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This paper presents a new model for ionospheric total electron content (TEC) over China. The new model is developed using a hybrid method composed of the particle swarm optimization (PSO) and artificial neural network and long‐term observations from 257 ground‐based global navigation satellite systems (GNSS) stations and space‐borne GNSS radio occultation systems (COSMIC and Fengyun) during the 14‐year period of 2008–2021. The PSO algorithm is used to optimize the traditional back‐propagation neural network (BP‐NN) model by reducing the effects of the local minimum problem. The new model is validated using out‐of‐sample data, and its results are compared to the BP‐NN, IRI‐2016 model, and global ionospheric maps provided by the International GNSS Service. Results show that TEC predicted from the new model agrees better with the reference TEC than the BP‐NN and IRI‐2016 models. The improvements made by the new model over the BP‐NN and IRI‐2016 models in the equinox, summer, and winter seasons of the solar maximum year (2015) are 4%–20%/20%–36%, 9%–21%/26%–42%, and 6%–22%/21%–43%, respectively, and their corresponding results in the solar minimum year (2019) are 12%–24%/41%–59%, 9%–24%/28%–56%, and 10%–26%/53%–72%. Furthermore, the new model well captures the diurnal evolution, seasonal variation, and variations in the ionospheric TEC under different solar activity levels. It also well captures the mid‐latitude summer nighttime anomaly over China, and the diurnal anomaly is more pronounced in the solar minimum year (2019) than in the solar maximum year (2015) in terms of the nighttime‐to‐noontime ratio and the range of months it lasts in a year. Plain Language Summary: With the continuous expansion of ground‐based and space‐borne global navigation satellite systems (GNSS) data sets, more and more high‐resolution, high‐accuracy, and long‐term measurements are available. These data can be used for characterizing, modeling, and forecasting the ionosphere. However, due to the limitation of data analysis tools and modeling techniques, only a fraction of the huge data set has been used. As proven by previous research, the back‐propagation neural network (BP‐NN) is an efficient approach in exploring hidden patterns from a large data set. Nevertheless, BP‐NN has shortcomings, for example, the possible local minimum and slow convergence rate problem. Therefore, this study uses the particle swarm optimization neural network and long‐term observations obtained from 257 ground‐based GNSS stations and space‐borne GNSS radio occultation systems (COSMIC and Fengyun) to develop a new model of ionospheric total electron content (TEC) over China. The new model captures the TEC diurnal evolution, seasonal variation, and variations under different solar activity levels. Moreover, the new model also well captures the mid‐latitude summer nighttime anomalies over China. This new model offers more opportunities for the investigation of large‐scale ionospheric phenomena over China, like the distribution of the mid‐latitude summer nighttime anomaly and its month‐to‐month variations. Key Points: A particle swarm optimization neural network based total electron content (TEC) model over China is developed using observations of ground‐based global positioning system, COSMIC, and Fengyun during 2008–2021The new TEC model performs better than the IRI‐2016 model and back‐propagation neural network model under high and low solar activity levelsMid‐latitude summer nighttime anomaly is well captured by the new model, and the diurnal anomaly is more pronounced in 2019 than in 2015 [ABSTRACT FROM AUTHOR]

For better modeling the vertical distribution and variations of water vapor, a 10-year time-series of a newly derived water vapor parameter (termed IRPWV), defined as the ratio of water vapor density (WVD) to the total precipitable water vapor (TPWV), was statistically analyzed. This research showed that the vertical distribution of IRPWV presents a periodic pattern and is highly correlated with the relative magnitude of its corresponding TPWV when compared with the other TPWVs in the same time range. Six TPWV ranges were first chosen to determine the relative magnitude and then used to classify the IRPWV vertical distributions of TPWV. For the periodic variations in each of the six classified IRPWV vertical distribution time-series, a temporal IRPWV model was developed accordingly with six sets of coefficients. The new models were validated by comparing their predictions against the reference values from sounding data at 12 radiosonde stations in China, and their performance was also evaluated against that of the commonly used exponential model. Results showed that, first, the proportions of the height range that had reduced annual root mean square error (RMSE) of WVD in all height ranges within all TPWV ranges were over 75% at the 12 stations. Then, the annual RMSEs of the WVD for all the stations were reduced by at least 11%, 20%, 43%, 48%, 40%, 38%, 32%, 35%, 32%, and 28% in each of the 10 selected height ranges, respectively. [ABSTRACT FROM AUTHOR]

The ionospheric total electron content (TEC) is an important ionospheric parameter, and it is widely utilized in research such as space weather prediction and precise positioning. However, it is still challenging to develop an ionospheric TEC prediction model with high accuracy. In this study, a new ionospheric TEC model over China was developed using the bidirectional long short‐term memory (bi‐LSTM) method and observations from 257 ground‐based global navigation satellite system (GNSS) stations in the Crustal Movement Observation Network of China from January 2018 to December 2021. The root mean square errors of the bi‐LSTM‐based model's 1 and 2 hr ahead predictions on the test data set (from June 2021 to December 2021) are 1.12 and 1.68 TECU, respectively, which are 75/50/32% and 72/48/22% smaller than those of the IRI‐2016, artificial neural network and LSTM‐based models, correspondingly. The bi‐LSTM‐based model shows the best performance, which is most likely due to the fact that the sequence information in both forward and backward directions is taken into consideration in the new model. In addition, the diurnal variation, seasonal variation of the ionospheric TEC, and variations under geomagnetic storm conditions are successfully captured by the bi‐LSTM‐based model. Moreover, the TEC maps resulting from the bi‐LSTM model agree well with those obtained from the final ionospheric product from the Chinese Academy of Sciences. Hence, the new model can be a good choice for the investigation of the spatiotemporal variation trend in the ionosphere and GNSS navigation. Plain Language Summary: The ionospheric total electron content (TEC) is the total number of free electrons along a signal propagation path in a cross section of one square meter, and the ionospheric delay error of the signal from the global navigation satellite system (GNSS) is proportional to the value of TEC. The variation of the ionospheric TEC can also be used to study the variations in space weather. In this study, a new ionospheric TEC model over China based on the bidirectional long short‐term memory (bi‐LSTM) method and long‐term ground‐based observations at 257 GNSS stations from the Crustal Movement Observation Network of China was developed and subsequently validated. The main reason for the selection of the bi‐LSTM model is for its consideration of the sequence information in both forward and backward directions. The prediction results of the new model show that in comparison with that of three other models, including the artificial neural network, LSTM, and traditional IRI‐2016 models, the new model is the best performer. Furthermore, the bi‐LSTM model also successfully captures the diurnal and seasonal variations of the ionospheric TEC and the TEC variations under geomagnetic storm conditions. Key Points: The bidirectional long short‐term memory (bi‐LSTM) method is adopted to predict the ionospheric total electron content maps over China using long‐term ground‐based global positioning system observations from the Crustal Movement Observation Network of ChinaThe bi‐LSTM‐based model outperforms the IRI‐2016, artificial neural network and long short‐term memory‐based modelsThe bi‐LSTM‐based model can predict the ionospheric response during geomagnetic storm periods, such as the 4–7 November 2021 G3 storm period [ABSTRACT FROM AUTHOR]

The spatial distribution of oxide abundances and Mg# (Mg/(Mg + Fe)) on the lunar surface is of great significance for in-depth understanding the origin and evolution of the Moon. China's Chang'E−5 (CE-5) mission returned young lunar soils for the first time, providing a new ground truth for the inversion of oxide abundances. In this study, the relationship between multi-source remote sensing data (including Chang'E−1 Interference Imaging Spectrometer (CE-1 IIM) data and the new global Christiansen feature (CF) product, named IIM-CF data), and the abundances of six oxides (FeO, TiO 2 , MgO, SiO 2 , Al 2 O 3 and CaO) measured at 40 lunar sampling sites including CE-5 were analyzed. The use of IIM-CF data as the input features of the selected inversion models for obtaining the abundances of oxides, and the oxide abundances measured at the 40 sampling sites were used as the ground truth. The models selected for this investigation contain three typical algorithms − random forest (RF), extreme gradient boosting (XGBoost) and partial least squares regression (PLSR), and a new method integrates RF, XGBoost and PLSR together named RXP was developed in this study. The modeling results of the abundances of the six oxides derived from the above four algorithms show that the RXP algorithm outperforms the other three algorithms. The distributions of the six oxides and Mg# on the lunar surface covering the range from 70° N to 70° S (70° N/S) with a resolution of about 200 m/pixel were generated using the proposed RXP algorithm. Our results indicate that, compared with the result of a single data source, the use of IIM-CF data improved the accuracy of the modeling of oxide abundances and Mg#. It is expected that the CE-5 samples can bring additional references to the studies of the inversion for the oxides of the lunar surface and deepen our understanding over this issue. • The changes in inversion results of the Moon due to the utilization of CE-5 samples. • RXP outperforms other three models since RXP take advantages of the three algorithms. • IIM-CF data avoid the limitation of single data and enhance accuracy of the inversion. • Mg# calculated from inverted FeO and MgO more accurate than Mg# directly inverted. [ABSTRACT FROM AUTHOR]

Due to the continuous intensification of human activities in the ocean, the frequent outbreaks of red tide have caused great harm to the marine environment and ecology. Thus, the rapid detection and monitoring of red tide become particularly important. At present, the main monitoring methods depend on artificial and buoy data, as well as optical satellite remote sensing. However, these methods may not be able to effectively deal with the characteristics of red tide bloom, such as suddenness and unpredictability. The global navigation satellite system-reflectometry (GNSS-R) is an emerging technology that makes use of navigation signals as a remote sensing opportunity to obtain Earth surface information. GNSS-R has already been proved to be capable of retrieving sea surface parameters (e.g., dielectric constant and sea surface roughness) closely related to the outbreak of a red tide. In this article, we proposed a new method to estimate red tide density, which utilizes an all-new model associating GNSS-R observations with sea surface red tide density. This method can remove the weather influence and greatly decrease the revisit period, which is much longer for optical red tide remote sensing methods. The Landsat-8 near-infrared data and TechDemoSat-1 (TDS-1) GNSS-R data of a red tide outbreak in the sea off the Tsingtao coast in China are used to build and test the proposed method. The results demonstrate that the correlation coefficient is 0.73, and the root mean square error of retrieved red tide density is 2.84%, which shows that the GNSS-R technology shows great potential to perform the rapid and preliminary red tide monitoring and judgment. [ABSTRACT FROM AUTHOR]

The ionosphere has been considered as one of the major error sources in GNSS signal propagation, and it is still difficult to be modeled precisely, especially for real-time positioning applications. The commonly used ionospheric models are usually based on the one-layer approximation, which neglects the ionospheric variation in the vertical domain and limits the scope of improvement over one-layer models. A new ionospheric model based on the two-layer approximation and two spherical harmonic (SH) functions is proposed in this contribution, where a quasi-globe projection is designed to avoid the inherent ill-posed problem and retain the physical meaning of the SH when regional data are used. GPS and BDS data from the National Positioning Infrastructure of Australia and Crustal Movement Observation Network of China are used for validating the new model's performance in different areas and different periods. Results show (1) the precision of ionospheric TEC estimates from the new model can be improved by about 26% and 31% in the cross-validation experiment compared to the traditional one-layer model in Australian and Chinese regions, respectively; (2) the positioning accuracy of kinematic single-frequency precise point positioning (SF-PPP) in the experimental regions using the new model reaches about 0.7 m and 0.8 m in the horizontal and vertical components, respectively, in comparison with the one-layer model's 1.0 m (horizontal) and 1.4 m (vertical); (3) the convergence time of the SF-PPP using the new model is 5–10 min for achieving a sub-meter level of positioning accuracy in both horizontal and vertical components, whereas it needs 30–40 min in case the one-layer model is used. [ABSTRACT FROM AUTHOR]

In this study, radiosonde observations during the period of 2012-2013 from three stations in the Hunan region, China, were used to establish regional Tm models (RTMs) that are a fitting function of multiple meteorological factors (Ts, Es, and Ps). One-factor, two-factor, and three-factor RTMs were assessed by comparing their Tm against the radiosonde-derived Tm (as the truth) during the period of 2013-2014. Statistical results showed that the bias and RMS of the one-factor RTM, in comparison to the BTM result, were reduced by 88% and 28%, respectively. The two-factor and three-factor RTMs showed similar accuracy and both outperformed the one-factor RTM, with an improvement of 7% in RMS. The bias and RMS of all the four seasonal two-factor RTMs were smaller than the yearly two-factor RTM, with the improvements of 3%, 10%, 2%, and 3% in RMS. The improvement of the conversion factors in mean bias and RMS resulting from the seasonal two-factor RTM is 92% and 31%. The bias and RMS of the PWV resulting from the seasonal two-factor RTM are improved by 37% and 12%, respectively. Therefore, the seasonal two-factor RTMs are recommended for the research and applications of GNSS meteorology in the Hunan region, China. [ABSTRACT FROM AUTHOR]

Water vapor is one of the most important parameters in climatic studies. MODerate-resolution Imaging Spectroradiometer (MODIS) is a key instrument and can provide spatially continuous precipitable water vapor (PWV) products. This study was focused on the performance evaluation of the MODIS near-infrared PWV product (MOD-NIR-PWV) over China. For a comprehensive assessment of the performance of MOD-NIR-PWV, PWV retrieved from the measurements at the global navigation satellite systems (GNSS) stations (i.e., GNSS-PWV) and the ERA5 reanalysis dataset (ERA-PWV) from 2013 to 2018 were used as the reference. To investigate the suitability of using ERA-PWV as the reference for the evaluation, ERA-PWV was compared to the high-accuracy GNSS-PWV at 246 GNSS stations and PWV retrieved from radiosonde observations (RS-PWV) at 78 radiosonde stations over China. The results showed that the mean bias and mean root-mean-square (RMS) of the differences between ERA-PWV and GNSS-PWV across all the stations were 0.5 and 1.7 mm, respectively, and the mean correlation coefficient of the two datasets was above 0.96. The values were 0.4 and 1.9 mm and 0.97, respectively, for the differences between ERA-PWV and RS-PWV. This suggests the suitability of ERA-PWV as the reference for the evaluation of MOD-NIR-PWV. In addition, MOD-NIR-PWV was compared with both GNSS-PWV and ERA-PWV, and their mean bias and mean RMS were 2.9 and 3.8 mm (compared to GNSS-PWV) and 2.1 and 3.0 mm (compared to ERA-PWV), respectively. The positive bias values and the non-normal distribution of the differences between MOD-NIR-PWV and both reference datasets imply that a considerable systematic overestimation of MOD-NIR-PWV over China may exist. To mitigate the systematic bias, ERA-PWV was utilized as the sample data due to its spatial continuities, and a grid-based calibration model was developed based on the annual and semiannual periodicities in the differences between MOD-NIR-PWV and ERA-PWV at each grid point. After applying the calibration model to correct MOD-NIR-PWV, the calibrated MOD-NIR-PWV was compared with ERA-PWV and GNSS-PWV for precision and accuracy analysis, respectively. The comparison showed that the model could significantly improve the precision by 94% and accuracy by 53%, which manifested the effectiveness of the calibration model in improving the performance of MOD-NIR-PWV over China. [ABSTRACT FROM AUTHOR]

Land surface deformation in metropolitan areas, which can cause varying degrees of hazard to both human lives and to properties, has been documented for decades in cities worldwide. Xuzhou, is one of the most important energy and industrial bases in eastern China, and has experienced significant land subsidence due to both excessive extraction of karst underground water and exploitation of mineral resources in recent decades. Furthermore, Xuzhou has recently undergone rapid urbanization in terms of urban expansion and underground construction, which could induce additional pressure on the urban land surface. However, most previous research on land surface deformation in the Xuzhou urban areas has been conducted based on traditional ground-based deformation monitoring techniques with sparse measurements. Little is known about the regional spatiotemporal behavior of land surface displacement in Xuzhou. In this study, a detailed interferometric synthetic aperture radar (InSAR) time series analysis was performed to characterize the spatial pattern and temporal evolution of land surface deformation in central areas of Xuzhou during 2015–2018. A method based on principal component analysis was adopted to correct artifacts in the InSAR signal. Results showed the correction strategy markedly reduced the discrepancy between global navigation satellite systems and InSAR measurements. Noticeable land subsidence (−5 to −41 mm/yr) was revealed widely within the Xuzhou urban areas, particularly along subway lines under construction, newly developed districts, and in old coal goafs. Remarkable consistent land uplift (up to +25 mm/yr) was found to have significantly affected two long narrow areas within the old goafs since 2015. The possible principal influencing factors contributing to the land surface displacements such as subway tunneling, building construction, mining, underground water levels and geological conditions are then discussed. [ABSTRACT FROM AUTHOR]

The OMI NO 2 standard product, OMNO2d, has been widely used in estimating surface NO 2 concentrations. The Peking University Ozone Monitoring Instrument NO 2 product (POMINO) is claimed to provide an improved quality over east-central China. This study estimated one year (Dec.2016–Nov.2017) of surface NO 2 concentrations at satellite overpass time based on OMNO2d data and POMINO data, respectively. We used an extra-trees (ET) regression model to convey the non-linear relationship between surface NO 2 and predictors, and compared the prediction accuracy with that of random forests (RF) regression model. The ET model showed a better estimation performance than the RF model, with the cross-validation R2 of 0.72 (RMSE = 9.20 μg/m3) and R2 of 0.70 (RMSE = 9.42 μg/m3) based on POMINO and OMNO2d data, respectively. The POMINO-derived monthly mean surface NO 2 concentrations were closer to ground NO 2 measurements than that OMNO2d-derived. Although the estimations from both satellite products were underestimated in polluted situations, the use of POMINO reduced the underestimation as compared to the use of OMNO2d data. • POMINO data with extra-trees model produces highest cross-validation R2 of 0.72 • POMINO increases surface NO 2 concentration compared with OMNO2d in polluted situation. • Monthly NO 2 from POMINO are consistent with ground sites, but OMNO2d underestimates in Jan and Oct. [ABSTRACT FROM AUTHOR]