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Objectives: Water vapor is one of the important components of the atmosphere, and its spatial and temporal variations are influenced by various meteorological factors such as temperature and pressure. Therefore, it is important to study the impact factors of water vapor inversion to obtain high-precision water vapor information. Methods: We study the method of precipitable water vapor (PWV) detection using global navigation satellite system (GNSS) and non-measured meteorological data (temperature and pressure) in China. First, we evaluate the data of temperature and pressure provided by the European Centre for Medium-Range Weather Forecasting (ECMWF) reanalysis v5 (ERA5), and the zenith troposphere delay retrieved by GNSS. Then, different calculation models of atmospheric weighted mean temperature, named Tm, are analyzed to determine the optimal calculation model in China region. Finally, the theoretical error of PWV is derived from the error propagation theory, and the hourly resolution PWV is calculated using the non-measured data, and then the accuracy is evaluated. Results: The results show that the PWV obtained by GNSS in China region is in good spatial and temporal agreement with PWV derived from radio sounding and ERA5. The error of PWV during the rainfall period is slightly larger than that during the nonrainfall period. The theoretical error of PWV obtained by the proposed method is 2.0 mm, and the actual error is 2.1 mm. Conclusions: The inversion of PWV based on GNSS and non-measured meteorological parameters has high accuracy and it is important for studying the spatial and temporal variation and distribution of water vapor in China region. [ABSTRACT FROM AUTHOR]

Objectives: In order to explore the impact of the decrease of human activities on the air quality in China during the period of the public health emergencies in 2019, the temporal and spatial abnormal changes of aerosol optical depth (AOD), precipitable water vapor (PWV) and temperature (T) are analyzed, and the impact of human activities on the air quality is revealed. Methods: First, the accuracy of AOD, PWV and T is verified by comparing with AOD provided by aerosol robotic network and PWV and T provided by radiosonde. Second, we analyze the long-term trends of AOD, PWV and T during the weekend and the week, and find that human activities have a certain impact on the air quality. Third, the temporal and spatial changes of AOD, PWV and T during the period of public health emergencies are studied, which confirmed that there is a good correlation between human activities and air quality. Finally, 184 cities of different grades in China are selected for further analysis to determine the impact of population density on AOD, PWV and T, and further reveal the specific response relationship between human activities and air quality. Results: Through the verification of the accuracy of the used data, it is found that the selected data have high accuracy, which can be used in this experimental study. By analyzing the PWV,AOD and T changes in public health emergencies, it is found that PWV, AOD and T are all affected. Conclusions: Due to the influence of public health emergencies, AOD, PWV and T show different trends. At the same time, it is found that the main reason for this phenomenon is the influence of the intensity of human activities. [ABSTRACT FROM AUTHOR]

Objectives: Precipitable water vapor (PWV) information with high precision and high spatialtemporal resolution plays an important role in the study of extreme weather. The PWV obtained by traditional single water vapor detection technology has the defects of poor precision and low spatial-temporal resolution due to the limitations of its system design.Methods: To solve this problem, we propose a PWV hybrid model based on multi-source data,called the GSP(GPT2w+spherical harmonical function+polynomial fitting) model.In this model,the initial value of PWV is calculated by the GPT2w model,the residual sequence of PWV is fitted by a spherical harmonic function, and after that, deviation correction is performed for the residual PWV based on the polynomial fitting, and the Bartlett test is introduced to deter mine the optimal weights of multi-source data in the GSP model.Results: The data of 26 GNSS stations and 37 ERA-Interim grid points (1°×1°) in Yunnan Province,China has been selected to validate the GSP model, and the numerical results show that the accuracy improvement rate of the GSP model is 15%— 18% compared with the traditional polynomial fitting model. Compared with the ERA5 (0.25°×0.25°) data, the mean root mean square and Bias of GSP model are 1.64 mm and －0.25 mm, respectively.Conclu⁃ sions: The above results show that the proposed GSP model has high accuracy and plays an important role in extreme weather warnings. [ABSTRACT FROM AUTHOR]

Objectives: Atmospheric water vapor is one of the important components in troposphere. It is of great significance to study the factors and accuracy of influencing water vapor. This paper mainly studies the influencing factors of precipitable water vapor (PWV) in the Loess Plateau and evaluates its actual accuracy. Methods: Firstly, the pressure, temperature data of the fifth-generation atmospheric reanalysis data of EC-MWF (ERA5) and global navigation satellite system (GNSS) derived zenith troposphere delay (ZTD) are evaluated. Secondly, according to the pressure and temperature data of ERA5 and ZTD data of crustal movement observation network of China (CMONOC), the PWV with 1-hour time resolution is calculated, and the theoretical error of PWV is deduced by using error propagation theory, and then the accuracy of PWV in the Loess Plateau is analyzed by comparing with the actual calculation error of PWV. Results: The results show that the average root mean square(RMS) and bias of GNSS-derived ZTD calculated by GAMIT/GLOBK and PANDA are 4.05 mm and −0.46 mm, respectively. The average RMS and bias of ERA5-provided pressure and temperature are 3.36 hPa/1.97 K and − 0.01 hPa/0.04 K, respectively. The theoretical error of PWV in the Loess Plateau is 1.51 mm, and the actual error is 1.94 mm. Conclusions: The accuracy of PWV calculated in this paper is high, which is of great significance to the investigation of water vapor distribution and climate monitoring. [ABSTRACT FROM AUTHOR]

Objectives：GNSS tomography technique is one of the most important methods to obtain the three‐dimensional water vapor information. However, due to lack of enough initial prior information in the process of building the tomography model, the design matrix of tomography model is unstable and the tomographic result is poor, which has become an urgent problem to be resolved.Methods: First of all, the grid data derived from European Center for Medium‐Range Weather Forecasting (ECMWF) is used to calculate the initial value of water vapor density in every voxel of interest area. And then, sophisticating the traditional tomography modeling using the calculated initial water vapor values. Finally, the influence of weightings of different equations in tomography model on tomographic result is also considered.Results: Comparing to the traditional methods, the proposed approach can enhance the accuracy of tomographic result by 41.2%, and its root mean squared error (RMSE) decreases from 1.82 g/m³ to 1.07 g/m³ . Additionally, the mean absolute error (MAE), Bias and standard deviation (STD) also show a better performance than those of the traditional methods.Conclusions: The purpose is to improve the accuracy of tomographic water vapor profiles and used to sophisticate the established tomography model. [ABSTRACT FROM AUTHOR]

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Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The number of signal rays passing through a research area are often limited due to the geometric distribution of satellite constellations and receivers. To solve this issue, an approach is proposed that imposes virtual slant signals on the tomographic model. This approach increases the number of signal rays passing through a research area and. improves accuracy. An experiment tested the feasibility and accuracy of proposed approach by comparing with the results using Global Positioning System and meteorological data for 31 days in May, 2013, provided by Hong Kong Satellite Positioning Reference Station Network and those derived from radiosonde data. The Root Mean Square water vapor profile and error statistics for proposed method are all superior to that of traditional method. The experiment shows that the proposed approach can improve the accuracy of tomographic results. [ABSTRACT FROM AUTHOR]

In existing Global Navigation Satellite System (GNSS) water vapor tomographic techniques, only signals which penetrate the entire research area are used. Due to the specific geometric distribution of satellite constellations and receivers as well as the specificity of tomography area, many signals penetrating from the side of a research area are excluded as ineffective information, which undermines the tomographic result. To solve this issue, a method is proposed for improving the utilization of signal rays by adding an assisted tomographic area, which allows the signal information crossed from the side of a research area to be utilized as well. An experiment using data from the Continuously Operation Reference Stations (CORS) network of Texas in UAS, is compared with radiosonde data of 72 249. The results show that the proposed method can enhance the accuracy of tomographic results, the improvement rate of Root Mean Square Error (RMS) was 14.6%. [ABSTRACT FROM AUTHOR]