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.

The urban ecological environment is related to human health and is one of the most concerned issues nowadays. Hence, it is essential to detect and then evaluate the urban ecological environment. However, the conventional manual detection methods have many limitations, such as the high cost of labor, time, and capital. The aim of this paper is to evaluate the urban ecological environment more conveniently and reasonably, thus this paper proposed an ecological environment evaluation method based on remote sensing and a projection pursuit model. Firstly, a series of criteria for the urban ecological environment in Shanghai City are obtained through remote sensing technology. Then, the ecological environment is comprehensively evaluated using the projection pursuit model. Lastly, the ecological environment changes of Shanghai City are analyzed. The results show that the average remote sensing ecological index of Shanghai in 2020 increased obviously compared with that in 2016. In addition, Jinshan District, Songjiang District, and Qingpu District have higher ecological environment quality, while Hongkou District, Jingan District, and Huangpu District have lower ecological environment quality. In addition, the ecological environment of all districts has a significant positive spatial autocorrelation. These findings suggest that the ecological environment of Shanghai has improved overall in the past five years. In addition, Hongkou District, Jingan District, and Huangpu District should put more effort into improving the ecological environment in future, and the improvement of ecological environment should consider the impact of surrounding districts. Moreover, the proposed weight setting method is more reasonable, and the proposed evaluation method is convenient and practical.

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.

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