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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.

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]