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Global Navigation Satellite Systems (GNSS) tropospheric tomography is a commonly used technique for the reconstruction of three-dimensional water vapour field, and a priori vertical constraint models are required for water vapour density (WVD) determination which plays a critical role in the quality of tomographic results. However, generalised exponential models were routinely used for vertical constraints and limited research was carried out in the GNSS tomography by taking epoch-by-epoch variations into consideration. In this study, an adaptive four-dimensional (4-D) WVD model for the vertical constraints in GNSS tropospheric tomography was developed based on both ERA5 and surface meteorological data in Hong Kong for each month during the period of 2015–2019, and the back-propagation neural network technique was used to develop the fitting model. Then, the WVD model was used to obtain the WVD of adjacent voxels in the vertical direction to alleviate the mis-representation of the generalised exponential model. The newly developed WVD model used in GNSS tropospheric tomography was validated using GNSS data from the Hong Kong region in the year 2020 and two tomographic epochs (00:00–00:30 UTC and 12:00–12:30 UTC) were evaluated each day. For each topographic epoch, the WVDs of the tomographic voxels including radiosonde profile are evaluated (10 voxels over 10 height layers) using radiosonde data as the reference and the WVDs of all tomographic voxels are evaluated (300 voxels over 10 height layers) using ERA5 data as the reference. Results showed that when radiosonde/ERA5 data were utilized as the references, corresponding monthly mean values of the root mean square errors (RMSEs) in the entire year reduced from 1.97/1.94 g/m3 of the traditional tomographic method to 1.56/1.36 g/m3 of the new method which showed approximately 21/30% improvements. These results suggest a better performance of the tomographic approach using the new WVD model for the vertical constraints proposed by this study by taking epoch-by-epoch information. [ABSTRACT FROM AUTHOR]

Missing data in precipitable water vapor derived from global navigation satellite systems (GNSS-PWV) is commonly a large hurdle in climatical applications, since continuous PWV is an important prerequisite. Interpolation using principal component analysis (PCA) is typically used to resolve this problem. However, the popular PCA-based interpolating methods, e.g., rank-deficient least squares PCA (RDPCA) and data interpolating empirical orthogonal function (DINEOF), often lead to unsatisfactory results. This study analyzes the relationship between missing data and PCA-based interpolation results and proposes an improved interpolation-based RDPCA (IRDPCA) that can take into account the PWV derived from ERA5 (ERA-PWV) as an additional aid. Three key steps are involved in the IRDPCA: initially interpolating missing data, estimating principal components through a functional model and optimizing the interpolation through an iterative process. Using a 6-year GNSS-PWV over 26 stations and ERA-PWV in Yunnan, China, the performance of the IRDPCA is compared with the RDPCA and DINEOF using simulation experiments based on both homogeneous data (i.e., interpolating ERA-PWV using available ERA-PWV) and heterogeneous data (i.e., interpolating GNSS-PWV using ERA-PWV). In the case of using homogeneous data, the root mean square (RMS) values of the interpolation errors are 3.45, 1.18 and 1.17 mm for the RDPCA, DINEOF and IRDPCA, respectively; while the values are 3.50, 2.50 and 1.55 mm in the heterogeneous case. These results demonstrate the superior performance of the IRDPCA in both the heterogeneous and homogeneous cases. Moreover, these methods are also applied to the interpolation of the real GNSS-PWV. The RMS, absolute bias and correlation of the GNSS-PWV are calculated by comparison with ERA-PWV. The results reveal that the interpolated GNSS-PWV using the IRDPCA is not impacted by the systematic discrepancies in the ERA-PWV and agrees well with the original data. [ABSTRACT FROM AUTHOR]

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]

One of the main challenges of Global Navigation Satellite System (GNSS) tomography is in solving ill-conditioned system equations. Vertical constraint models are typically used in the solution procedure and play an important role in the quality of the GNSS tomography, in addition to helping resolve ill-posed problems in system equations. In this study, based on a water vapor (WV) parameter, namely IRPWV, a new vertical constraint model with six sets of coefficients for six different WV states was developed and tested throughout 2019 in the Hong Kong region with four tomographic schemes, which were carried out with the model and the traditional vertical constraint model using three different types of water vapor scale height parameters. Experimental results were numerically compared against their corresponding radiosonde-derived WV values. Compared with the tests that used the traditional model, our results showed that, first, for the daily relative error of WV density (WVD) less than 30%, the new model can lead to at least 10% and 49% improvement on average at the lower layers (below 3 km, except for the ground surface) and the upper layers (about 5–10 km), respectively. Second, the skill score of the monthly root-mean-square error (RMSE) of layered WVD above 10 accounted for about 83%, 87%, and 64%. Third, for the annual biases of layered WVD, the new model significantly decreased by 1.1–1.5 g/m3 at layers 2–3 (about 1 km), where all schemes showed the maximal bias value. Finally, for the annual RMSE of layered WVD, the new model at the lower (about 0.6–3 km) and upper layers improved by 13–42% and 5–47%, respectively. Overall, the new model performed better on GNSS tomography and significantly improved the accuracy of GNSS tomographic results, compared to the traditional model. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) is critical for the respiration, transpiration, and photosynthesis of crops. Color indices (CIs) and vegetation indices (VIs) extracted from unmanned aerial vehicle (UAV) imagery have been widely applied to the monitoring of the crop LAI. However, when the coverage of the crop canopy is large and only spectral data are used to monitor the LAI of the crop, the LAI tends to be underestimated. The canopy height model (CHM) data obtained from UAV-based point clouds can represent the height and canopy structure of the plant. However, few studies have been conducted on the use of the CHM data in the LAI modelling. Thus, in this study, the feasibility of combining the CHM data and CIs and VIs, respectively, to establish LAI fitting models for winter wheat in four growth stages was investigated, and the impact of image resolution on the extraction of remote sensing variables (the CHM data, CIs, and VIs) and on the accuracy of the LAI models was evaluated. Experiments for acquiring remote sensing images of wheat canopies during the four growth stages from the RGB and multispectral sensors carried by a UAV were carried out. The partial least squares regression (PLSR), random forest regression (RFR), and support vector machine regression (SVR) were used to develop the LAI fitting models. Results showed that the accuracy of the wheat LAI models can be improved in the entire growth stages by the use of the additional CHM data with the increment of 0.020–0.268 in R2 for three regression methods. In addition, the improvement from the Cis-based models was more noticeable than the Vis-based ones. Furthermore, the higher the spatial resolution of the CHM data, the better the improvement made by the use of the additional CHM data. This result provides valuable insights and references for UAV-based LAI monitoring. [ABSTRACT FROM AUTHOR]

Global navigation satellite systems (GNSS) has been applied to the sounding of precipitable water vapor (PWV) due to its high accuracy and high spatiotemporal resolutions. PWV obtained from GNSS (GNSS-PWV) can be used to investigate extreme weather phenomena, such as the formation mechanism and prediction of rainfalls. In the study, a new, improved model for rainfall forecasting was developed based on GNSS data and rainfall data for the 9-year period from 2010 to 2018 at 66 stations located in the USA. The new model included three prediction factors—PWV value, PWV increase, maximum hourly PWV increase. The two key tasks involved for the development of the model were the determination of the thresholds for each prediction factor and the selection of the optimal strategy for using the three prediction factors together. For determining the thresholds, both critical success index (CSI) and true skill statistic (TSS) were tested, and results showed that TSS outperformed CSI for all rainfall events tested. Then, various strategies by combining the three prediction factors together were also tested, and results indicated that the best forecast result was from the case that any two of the prediction factors were over their own thresholds. Finally, the new model was evaluated using the GNSS data for the 2-year period from 2019 to 2020 at the above mentioned 66 stations, and the probability of detection (POD) and false-alarms rate (FAR) were adopted to measure the model performances. Over the 66 stations, the POD values ranged from 73% to 97% with the mean of 87%, and the FARs ranged from 26% to 77% with the mean of 53%. Moreover, it was also found that both POD and FAR values were related to the region of the station; e.g., the results at the stations that are located in humid regions were better than the ones located in dry regions. All these results suggest the feasibility and good performance of using GNSS-PWV for forecasting rainfall. [ABSTRACT FROM AUTHOR]

Transition metal nitrides anchored on carbon as oxygen reduction catalysts are widely used in zinc-air batteries. However, the size of transition metal nitrides and the thickness of carbon substrate are often difficultly controlled. Herein, the CoN nanoparticle anchored on ultra-thin nitrogen-doped graphene electrocatalyst (CoN/UNG) was synthesized by the combination strategy of the coordination of 1, 10-phenanthroline with the intercalation effect of polyethylene imine. Polyethylene imine can intercalate into layers of graphene and block the heaping of graphene to achieve ultra-thin nanosheet structures. Lone-pair electrons on nitrogen atoms of 1, 10-phenanthroline can bond to Co2+ to form the Co−N coordination, then confine the aggregation of Co atoms. Finally, small-sized CoN nanoparticles were successfully anchored on ultra-thin nitrogen-doped graphene. The CoN/UNG has a half-wave potential of 0.87 V vs. RHE with excellent electrochemical ORR performance and high stability. The power density of CoN/UNG-based zinc-air battery is 149.3 mW cm−2; the specific capacity is 917.2 mAh g−1 and the cycle stability is 350 h. The new combination strategy is conducive to the rational design of small-sized transition metal nitrides anchored ultra-thin nitrogen-doped graphene catalysts to boost the application of zinc-air batteries. [Display omitted] • The addition of 1,10-phenanthroline reduces the agglomerate of CoN nanoparticles. • The introduced polyethylene imine achieves the ultra-thin graphene nanosheets. • The CoN/UNG has excellent ORR activity with half-wave potential of 0.87 V. • The CoN/UNG allows the zinc-air battery to stably operate for 350 h. [ABSTRACT FROM AUTHOR]