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Precise Point Positioning (PPP) has been proven as a cost-effective and efficient technique for monitoring the precipitable water vapor (PWV), which plays a critical role in weather forecasting. The recent developments of the International Global Navigation Satellite Systems Service (IGS) Real-Time Pilot Project (RTPP) ease the difficulties of real-time determination of PWV. In this study, the quality of the real-time PPP-derived PWVs and their correlation with precipitation estimates from different global products are investigated. The accuracies of the orbits and clocks derived from the IGS02 streams are evaluated and their performance outperforms the predicted part of IGS Ultra-rapid (IGU) orbits and clocks. A year of GNSS observations from 9 stations located in Turkey are processed to estimate the PWV in the real-time. Since there are no stations to collect meteorological data near GNSS stations, the real-time solutions employ empirical Global Pressure and Temperature 3 (GPT3) and its fully consistent discrete Vienna Mapping Function 3 (VMF3) to model the troposphere. The performance of the PWV estimates is assessed by comparing with Radiosonde-derived PWV values and post-processed PPP-derived PWV estimates employing the Center for Orbit Determination in Europe (CODE) final orbits and clocks. At most sites, the root-mean-square (RMS) differences of the estimated PWVs in comparison with the radiosonde-derived PWVs are less than 2.4 mm with correlation coefficients greater than 0.90. The RMS differences between RT and post-processed PPP PWV estimates are found to be less than 1.2 mm. The estimated RT zenith total delays (ZTDs) at two IGS sites agree well with the ZTDs provided by the IGS final products. A method of precipitation monitoring is proposed based on the daily standard deviation (SD) of the PWV estimates. Experimental analyses of the proposal are undertaken by comparing the daily SDs of the PWV estimates and the daily accumulated precipitation estimates from the global precipitation grids produced from rain gauges, satellite images, and reanalyses product sets. The results show that the daily variation of the PWVs and the high precipitation estimates present a good agreement for some days but not always. The PWVs overestimate and underestimate a significant number of events. Spatial refinement of GPT3 grids and gridded precipitation products with higher sampling over Turkey may offer better results. [ABSTRACT FROM AUTHOR]

Air pollution, especially the particulate matter (PM2.5) remains to be a severe problem for the environmental managers and researchers because it causes many respiratory diseases even casualties and huge economic losses. Therefore, it is significant to study the distribution characteristics of regional PM2.5 and analyze the mutual influence relationship between it and some influencing factors including air humidity, wind speed, and atmospheric boundary conditions. Some scholars have studied the relationship between PM2.5 and the atmospheric planetary boundary layer or relative humidity. However, the relationships between air pollution and meteorological conditions in China has been extensively studied in recent years. Therefore, the association between the pollution and precipitation water vapor and wind speed in the northeast of China (e.g., Jilin and Heilongjiang province) could be strengthened. Considering all components in the atmosphere affecting each other and the ability of Weather Research and Forecast (WRF) and WRF‐Chemistry (WRF‐Chem) simulating all of the atmospheric parameters, we utilized the WRF and WRF‐Chem to simulate the precipitable water vapor, wind speed, and PM2.5. Then first, the PM2.5 concentration values predicted from WRF‐Chem are validated with those at the ground stations to evaluate the accuracy of WRF‐Chem model. Second, the relationship analysis between the PM2.5 and wind speed or PWV was done. And the results demonstrate that the wind is helpful to decrease the severe air pollution in winter while the precipitable water vapor plays a small effect on the elimination of the particulate matter. WRF‐Chem is a potential and effective tool for the study of air pollution. [ABSTRACT FROM AUTHOR]

Urbanization may exert a strong influence on both near-surface and atmospheric moisture. However, studies on the effect of urbanization on atmospheric moisture using remotely sensed observations have been infrequently conducted. To fill this research gap, we used remotely sensed and station-based observations to calculate urban–rural differences in total-column, near-surface, and atmospheric moisture in the Beijing metropolis. Multiple humidity indicators were selected, including precipitation water vapor, relative humidity, water vapor pressure, and absolute humidity. Results showed that both the total-column and near-surface urban dry islands (UDIs) were detected in Beijing. A significant decreasing trend of the near-surface UDIs intensity (i.e., urban minus rural) was found, which was closely related to the expansion of built-up areas (i.e., the moisture reduction in the newly urbanized areas). However, the trend of the total-column UDIs intensity was not significant. A further investigation of the atmospheric urban–rural moisture differences showed that the UDIs effect mainly existed in the near-surface layer, while an urban wet island was found above the 950 hPa pressure level, probably due to enhanced convergence in urban areas. Generally, the UDIs intensity was the strongest in the hot, wet summer and the weakest in the dry, cold winter. However, the UDIs intensity represented by relative humidity was the largest in autumn and showed the strongest correlation with the expansion of built-up areas in Beijing. This study employed satellite observations to understand the UDIs effect and highlighted the significance of urbanization-induced moisture changes on and above the ground. Findings of this study provided new insights into how urbanization affected atmospheric moisture in the boundary layer and paved the way for process-based modeling studies.

Precise modeling of weighted mean temperature (Tm) is critical for realizing real-time conversion from zenith wet delay (ZWD) to precipitation water vapor (PWV) in Global Navigation Satellite System (GNSS) meteorology applications. The empirical Tm models developed by neural network techniques have been proved to have better performances on the global scale; they also have fewer model parameters and are thus easy to operate. This paper aims to further deepen the research of Tm modeling with the neural network, and expand the application scope of Tm models and provide global users with more solutions for the real-time acquisition of Tm. An enhanced neural network Tm model (ENNTm) has been developed with the radiosonde data distributed globally. Compared with other empirical models, the ENNTm has some advanced features in both model design and model performance, Firstly, the data for modeling cover the whole troposphere rather than just near the Earth’s surface; secondly, the ensemble learning was employed to weaken the impact of sample disturbance on model performance and elaborate data preprocessing, including up-sampling and down-sampling, which was adopted to achieve better model performance on the global scale; furthermore, the ENNTm was designed to meet the requirements of three different application conditions by providing three sets of model parameters, i.e., Tm estimating without measured meteorological elements, Tm estimating with only measured temperature and Tm estimating with both measured temperature and water vapor pressure. The validation work is carried out by using the radiosonde data of global distribution, and results show that the ENNTm has better performance compared with other competing models from different perspectives under the same application conditions, the proposed model expanded the application scope of Tm estimation and provided the global users with more choices in the applications of real-time GNSS-PWV retrival.

The weighted mean temperature (Tm) is a key parameter when converting the zenith wet delay (ZWD) to precipitation water vapor (PWV) in ground-based Global Navigation Satellite System (GNSS) meteorology. Tm can be calculated via numerical integration with the atmospheric profile data measured along the zenith direction, but this method is not practical in most cases because it is not easy for general users to get real-time atmospheric profile data. An alternative method to obtain an accurate Tm value is to establish regional or global models on the basis of its relations with surface meteorological elements as well as the spatiotemporal variation characteristics of Tm. In this study, the complex relations between Tm and some of its essentially associated factors including the geographic position and terrain, surface temperature and surface water vapor pressure were considered to develop Tm models, and then a non-meteorological-factor Tm model (NMFTm), a single-meteorological-factor Tm model (SMFTm) and a multi-meteorological-factor Tm model (MMFTm) applicable to China and adjacent areas were established by adopting the artificial neural network technique. The generalization performance of new models was strengthened with the help of an ensemble learning method, and the model accuracies were compared with several representative published Tm models from different perspectives. The results show that the new models all exhibit consistently better performance than the competing models under the same application conditions tested by the data within the study area. The NMFTm model is superior to the latest non-meteorological model and has the advantages of simplicity and utility. Both the SMFTm model and MMFTm model show higher accuracy than all the published Tm models listed in this study; in particular, the MMFTm model is about 14.5% superior to the first-generation neural network-based Tm (NN-I) model, with the best accuracy so far in terms of the root-mean-square error.

Urbanization may exert a strong influence on both near-surface and atmospheric moisture. However, studies on the effect of urbanization on atmospheric moisture using remotely sensed observations have been infrequently conducted. To fill this research gap, we used remotely sensed and station-based observations to calculate urban–rural differences in total-column, near-surface, and atmospheric moisture in the Beijing metropolis. Multiple humidity indicators were selected, including precipitation water vapor, relative humidity, water vapor pressure, and absolute humidity. Results showed that both the total-column and near-surface urban dry islands (UDIs) were detected in Beijing. A significant decreasing trend of the near-surface UDIs intensity (i.e., urban minus rural) was found, which was closely related to the expansion of built-up areas (i.e., the moisture reduction in the newly urbanized areas). However, the trend of the total-column UDIs intensity was not significant. A further investigation of the atmospheric urban–rural moisture differences showed that the UDIs effect mainly existed in the near-surface layer, while an urban wet island was found above the 950 hPa pressure level, probably due to enhanced convergence in urban areas. Generally, the UDIs intensity was the strongest in the hot, wet summer and the weakest in the dry, cold winter. However, the UDIs intensity represented by relative humidity was the largest in autumn and showed the strongest correlation with the expansion of built-up areas in Beijing. This study employed satellite observations to understand the UDIs effect and highlighted the significance of urbanization-induced moisture changes on and above the ground. Findings of this study provided new insights into how urbanization affected atmospheric moisture in the boundary layer and paved the way for process-based modeling studies. [ABSTRACT FROM AUTHOR]

Precise modeling of weighted mean temperature ( T m ) is critical for realizing real-time conversion from zenith wet delay (ZWD) to precipitation water vapor (PWV) in Global Navigation Satellite System (GNSS) meteorology applications. The empirical T m models developed by neural network techniques have been proved to have better performances on the global scale; they also have fewer model parameters and are thus easy to operate. This paper aims to further deepen the research of T m modeling with the neural network, and expand the application scope of T m models and provide global users with more solutions for the real-time acquisition of T m . An enhanced neural network T m model (ENNTm) has been developed with the radiosonde data distributed globally. Compared with other empirical models, the ENNTm has some advanced features in both model design and model performance, Firstly, the data for modeling cover the whole troposphere rather than just near the Earth's surface; secondly, the ensemble learning was employed to weaken the impact of sample disturbance on model performance and elaborate data preprocessing, including up-sampling and down-sampling, which was adopted to achieve better model performance on the global scale; furthermore, the ENNTm was designed to meet the requirements of three different application conditions by providing three sets of model parameters, i.e., T m estimating without measured meteorological elements, T m estimating with only measured temperature and T m estimating with both measured temperature and water vapor pressure. The validation work is carried out by using the radiosonde data of global distribution, and results show that the ENNTm has better performance compared with other competing models from different perspectives under the same application conditions, the proposed model expanded the application scope of T m estimation and provided the global users with more choices in the applications of real-time GNSS-PWV retrival. [ABSTRACT FROM AUTHOR]

The weighted mean temperature ( T m ) is a key parameter when converting the zenith wet delay (ZWD) to precipitation water vapor (PWV) in ground-based Global Navigation Satellite System (GNSS) meteorology. T m can be calculated via numerical integration with the atmospheric profile data measured along the zenith direction, but this method is not practical in most cases because it is not easy for general users to get real-time atmospheric profile data. An alternative method to obtain an accurate T m value is to establish regional or global models on the basis of its relations with surface meteorological elements as well as the spatiotemporal variation characteristics of T m . In this study, the complex relations between T m and some of its essentially associated factors including the geographic position and terrain, surface temperature and surface water vapor pressure were considered to develop T m models, and then a non-meteorological-factor T m model (NMFTm), a single-meteorological-factor T m model (SMFTm) and a multi-meteorological-factor T m model (MMFTm) applicable to China and adjacent areas were established by adopting the artificial neural network technique. The generalization performance of new models was strengthened with the help of an ensemble learning method, and the model accuracies were compared with several representative published T m models from different perspectives. The results show that the new models all exhibit consistently better performance than the competing models under the same application conditions tested by the data within the study area. The NMFTm model is superior to the latest non-meteorological model and has the advantages of simplicity and utility. Both the SMFTm model and MMFTm model show higher accuracy than all the published T m models listed in this study; in particular, the MMFTm model is about 14.5% superior to the first-generation neural network-based T m (NN-I) model, with the best accuracy so far in terms of the root-mean-square error. [ABSTRACT FROM AUTHOR]