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.

Many studies have reported that there is a coupling mechanism between ionosphere and earthquake (EQ). Ionospheric anomalies in the form of abnormal increases and decreases of ionospheric Total Electron Content (TEC) are even regarded as precursors to EQs. In this paper, TEC anomalies associated with three major EQs were investigated by Global Ionospheric Maps (GIMs) and GPS-TEC, including Kumamoto-shi, Japan—EQ occurred on 15 April 2016 with Mw = 7.0; Jinghe, China—EQ occurred on 8 August 2017 with Mw = 6.3; and Lagunas, Peru—EQ occurred on 26 May 2019 with Mw = 8.0. It was found that the negative ionospheric anomalies linger above or near the epicenter for 4–10 h on the day of the EQ. For each EQ, the 10-min sampling interval of TEC was extracted from three permanent GPS stations around the epicenter within 10 days before and after the EQ. Variations of TEC manifest that the negative ionospheric anomalies first appear 10 days before the EQ. From 5 days before to 2 days after the main shock, the negative ionospheric anomalies were more prominent than the other days, with the amplitude of negative ionospheric anomaly reaching −3 TECu and the relative ionospheric anomaly exceeding 20%. In case of Kumamoto-shi EQ, the solar-geomagnetic conditions were not quiet (Dst < −30 nT, Kp > 4, and F10.7 > 100 SFU) on the suspected EQ days. We discussed the differences between ionospheric anomalies caused by active solar-geomagnetic conditions and EQ. Combining the analysis results of Jinghe EQ and Lagunas EQ, under quiet solar-geomagnetic conditions (Dst > −30 nT, Kp < 4, and F10.7 < 100 SFU), it can be found that TEC responds to various solar-geomagnetic conditions and EQ differently. The negative ionospheric anomalies could be considered as significant signals of upcoming EQs. These anomalies under different solar-geomagnetic conditions may be effective to link the lithosphere and ionosphere in severe seismic zones to detect EQ precursors before future EQs.

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Monitoring spatiotemporal variations of ionospheric Vertical Total Electron Content (VTEC) is crucial for space weather and satellite positioning. In the present study, an Enhanced Neural Network (ENN) model is proposed to capture the changing characteristics of ionospheric VTEC and compared with the traditional mathematical models, i.e., the POLYnomial (POLY) model, Generalized Trigonometric Series Function (GTSF) and Spherical Harmonic Function (SHF) model. The ionospheric VTEC data obtained from 31 permanent Global Positioning System (GPS) stations in the southwest region of China on 26 August and 8 September, 2017, were used to test the performance of the mentioned models under different Solar-geomagnetic conditions. The ENN model is derived from the ensemble learning method, and the disadvantage that simple backpropagation neural network (BPNN) learners that are not robust enough is weakened by the ENN model. After statistical analysis and Single-Frequency Precise Point Positioning (SF-PPP) experiments, it is demonstrated that the ENN model is superior to the above three mathematical models, regardless of the solar-geomagnetic conditions. In terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Standard Deviation (STD), and Mean Absolute Percentage Error (MAPE), the ENN model outperforms the SHF model, which is the best mathematical model in the analysis, by 40.7%, 30.20%, 29.88%, 38.04% under quiet solar-geomagnetic conditions, and by 37.66%, 29.93%, 30.96%, 32.01% under active solar-geomagnetic conditions. In addition, the accuracy of the SF-PPP is greatly affected by the error caused by ionosphere. In the static SF-PPP experiment of this study, the ENN model can better correct ionospheric error. Under quiet and active solar-geomagnetic conditions, the SF-PPP accuracy can be improved by 85.1% and 85.2% with the ionosphere delay correction from the ENN model

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