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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]

For better modeling the vertical distribution and variations of water vapor, a 10-year time-series of a newly derived water vapor parameter (termed IRPWV), defined as the ratio of water vapor density (WVD) to the total precipitable water vapor (TPWV), was statistically analyzed. This research showed that the vertical distribution of IRPWV presents a periodic pattern and is highly correlated with the relative magnitude of its corresponding TPWV when compared with the other TPWVs in the same time range. Six TPWV ranges were first chosen to determine the relative magnitude and then used to classify the IRPWV vertical distributions of TPWV. For the periodic variations in each of the six classified IRPWV vertical distribution time-series, a temporal IRPWV model was developed accordingly with six sets of coefficients. The new models were validated by comparing their predictions against the reference values from sounding data at 12 radiosonde stations in China, and their performance was also evaluated against that of the commonly used exponential model. Results showed that, first, the proportions of the height range that had reduced annual root mean square error (RMSE) of WVD in all height ranges within all TPWV ranges were over 75% at the 12 stations. Then, the annual RMSEs of the WVD for all the stations were reduced by at least 11%, 20%, 43%, 48%, 40%, 38%, 32%, 35%, 32%, and 28% in each of the 10 selected height ranges, respectively. [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]

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]

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]

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]

Ground pressure and temperature are two key meteorological parameters for retrieving precipitable water vapor (PWV) from Global Navigation Satellite Systems (GNSS). The problem is that, many GNSS stations are either not equipped with meteorological sensors or the time resolution of meteorological data is relatively low. To improve the spatio-temporal resolution of meteorological parameters, a new spatio-temporal Kriging model based on an improved adaptive genetic algorithm (IAGA-STK) is proposed. The ERA5 (fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting) dataset derived temperature and pressure with a time resolution of 1 h and horizontal resolution of 0.125° × 0. 125 ° during the period of storm Mawar (31 Aug–4 Sep 2017), was used as inputs to the IAGA-STK model. Test data were from 13 GNSS stations equipped with meteorological sensors in the Hong Kong region, and the root mean square errors (RMSEs) of temperature and pressure from IAGA-STK model are reduced 1.5% and 5% in comparison with the traditional spatio-temporal interpolated model, respectively. GNSS-derived zenith total delays with the IAGA-STK were converted into PWV (GNSS-PWVIE), and the RMSE of the GNSS-PWVIE was less than 1.7 mm, which can meet the RMSE threshold requirements (3 mm) of PWVs as inputs to weather nowcasting. In addition, the relationship between the storm's path and water vapor was discussed, and the PWV products with a high time resolution can be used to study the life cycle of a storm. [ABSTRACT FROM AUTHOR]

ABSTRACT Radiosonde observations provide a good data source for examining the long-term trend of atmospheric water vapour, temperature and cold point tropopause ( CPT). In this article, precipitable water vapour ( PWV) and surface temperature from the Hong Kong radiosonde station over the period of 1973-2012 are analysed. We find that the atmospheric water vapour in Hong Kong in the layers from the surface to approximately 1181 m, from ∼1181 to ∼2509 m, from ∼2509 to ∼5126 m and from ∼5126 to ∼8093 m accounts for 50, 25, 20 and 5% of the PWV, respectively. The atmosphere is almost completely dry above approximately 8000 m. Surface temperature has increased at a rate of 0.16° decade−1 over the past 40 years. On a seasonal timescale, the largest rate of increase is 0.23° decade−1 in winter and the smallest rate is 0.09° decade−1 in spring. The CPT height is located at approximately 17.5 km above mean sea level over the period of this study. This CPT height is estimated to have risen at a rate of 87.3 m decade−1 over 1983-2012, which is 1.36 times the global average. The CPT temperature is observed to decrease at a rate of 0.84° decade−1, which is 2.05 times the global average. Taking advantage of the periodicity of PWV and surface temperature over the past 40 years, Fourier series analysis models have been developed. The models are evaluated using 1 year (2012) of radiosonde data. It was found that the modelled PWV data can achieve root mean square error accuracy of 9.23 mm and the modelled surface temperature can achieve a standard deviation of 2.34° with a bias of −0.19°. [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]

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]

Global Navigation Satellite Systems (GNSS) tomography plays an important role in the monitoring and tracking of the tropospheric water vapor. In this study, a new approach for improving the node-based GNSS tomography is proposed, which makes a trade-off between the real observed region and the complexity of the discretization of the tomographic region. To obtain dynamically the approximate observed region, the convex hull algorithm and minimum bounding box algorithm are used at each tomographic epoch. This new approach can dynamically define the tomographic model for all types of study areas based on the GNSS data. The performance of the new approach is tested by comparing it against the common node-based GNSS tomographic approach. Test data in May 2015 are obtained from the Hong Kong GNSS network to build the tomographic models and the radiosonde data as a reference are used for validating the quality of the new approach. The experimental results show that the root-mean-square errors of the new approach, in most cases, have a 38 percent improvement and the values of standard deviation reduce to over 43 percent compared with the common approach. The results indicate that the new approach is applicable to the node-based GNSS tomography. [ABSTRACT FROM AUTHOR]