations’ coordinates (latitude, and ellipsoidal height) obtained from the site-wise VMF3-ZTD products for the period 2015–2018, were used as input variables to train and test the model, while data from 2019 were used to evaluate the predictive performance of the developed model. Statistical performance indicators such as Mean Bias (MB), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), coefficient of determination (R2), Nash-Sutcliffe coefficient of Efficiency (NSE), and the fraction of prediction within a Factor of Two (FAC2) were employed to determine the degree of agreement between the DLztd model predictions and IGS final ZTD data. The results from the various analyses indicate exceptionally good prediction capability of the DLztd model with average MB, RMSE, MAPE, R2, NSE and FAC2 of 3.25 mm, 9.62 mm, 0.30%, 0.959, 0.947, and 1.00 respectively. This demonstrates that the DLztd model provides a remarkable alternative for improving the availability of the ZTD data over the IGS stations under study should the stations' data be inaccessible or unavailable.
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