Abstract
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.
Highlights
Water vapor is a minor constituent of the Earth’s atmosphere and is mainly distributed in the lower atmosphere
The significant negative bias in the Tm estimation with new models caused by the interannual variation of Tm cannot be ignored, and an external correction was made to the results of three-layer feedforward neural network (TFNN) models after combination
The three models developed in this study are named the non-meteorological-factor Tm model (NMFTm) model, single-meteorological-factor Tm model (SMFTm) model and multi-meteorological-factor Tm model (MMFTm) model, respectively, and were able to meet the requirements of Tm calculation under different application conditions
Summary
Water vapor is a minor constituent of the Earth’s atmosphere and is mainly distributed in the lower atmosphere. Yao et al [26] established a one-meteorological-factor model (GTm) and a multi-meteorological-factor model (PTm) with data from 135 radiosonde stations distributed globally; the results showed that the PTm model was about 0.5 K better than the GTm model in terms of the root-mean-square error They further took the seasonal and geographic variation characteristics of the Tm–Ts and Tm–Ts, es relations into account to develop the GTm-I model and PTm-I model, the accuracy of which were greatly improved compared with the GTm model and PTm model, respectively, on a global scale. We took into account the geographic and seasonal variation characteristics of Tm, as well as the relations between Tm and surface meteorological elements (Ts, es), and adopted the neural network technique to develop Tm models applicable to China and adjacent areas. It is very important to get an accurate Tm value to derive a precise PWV in GNSS meteorology
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