Abstract
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.
Highlights
Detecting atmospheric water vapor with Global Navigation Satellite System (GNSS)has been paid more attention than traditional techniques, because it has the advantages of low-cost, all-weather, high-precision and high-resolution in space and time [1,2,3,4,5]
Atmospheric profiles measured from 2016 to 2018 by 569 Integrated Global Radiosonde Archive (IGRA) stations were utilized to verify the performance of the enhanced neural network Tm model (ENNTm) proposed in this work
We developed an enhanced neural network Tm model named ENNTm, in the aim of achieving the excellent capability of the neural network in dealing with nonlinear optimization problems of multiple input parameters
Summary
Has been paid more attention than traditional techniques (such as water vapor radiometer, radiosonde etc.), because it has the advantages of low-cost, all-weather, high-precision and high-resolution in space and time [1,2,3,4,5]. Tm needs to be calculated by numerical integration based on the measured atmospheric profiles [6,7]; for example, the atmospheric profile data collected by radiosonde and GNSS radio occultation are the closest to the actual conditions and can be used to compute Tm [8,9,10], but they are rarely applied in the real-time retrieval of GNSS-PWV, because the cost of collecting such data is very high, which is difficult for general users to afford. The reanalysis data have been widely used for climatological studies of PWV rather than for real-time GNSS-PWV retrieval, because these data are produced using the assimilation system and there is a time lag from generation to release.
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