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Yibin Yao
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

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Journal article
Published: 24 August 2021 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Potential evapotranspiration (PET) is a key parameter for calculating drought monitoring index that is generally difficult to obtain. In addition, PET has low spatial resolution and can only be obtained at a site-based point. Therefore, retrieving PET with high precision, high spatial resolution, and less meteorological data becomes the focus of this paper. In this paper, a high-precision and high-spatial-resolution drought monitoring (HDM) model was established to accurately calculate PET by using the zenith troposphere delay (ZTD) derived from global navigation satellite system (GNSS) and temperature (T). The initial PET value was calculated by using the PET periodical model based on PenmanMonteith (PM)-derived PET. The PET difference (DET) between the PM and periodic model was then calculated, and a multiple linear regression model was established to fit DET by using the ZTD and T differences at meteorological stations. To improve the spatial resolution of the calculated PET, a spherical harmonic function was applied to fit the coefficients of these stations. The HDM-derived PET at grid points was eventually obtained by using the fitted coefficients and ZTD/T. The HDM-derived PET and standardized precipitation evapotransporation index (SPEI) were compared with those from the Thornthwaite (TH) and revised TH (RTH) models over the loess plateau (LP) area with the PM-derived PET and SPEI as references. Comparison results highlight the excellent performance of the proposed HDM model and the Pearsons correlations of SPEI between the HDM and PM models all exceeded 0.96 under different month scales.

ACS Style

Qingzhi Zhao; Yongjie Ma; Zufeng Li; Yibin Yao. Retrieval of a high-precision drought monitoring index by using GNSS-derived ZTD and temperature. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, PP, 1 -1.

AMA Style

Qingzhi Zhao, Yongjie Ma, Zufeng Li, Yibin Yao. Retrieval of a high-precision drought monitoring index by using GNSS-derived ZTD and temperature. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; PP (99):1-1.

Chicago/Turabian Style

Qingzhi Zhao; Yongjie Ma; Zufeng Li; Yibin Yao. 2021. "Retrieval of a high-precision drought monitoring index by using GNSS-derived ZTD and temperature." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing PP, no. 99: 1-1.

Journal article
Published: 02 August 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Aerosol optical depth (AOD) is one of the basic parameters for determining the total aerosol content, and it exerts an important impact on regional environment pollution. To investigate the spatiotemporal variations of AOD, this study analyzes the relationship of AOD with precipitable water vapor (PWV) derived from a global navigation satellite system (GNSS) and meteorological parameters and proposes an adaptive AOD forecasting (AAF) model. In this model, the initial AOD value is determined using an empirical AOD model that considers annual periodicity, and the AOD difference is fitted using PWV, temperature (T), and surface pressure (P). In addition, this model also considers the time autocorrelation of the AOD difference; the model coefficients can be adaptively updated with training data. AOD data at 550 nm derived from the aerosol robotic network (AERONET), second modern-era retrospective analysis for research and applications (MERRA-2), and Copernicus atmosphere monitoring service (CAMS) for the Beijing-Tianjin-Hebei area are utilized to validate the proposed AAF model. Numerical results show that: 1) the accuracy of AOD derived from MERRA-2 is superior to that obtained from CAMS; 2) AOD is negatively correlated with P, is positively correlated with PWV and T, and has a high time autocorrelation with the AOD difference at consecutive times; and 3) the proposed AAF model demonstrates better performance than the traditional multiple linear regression (MLR) model. The average root mean square error (RMSE), mean absolute error (MAE), and bias of the AAF model are 0.17, 0.14, and -0.04, respectively, and those of the MLR model are 0.31, 0.25, and 0.06, respectively. These results reveal that the proposed AAF model can estimate AOD with high precision and has considerable potential for application in AAF research.

ACS Style

Qingzhi Zhao; Pengfei Yang; Wanqiang Yao; Yibin Yao. Adaptive AOD Forecast Model Based on GNSS-Derived PWV and Meteorological Parameters. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -10.

AMA Style

Qingzhi Zhao, Pengfei Yang, Wanqiang Yao, Yibin Yao. Adaptive AOD Forecast Model Based on GNSS-Derived PWV and Meteorological Parameters. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-10.

Chicago/Turabian Style

Qingzhi Zhao; Pengfei Yang; Wanqiang Yao; Yibin Yao. 2021. "Adaptive AOD Forecast Model Based on GNSS-Derived PWV and Meteorological Parameters." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-10.

Journal article
Published: 13 July 2021 in Remote Sensing
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GNSS attitude determination has been widely used in various navigation and positioning applications, due to its advantages of low cost and high efficiency. The navigation positioning and attitude determination modules in the consumer market mostly use low-cost receivers and face many problems such as large multipath effects, frequent cycle slips and even loss of locks. Ambiguity fixing is the key to GNSS attitude determination and will face more challenges in the complex urban environment. Based on the CLAMBDA algorithm, this paper proposes a CLAMBDA-search algorithm based on the multi-baseline GNSS model. This algorithm improves the existing CLAMBDA method through a fixed geometry constraint among baselines in the vehicle coordinate system. A fixed single-baseline solution reduces two degrees of freedom of vehicle rigid body, and a global minimization search for the ambiguity objective function in the other degree of freedom is conducted to calculate the baseline vector and its Euler angles. In addition, in order to make up for the shortcomings of short baseline ambiguity in complex environments, this paper proposes different validation strategies. Using three low-cost receivers (ublox M8T) and patch antennas, static and dynamic on-board experiments with different baseline length set-ups were carried out in different environments. Both the experiments prove that the method proposed in this paper has greatly improved the ambiguity fixing performance and also the Euler angle calculation accuracy, with an acceptable calculation burden. It is a practical vehicle-mounted attitude determination algorithm.

ACS Style

Xinzhe Wang; Yinbin Yao; Chaoqian Xu; Yinzhi Zhao; Huan Zhang. An Improved Single-Epoch Attitude Determination Method for Low-Cost Single-Frequency GNSS Receivers. Remote Sensing 2021, 13, 2746 .

AMA Style

Xinzhe Wang, Yinbin Yao, Chaoqian Xu, Yinzhi Zhao, Huan Zhang. An Improved Single-Epoch Attitude Determination Method for Low-Cost Single-Frequency GNSS Receivers. Remote Sensing. 2021; 13 (14):2746.

Chicago/Turabian Style

Xinzhe Wang; Yinbin Yao; Chaoqian Xu; Yinzhi Zhao; Huan Zhang. 2021. "An Improved Single-Epoch Attitude Determination Method for Low-Cost Single-Frequency GNSS Receivers." Remote Sensing 13, no. 14: 2746.

Journal article
Published: 05 July 2021 in Remote Sensing
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Accurate tropospheric delay (TD) and weighted mean temperature (Tm) are important for Global Navigation Satellite System (GNSS) positioning and GNSS meteorology. For this purpose, plenty of empirical models have been built to provide estimates of TD and Tm. However, these models cannot resolve TD and Tm variations at synoptic timescales since they only model the average annual, semi-annual, and/or daily variations. As a result, the existed empirical models cannot perform well under extreme weather conditions. To address this limitation, we propose to estimate Zenith Hydrostatic Delay (ZHD), Zenith Wet Delay (ZWD), and Tm directly from the stratified numerical weather forecasting products of the mesoscale version of the Global/Regional Assimilation and PrEdiction System (GRAPES_MESO) of China. The GRAPES_MESO forecasting data has a temporal resolution of 3 h, which provides the opportunity to resolve the synoptic variation. However, it is found that the estimated ZWD and Tm exhibit apparent systematic deviation from in situ observation-based estimates, which is due to the inherent biases in the GRAPES_MESO data. To solve this problem, we propose to correct these biases using a linear model and a spherical cap harmonic model. The estimates after correction are termed as the “CTropGrid” products. When validated by the radiosonde data, the CTropGrid product has biases of 1.5 mm, −0.7 mm, and −0.1 K, and Root Mean Square (RMS) error of 8.9 mm, 20.2 mm, and 1.5 K for ZHD, ZWD, and Tm. Compared to the widely used GPT2w model, the CTropGrid products have improved the accuracies of ZHD, ZWD, and Tm by 11.9%, 55.6%, and 60.5% in terms of RMS. When validating the Zenith Tropospheric Delay (ZTD) products (the sum of ZHD and ZWD) using the IGS ZTD data, the CTropGrid ZTD has a bias of −0.7 mm and an RMS of 35.8 mm, which is 22.7% better than the GPT2w model in terms of RMS. Besides the accuracy improvements, the CTropGrid products well model the synoptic-scale variations of ZHD, ZWD, and Tm. Compared to the existing empirical models that only capture the tidal (seasonal and/or diurnal) variations, the CTropGrid products capture well the non-tidal variations of ZHD, ZWD, and Tm, which enhances the tropospheric delay corrections and GNSS water vapor monitoring at synoptic timescales. Therefore, the CTropGrid product is an important progress in GNSS positioning and GNSS meteorology.

ACS Style

Liying Cao; Bao Zhang; Junyu Li; Yibin Yao; Lilong Liu; Qishun Ran; Zhaohui Xiong. A Regional Model for Predicting Tropospheric Delay and Weighted Mean Temperature in China Based on GRAPES_MESO Forecasting Products. Remote Sensing 2021, 13, 2644 .

AMA Style

Liying Cao, Bao Zhang, Junyu Li, Yibin Yao, Lilong Liu, Qishun Ran, Zhaohui Xiong. A Regional Model for Predicting Tropospheric Delay and Weighted Mean Temperature in China Based on GRAPES_MESO Forecasting Products. Remote Sensing. 2021; 13 (13):2644.

Chicago/Turabian Style

Liying Cao; Bao Zhang; Junyu Li; Yibin Yao; Lilong Liu; Qishun Ran; Zhaohui Xiong. 2021. "A Regional Model for Predicting Tropospheric Delay and Weighted Mean Temperature in China Based on GRAPES_MESO Forecasting Products." Remote Sensing 13, no. 13: 2644.

Journal article
Published: 14 May 2021 in Remote Sensing
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From the aspect of global drought monitoring, improving the regional drought monitoring method is becoming increasingly important for the sustainable development of regional agriculture and the economy. The standardized precipitation conversion index (SPCI) calculated by the Global Navigation Satellite System (GNSS) observation is a new means for drought monitoring that has the advantages of simple calculation and real-time monitoring. However, only SPCI with a 12-month scale has been verified on a global scale, while its capability and applicability for monitoring drought at a short time scale in regional areas have never been investigated. Therefore, this study aims to evaluate the performance of SPCI at other time scales in Yunnan, China, and propose an improved method for SPCI. The data of six GNSS stations were selected to calculate SPCI; the standardized precipitation evapotranspiration index (SPEI) and composite meteorological drought index (CI) are introduced to evaluate the SPCI at a short time scale in Yunnan Province. In addition, a modified CI (MCI) was proposed to calibrate the SPCI because of its large bias in Yunnan. Experimental results show that (1) SPCI exhibits better agreement with CI in Yunnan Province when compared to SPEI; (2) the capability of SPCI for drought monitoring is superior to that of SPEI in Yunnan; and (3) the improved SPCI is more suitable for drought monitoring in Yunnan, with a relative bias of 5.43% when compared to the MCI. These results provide a new means for regional drought monitoring in Yunnan, which is significant for dealing with drought disasters and formulating related disaster prevention and mitigation policies.

ACS Style

Xiongwei Ma; Yibin Yao; Qingzhi Zhao. Regional GNSS-Derived SPCI: Verification and Improvement in Yunnan, China. Remote Sensing 2021, 13, 1918 .

AMA Style

Xiongwei Ma, Yibin Yao, Qingzhi Zhao. Regional GNSS-Derived SPCI: Verification and Improvement in Yunnan, China. Remote Sensing. 2021; 13 (10):1918.

Chicago/Turabian Style

Xiongwei Ma; Yibin Yao; Qingzhi Zhao. 2021. "Regional GNSS-Derived SPCI: Verification and Improvement in Yunnan, China." Remote Sensing 13, no. 10: 1918.

Original paper
Published: 04 May 2021 in Theoretical and Applied Climatology
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Monsoon precipitation is the major driver of agricultural productivity in the Myanmar Coast; it is crucial to quantify and understand recent changes in precipitation during the monsoon season over this region. By using multiple precipitation datasets, we demonstrate that total precipitation during monsoon season over the Myanmar Coast has increased slightly but not significantly, but precipitation during the onset and withdrawal phases of monsoon season exhibit a significant increasing trend during 1979–2015, and the contribution of precipitation during the two phases to total monsoon precipitation has increased significantly. The increased precipitation during the onset phase over the Myanmar Coast directly results from the earlier onset of the South Asian Summer Monsoon in recent decades, which is associated with the phase transition of the Interdecadal Pacific Oscillation in the late 1990s. And the precipitation increase during the withdrawal phase is directly due to the enhances of the ascending motion and convection around this region, which is dynamically correlated to the anomalous cyclone-like circulation around the Bay of Bengal as well as the strengthening of the cross-equatorial flow around the equatorial Indian Ocean.

ACS Style

Xiao Yan; Yibin Yao; YuanJian Yang; Liang Zhang; Bao Zhang. Recent trends in precipitation over the Myanmar Coast during onset and withdrawal phases of monsoon season. Theoretical and Applied Climatology 2021, 145, 363 -376.

AMA Style

Xiao Yan, Yibin Yao, YuanJian Yang, Liang Zhang, Bao Zhang. Recent trends in precipitation over the Myanmar Coast during onset and withdrawal phases of monsoon season. Theoretical and Applied Climatology. 2021; 145 (1-2):363-376.

Chicago/Turabian Style

Xiao Yan; Yibin Yao; YuanJian Yang; Liang Zhang; Bao Zhang. 2021. "Recent trends in precipitation over the Myanmar Coast during onset and withdrawal phases of monsoon season." Theoretical and Applied Climatology 145, no. 1-2: 363-376.

Journal article
Published: 01 April 2021 in Journal of Hydrology
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Terrestrial evaporation is the central link of land surface energy balance, which is very important for climate change, water cycle research and drought monitoring. Potential evapotranspiration (ETP) is an important form of ET, which play a significance for calculating standardized precipitation evapotranspiration index (SPEI). This paper proposed a novel method of retrieving ETP using precipitable water vapor (PWV) and temperature to obtain the high precision and resolution ETP dataset, and the ETP and SPEI are obtained with the temporal-spatial resolutions of monthly and 0.125° × 0.125°, respectively, in China. The PWV derived from the European Center for Medium-range Weather Forecasts (ECMWF) is first validated and calibrated using the Global Navigation Satellite System (GNSS) technique. ETP and SPEI at specific stations are then calculated using the site-based revised Thornthwaith (S-RTH) model with high precision. Finally, spherical harmonic function is applied to fit the coefficients of the S-RTH model in China, and a RTH model over China (C-RTH) with high spatial resolution (0.125° × 0.125°) is established. Statistical result reveals that (1) the average root mean square (RMS) and mean absolute error (MAE) of the calibrated ECMWF-derived PWV have been improved from 2.0/1.7 mm to 1.7/1.4 mm, respectively using the GNSS-derived PWV. (2) The improvement rate of ETP derived from the S-RTH model is approximately 68% compared with that from the TH model, and the average RMS and MAE of the ETP difference between S-RTH and Penman–Monteith (PM) are 10.7 mm and 8.5 mm, respectively. (3) The average RMS and MAE of potential difference between C-RTH and PM are 17.6 mm and 13.7 mm, respectively. Although the accuracy of the C-RTH model is slightly lower than that of the S-RTH model, it is significantly improved compared with the TH model, and the calculated ETP data have been converted from point to surface. The proposed method expands the application of GNSS technique to obtain ETP data set, which provides the basic data guarantee for drought disaster prevention and control in China.

ACS Style

Xiongwei Ma; Qingzhi Zhao; Yibin Yao; Wanqiang Yao. A novel method of retrieving potential ET in China. Journal of Hydrology 2021, 598, 126271 .

AMA Style

Xiongwei Ma, Qingzhi Zhao, Yibin Yao, Wanqiang Yao. A novel method of retrieving potential ET in China. Journal of Hydrology. 2021; 598 ():126271.

Chicago/Turabian Style

Xiongwei Ma; Qingzhi Zhao; Yibin Yao; Wanqiang Yao. 2021. "A novel method of retrieving potential ET in China." Journal of Hydrology 598, no. : 126271.

Journal article
Published: 19 March 2021 in Sensors
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There are a large number of excellent research cases in Global Navigation Satellite System (GNSS) positioning and disaster prediction in Japan region, where the simulation and prediction of total electron content (TEC) is a powerful research method. In this study, we used the data of the GNSS Earth Observation Network (GEONET) established by the Geographical Survey Institute of Japan (GSI) to compare the performance of two regional ionospheric models in Japan, in which the spherical cap harmonic (SCH) model has the best performance. In this paper, we investigated the spatial and temporal variations of ionospheric TEC in Japan and their relationship with latitude, longitude, seasons, and solar activity. The results show that the TEC in Japan increases as the latitude decreases, with the highest average TEC in spring and summer and the lowest in winter, and has a strong correlation with solar activity. In addition, the observation and analysis of ionospheric disturbances over Japan before the 2016 Kumamoto earthquake and geomagnetic storms showed that GNSS observing of ionospheric TEC seems to be very effective in forecasting natural disasters and monitoring space weather.

ACS Style

Tianyang Hu; Yibin Yao; Jian Kong. Study of Spatial and Temporal Variations of Ionospheric Total Electron Content in Japan, during 2014–2019 and the 2016 Kumamoto Earthquake. Sensors 2021, 21, 2156 .

AMA Style

Tianyang Hu, Yibin Yao, Jian Kong. Study of Spatial and Temporal Variations of Ionospheric Total Electron Content in Japan, during 2014–2019 and the 2016 Kumamoto Earthquake. Sensors. 2021; 21 (6):2156.

Chicago/Turabian Style

Tianyang Hu; Yibin Yao; Jian Kong. 2021. "Study of Spatial and Temporal Variations of Ionospheric Total Electron Content in Japan, during 2014–2019 and the 2016 Kumamoto Earthquake." Sensors 21, no. 6: 2156.

Journal article
Published: 08 March 2021 in Remote Sensing
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As a crucial parameter in estimating precipitable water vapor from tropospheric delay, the weighted mean temperature (Tm ) plays an important role in Global Navigation Satellite System (GNSS)-based water vapor monitoring techniques. However, the rigorous calculation of Tm requires vertical profiles of temperature and water vapor pressure that are difficult to acquire in practice. As a result, empirical models are widely used but have limited accuracy. In this study, we use three machine learning methods, i.e., random forest (RF), backpropagation neural network (BPNN), and generalized regression neural network (GRNN), to improve the estimation of empirical Tm in China. The basic idea is to use the high-quality radiosonde observations estimated Tm to calibrate and optimize the empirical Tm through machine learning methods. Validating results show that the three machine learning methods improve the Tm accuracy by 37.2%, 32.6%, and 34.9% compared with the global pressure and temperature model 3 (GPT3). In addition to the overall accuracy improvement, the proposed methods also mitigate the accuracy variations in space and time, guaranteeing evenly high accuracy. This study provides a new idea to estimate Tm , which could potentially contribute to the GNSS meteorology.

ACS Style

Zhangyu Sun; Bao Zhang; Yibin Yao. Improving the Estimation of Weighted Mean Temperature in China Using Machine Learning Methods. Remote Sensing 2021, 13, 1016 .

AMA Style

Zhangyu Sun, Bao Zhang, Yibin Yao. Improving the Estimation of Weighted Mean Temperature in China Using Machine Learning Methods. Remote Sensing. 2021; 13 (5):1016.

Chicago/Turabian Style

Zhangyu Sun; Bao Zhang; Yibin Yao. 2021. "Improving the Estimation of Weighted Mean Temperature in China Using Machine Learning Methods." Remote Sensing 13, no. 5: 1016.

Original article
Published: 01 March 2021 in Journal of Geodesy
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Water vapor plays an important role in Earth’s weather and climate processes and energy transfer. Plenty of techniques have developed to monitor precipitable water vapor (PWV), but joint use of different techniques has some problems, including systematic biases, different spatiotemporal coverages and resolutions among different datasets. To address the above problems and improve the data utilization, we propose to use a generalized regression neural network (GRNN) to fuse PWVs from Global Navigation Satellite System (GNSS), Moderate-Resolution Imaging Spectroradiometer (MODIS), and European Centre for Medium‐Range Weather Forecasts Reanalysis 5 (ERA5). The core idea of this method is to use the high-quality GNSS PWV to calibrate and optimize the relatively low-quality MODIS and ERA5 PWV through the constructed GRNNs. Using the proposed method, we generated more than 400 PWV maps that combine GNSS, MODIS, and ERA5 PWVs in North America in 2018. Results show that the overall bias, standard deviation (STD), and root-mean-square (RMS) error are 0.0 mm, 2.1 mm, and 2.2 mm for the improved MODIS PWV, and 0.0 mm, 1.6 mm, and 1.6 mm for the improved ERA5 PWV. Compared to the original MODIS and ERA5 PWV, the total improvements are 37.1% and 15.8% in terms of RMS. The RMS improvements are mainly contributed from the calibration of bias for the MODIS PWV and optimization for the ERA5 PWV. It also demonstrates that the original MODIS PWV tends to be greater than the GNSS PWV while the ERA5 PWV has very small biases. After calibration and optimization, the correlation coefficients between the modified PWV and the GNSS PWV are 0.96 for the MODIS PWV and 0.98 for the ERA5 PWV. The proposed method also diminishes the temporal and spatial variations in accuracy, generating homogeneous PWV products. Since the biases among the three datasets are well removed and data accuracies are improved to the same level, they are thus easily fused and jointly used.

ACS Style

Bao Zhang; Yibin Yao. Precipitable water vapor fusion based on a generalized regression neural network. Journal of Geodesy 2021, 95, 1 -14.

AMA Style

Bao Zhang, Yibin Yao. Precipitable water vapor fusion based on a generalized regression neural network. Journal of Geodesy. 2021; 95 (3):1-14.

Chicago/Turabian Style

Bao Zhang; Yibin Yao. 2021. "Precipitable water vapor fusion based on a generalized regression neural network." Journal of Geodesy 95, no. 3: 1-14.

Journal article
Published: 23 February 2021 in Sensors
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Global navigation satellite system (GNSS) can provide dual-frequency observation data, which can be used to effectively calculate total electron content (TEC). Numerical studies have utilized GNSS-derived TEC to evaluate the accuracy of ionospheric empirical models, such as the International Reference Ionosphere model (IRI) and the NeQuick model. However, most studies have evaluated vertical TEC rather than slant TEC (STEC), which resulted in the introduction of projection error. Furthermore, since there are few GNSS observation stations available in the Antarctic region and most are concentrated in the Antarctic continent edge, it is difficult to evaluate modeling accuracy within the entire Antarctic range. Considering these problems, in this study, GNSS STEC was calculated using dual-frequency observation data from stations that almost covered the Antarctic continent. By comparison with GNSS STEC, the accuracy of IRI-2016 and NeQuick2 at different latitudes and different solar radiation was evaluated during 2016–2017. The numerical results showed the following. (1) Both IRI-2016 and NeQuick2 underestimated the STEC. Since IRI-2016 utilizes new models to represent the F2-peak height (hmF2) directly, the IRI-2016 STEC is closer to GNSS STEC than NeQuick2. This conclusion was also confirmed by the Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) occultation data. (2) The differences in STEC of the two models are both normally distributed, and the NeQuick2 STEC is systematically biased as solar radiation increases. (3) The root mean square error (RMSE) of the IRI-2016 STEC is smaller than that of the NeQuick2 model, and the RMSE of the two modeling STEC increases with solar radiation intensity. Since IRI-2016 relies on new hmF2 models, it is more stable than NeQuick2.

ACS Style

Zihuai Guo; Yibin Yao; Jian Kong; Gang Chen; Chen Zhou; Qi Zhang; Lulu Shan; Chen Liu. Accuracy Analysis of International Reference Ionosphere 2016 and NeQuick2 in the Antarctic. Sensors 2021, 21, 1551 .

AMA Style

Zihuai Guo, Yibin Yao, Jian Kong, Gang Chen, Chen Zhou, Qi Zhang, Lulu Shan, Chen Liu. Accuracy Analysis of International Reference Ionosphere 2016 and NeQuick2 in the Antarctic. Sensors. 2021; 21 (4):1551.

Chicago/Turabian Style

Zihuai Guo; Yibin Yao; Jian Kong; Gang Chen; Chen Zhou; Qi Zhang; Lulu Shan; Chen Liu. 2021. "Accuracy Analysis of International Reference Ionosphere 2016 and NeQuick2 in the Antarctic." Sensors 21, no. 4: 1551.

Short communication
Published: 09 February 2021 in Earth and Planetary Science Letters
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Interannual variability in the ice mass balance over the Antarctic Ice Sheet (AIS) is closely related to atmospheric circulation and has large impact on estimating the secular trends of mass change. However, the spatiotemporal patterns of the interannual mass balance over the AIS have not been well characterized and their connection with atmospheric circulation remains unclear. To address this limitation, we applied a statistical method to three sets of mass balance data and extracted the interannual mass change signals over the Antarctic Peninsula (AP), the West Antarctic Ice Sheet (WAIS), the East Antarctic Ice Sheet (EAIS), and the whole AIS from 2003 to 2017. Our results reveal that the interannual mass variations over the AP and the WAIS displayed similar temporal patterns, characterized by an increase in 2003-2008, a decrease in 2009-2013, and an increase again in 2014-2016 (relative to the mean of the interannual mass variations in 2003-2017). The interannual mass variations over the EAIS showed opposite patterns, characterized by a decrease in 2003-2008, an increase in 2009-2013, and a decrease again in 2014-2016. These temporal patterns generated a maximum value and a minimum value; the peak-to-valley mass change was −14 Gt for the AP and −129 Gt for the WAIS while the valley-to-peak mass change was 149 Gt for the EAIS. The entire AIS did not exhibit similar patterns to the WAIS or the EAIS but demonstrated oscillations of about three years. We find that the interannual variation in precipitation is the reason for the interannual variation of the mass balance over the AIS and was highly correlated with El Niño-Southern Oscillation (ENSO) in 2003-2017. The interannual precipitation was positively correlated with ENSO in the AP (correlation=0.8) and the WAIS (correlation=0.9) but negatively correlated in the EAIS (correlation=-0.8). We also uncover that ENSO largely modulated the atmospheric circulation over the AIS and its surrounding regions. In 2009-2013 when ENSO was in strong negative phase, precipitation decreased in the AP and the WAIS but increased in the EAIS, conversely, in 2014-2016 when ENSO was in strong positive phase, precipitation increased in the AP and the WAIS but decreased in the EAIS. Overall, the opposite behaviors of precipitation in the WAIS and the EAIS under strong ENSO conditions explained the spatiotemporal patterns of interannual ice mass variations over the AIS in 2003-2017. The findings of the anti-correlation between the ENSO and the precipitation in the EAIS and the opposite temporal patterns between the WAIS and the EAIS are particularly novel and adds new insights to cryosphere studies.

ACS Style

Bao Zhang; Yibin Yao; Lin Liu; YuanJian Yang. Interannual ice mass variations over the Antarctic ice sheet from 2003 to 2017 were linked to El Niño-Southern Oscillation. Earth and Planetary Science Letters 2021, 560, 116796 .

AMA Style

Bao Zhang, Yibin Yao, Lin Liu, YuanJian Yang. Interannual ice mass variations over the Antarctic ice sheet from 2003 to 2017 were linked to El Niño-Southern Oscillation. Earth and Planetary Science Letters. 2021; 560 ():116796.

Chicago/Turabian Style

Bao Zhang; Yibin Yao; Lin Liu; YuanJian Yang. 2021. "Interannual ice mass variations over the Antarctic ice sheet from 2003 to 2017 were linked to El Niño-Southern Oscillation." Earth and Planetary Science Letters 560, no. : 116796.

Journal article
Published: 09 February 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Previous studies on short-term rainfall forecast using precipitable water vapor (PWV) and meteorological parameters mainly focus on rain occurrence, while the rainfall forecast is rarely investigated. Therefore, an hourly rainfall forecast (HRF) model based on a supervised learning algorithm is proposed in this study to predict rainfall with high accuracy and time resolution. Hourly PWV derived from Global Navigation Satellite System (GNSS) and temperature data are used as input parameters of the HRF model, and a support vector machine is introduced to train the proposed model. In addition, this model also considers the time autocorrelation of rainfall in the previous epoch. Hourly PWV data of 21 GNSS stations and collocated meteorological parameters (temperature and rainfall) for five years in Taiwan Province are selected to validate the proposed model. Internal and external validation experiments have been performed under the cases of slight, moderate, and heavy rainfall. Average root-mean-square error (RMSE) and relative RMSE of the proposed HRF model are 1.36/1.39 mm/h and 1.00/0.67, respectively. In addition, the proposed HRF model is compared with the similar works in previous studies. Compared results reveal the satisfactory performance and superiority of the proposed HRF model in terms of time resolution and forecast accuracy.

ACS Style

Qingzhi Zhao; Yang Liu; Wanqiang Yao; Yibin Yao. Hourly Rainfall Forecast Model Using Supervised Learning Algorithm. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -9.

AMA Style

Qingzhi Zhao, Yang Liu, Wanqiang Yao, Yibin Yao. Hourly Rainfall Forecast Model Using Supervised Learning Algorithm. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-9.

Chicago/Turabian Style

Qingzhi Zhao; Yang Liu; Wanqiang Yao; Yibin Yao. 2021. "Hourly Rainfall Forecast Model Using Supervised Learning Algorithm." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-9.

Journal article
Published: 22 November 2020 in Space Weather
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Global ionospheric total electron content (TEC) maps are widely utilized in research regarding ionospheric physics and the associated space weather impacts, so there is a great interest in the community in short‐term ionosphere TEC forecasting. In this study, the long short‐term memory (LSTM) neural network (NN) is applied to forecast the 256 spherical harmonic (SH) coefficients that are traditionally used to construct global ionospheric maps (GIM). Multiple input data, including historical time series of the SH coefficients, solar extreme ultraviolet (EUV) flux, disturbance storm time (Dst) index, and hour of the day, are used in the developed LSTM NN model. Different combinations of the above parameters have been used in constructing the LSTM NN model, and it is found that the model using all four parameters performs the best. Then the best performing LSTM model is used to forecast the SH coefficients, and the global hourly TEC maps are reproduced using the 256 predicted SH coefficients. A comprehensive evaluation is carried out with respect to the CODE GIM TEC. Results show that the first/second hour TEC root mean square error (RMSE) is 1.27/2.20 TECU during storm time and 0.86/1.51 TECU during quiet time, so the developed model performs well during both quiet and storm times. Moreover, typical ionospheric structures, such as equatorial ionization anomaly (EIA) and storm‐enhanced density (SED), are well reproduced in the predicted TEC maps during storm time. The developed model also shows competitive performance in predicting global TEC when compared to the persistence model and two empirical models (IRI‐2016 and NeQuick‐2).

ACS Style

Lei Liu; Shasha Zou; Yibin Yao; Zihan Wang. Forecasting Global Ionospheric TEC Using Deep Learning Approach. Space Weather 2020, 18, 1 .

AMA Style

Lei Liu, Shasha Zou, Yibin Yao, Zihan Wang. Forecasting Global Ionospheric TEC Using Deep Learning Approach. Space Weather. 2020; 18 (11):1.

Chicago/Turabian Style

Lei Liu; Shasha Zou; Yibin Yao; Zihan Wang. 2020. "Forecasting Global Ionospheric TEC Using Deep Learning Approach." Space Weather 18, no. 11: 1.

Journal article
Published: 29 October 2020 in Journal of Geophysical Research: Space Physics
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The three‐dimensional computerized ionospheric tomography (3DCIT) technique is used to reconstruct the spatial distribution of storm enhanced density (SED) based on the global positioning system (GPS) total electron content (TEC) measurements over the North American area during the March 17, 2013 storm. The reconstruction results are carefully validated with observations from three ionosonde stations, the constellation observing system for meteorology, ionosphere, and climate (COSMIC) radio occultations and the Millstone Hill incoherent scatter radar (ISR). The electron density profiles from the 3DCIT reconstruction show a good agreement with the ionosonde and COSMIC electron density profiles. The 3DCIT‐derived electron density difference between the storm day of March 17 and the quiet day of March 16 also captures the similar SED plume signature that was observed by the Millstone Hill ISR. The 3DCIT reconstruction allows us for the first time to unveil the 3D configuration of the SED plume and its spatiotemporal evolution. It was found that the SED plume first appeared around 400 km, and then expanded downward to ~ 300 km as well as upward to ~ 500 km over the course of a 3‐hour period from 19 to 22 UT on March 17. Our study also showed that the density enhancement within the SED plume occurred mostly above the storm‐time F‐layer peak height.

ACS Style

Changzhi Zhai; Gang Lu; Yibin Yao; Wenbin Wang; Shunrong Zhang; Jing Liu; Wenjie Peng; Jian Kong; Jun Chen. 3‐D Tomographic Reconstruction of SED Plume During 17 March 2013 Storm. Journal of Geophysical Research: Space Physics 2020, 125, 1 .

AMA Style

Changzhi Zhai, Gang Lu, Yibin Yao, Wenbin Wang, Shunrong Zhang, Jing Liu, Wenjie Peng, Jian Kong, Jun Chen. 3‐D Tomographic Reconstruction of SED Plume During 17 March 2013 Storm. Journal of Geophysical Research: Space Physics. 2020; 125 (11):1.

Chicago/Turabian Style

Changzhi Zhai; Gang Lu; Yibin Yao; Wenbin Wang; Shunrong Zhang; Jing Liu; Wenjie Peng; Jian Kong; Jun Chen. 2020. "3‐D Tomographic Reconstruction of SED Plume During 17 March 2013 Storm." Journal of Geophysical Research: Space Physics 125, no. 11: 1.

Journal article
Published: 28 October 2020 in IEEE Transactions on Geoscience and Remote Sensing
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Computerized ionospheric tomography is an important technique for ionosphere investigation. However, it is an ill-posed problem owing to an insufficient amount of available data, because of which the distributions of ionospheric electron density (IED) cannot be reconstructed accurately. In light of this, the ordered subsets-constrained algebraic reconstruction technique (OS_CART) is developed here using vertical total electron content (VTEC) data to solve this problem, where the VTEC derived from the slant total electron content (STEC) of Global Navigation Satellite System (GNSS) signal paths is used to compensate for the lack of data provided by GNSS observations in inversion regions, and the OS_CART is also used to improve the spatial resolution and inversion efficiency. The proposed method was validated by conducting numerical experiments using GNSS and independent ionosonde data in both quiescent and disturbed ionospheric conditions. In contrast to classical methods of ionospheric tomography, the proposed method exhibited significantly higher reconstruction accuracy. While delivering a comparable accuracy to that of traditional methods in terms of self-consistency validation using STEC data and without overfitting, the proposed method yielded a more than 90% improvement over the self-consistency validation using VTEC data. In addition, a better daily description of the ionosphere was obtained using the proposed method, where an increase in the peak height and irregular changes to the IED, associated with variations in the number of epochs and the occurrence of magnetic storms, were observed. Overall, the results reveal that the proposed method is a useful tool for research on space weather.

ACS Style

Dunyong Zheng; Yibin Yao; Wenfeng Nie; Mengguang Liao; Ji Liang; Minsi Ao. Ordered Subsets-Constrained ART Algorithm for Ionospheric Tomography by Combining VTEC Data. IEEE Transactions on Geoscience and Remote Sensing 2020, PP, 1 -11.

AMA Style

Dunyong Zheng, Yibin Yao, Wenfeng Nie, Mengguang Liao, Ji Liang, Minsi Ao. Ordered Subsets-Constrained ART Algorithm for Ionospheric Tomography by Combining VTEC Data. IEEE Transactions on Geoscience and Remote Sensing. 2020; PP (99):1-11.

Chicago/Turabian Style

Dunyong Zheng; Yibin Yao; Wenfeng Nie; Mengguang Liao; Ji Liang; Minsi Ao. 2020. "Ordered Subsets-Constrained ART Algorithm for Ionospheric Tomography by Combining VTEC Data." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-11.

Original article
Published: 27 October 2020 in GPS Solutions
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Computerized ionospheric tomography (CIT) is an ill-posed inverse problem owing to insufficient data acquisition. Therefore, the ionospheric electron density (IED) distributions cannot be reconstructed accurately. Although many attempts have been made to deal with this issue, there is still a long way to go before it can be completely overcome. Specifically, the inverted IEDs of voxels without observational information show a strong dependence on initial values, which affects the overall accuracy of CIT. Taking this into account, a new three-dimensional CIT model is developed, based on a backpropagation neural network. The neural network model is trained using the characteristics and inverted IEDs of voxels with observational information, and then, the IEDs of voxels without observational information are predicted again. Careful validation of the proposed model is performed by conducting numerical experiments with GPS simulation and real data under both quiet and disturbed ionospheric conditions. Compared with the traditional non-neural network method in the simulation experiment, the proposed method offers improvements of 62.0 and 56.89% in root mean square error and the mean absolute error for those voxels without observational information, respectively, while it offers improvements of 30.98 and 26.67% for all voxels of the whole region. In the real data experiment, the IEDs of the control groups obtained by the proposed method are compared with the target IEDs for all periods. The result presents correlation coefficient greater than 0.96 between this predicted IEDs and the target IEDs for all periods, and this further certifies the feasibility of the proposed method. Additionally, the latitude–longitude maps and profiles of the ionospheric electron density also show that the ill-posedness problem has a significantly weaker effect for those voxels without observational information.

ACS Style

Dunyong Zheng; Yibin Yao; Wenfeng Nie; Nan Chu; Dongfang Lin; Minsi Ao. A new three-dimensional computerized ionospheric tomography model based on a neural network. GPS Solutions 2020, 25, 1 -17.

AMA Style

Dunyong Zheng, Yibin Yao, Wenfeng Nie, Nan Chu, Dongfang Lin, Minsi Ao. A new three-dimensional computerized ionospheric tomography model based on a neural network. GPS Solutions. 2020; 25 (1):1-17.

Chicago/Turabian Style

Dunyong Zheng; Yibin Yao; Wenfeng Nie; Nan Chu; Dongfang Lin; Minsi Ao. 2020. "A new three-dimensional computerized ionospheric tomography model based on a neural network." GPS Solutions 25, no. 1: 1-17.

Journal article
Published: 14 October 2020 in Remote Sensing
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Ionospheric delay is a crucial error source and determines the source of single-frequency precise point positioning (SF-PPP) accuracy. To meet the demands of real-time SF-PPP (RT-SF-PPP), several international global navigation satellite systems (GNSS) service (IGS) analysis centers provide real-time global ionospheric vertical total electron content (VTEC) products. However, the accuracy distribution of VTEC products is nonuniform. Proposing a refinement method is a convenient means to obtain a more accuracy and consistent VTEC product. In this study, we proposed a refinement method of a real-time ionospheric VTEC model for China and carried out experiments to validate the model effectiveness. First, based on the refinement method and the Centre National d’Études Spatiales (CNES) VTEC products, three refined real-time global ionospheric models (RRTGIMs) with one, three, and six stations in China were built via GNSS observations. Second, the slant total electron content (STEC) and Jason-3 VTEC were used as references to evaluate VTEC accuracy. Third, RT-SF-PPP was used to evaluate the accuracy in the positioning domain. Results showed that even if using only one station to refine the global ionospheric model, the refined model achieved a better performance than CNES and the Center for Orbit Determination in Europe (CODE). The refinement model with six stations was found to be the best of the three refinement models.

ACS Style

Yang Wang; Yibin Yao; Liang Zhang; Mingshan Fang. A Refinement Method of Real-Time Ionospheric Model for China. Remote Sensing 2020, 12, 3354 .

AMA Style

Yang Wang, Yibin Yao, Liang Zhang, Mingshan Fang. A Refinement Method of Real-Time Ionospheric Model for China. Remote Sensing. 2020; 12 (20):3354.

Chicago/Turabian Style

Yang Wang; Yibin Yao; Liang Zhang; Mingshan Fang. 2020. "A Refinement Method of Real-Time Ionospheric Model for China." Remote Sensing 12, no. 20: 3354.

Journal article
Published: 24 September 2020 in IEEE Transactions on Geoscience and Remote Sensing
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Global Navigation Satellite System (GNSS) ionospheric tomography is a typical ill-posed problem. Joint inversion with external observation data is one of the effective ways to mitigate the problem. In this article, by fusing 3-D multisource ionospheric data, and improving the stochastic model, an improved GNSS tomographic algorithm MFCIT [computerized ionospheric tomography (CIT) using mapping function] is presented. The accuracy of the algorithm is validated by selected data under different geomagnetic and solar conditions acquired in Europe. The results show that the estimated, statistically significant uncertainty for each of the layers is about 0.50-3.0TECU, with the largest absolute error within 6.0TECU. The advantage of the MFCIT is that it is based on the Kalman filter, which enables efficient near real-time 3-D monitoring of ionosphere. The temporal resolution can reach ~1 min level. Here, we apply the ionospheric tomography inversion to the magnetic storm on January 7, 2015, in the European region, and quantified the evolution of the storm. The results show that the difference of the core region between the MFCIT and CODE GIM is less than 1TECU. More importantly, during the initial phase of the storm, when the ionospheric disturbance is not evident in the single layer CODE GIM model, the MFCIT shows obvious positive disturbances in the upper ionosphere, although there is no disturbance in the F2 layer. The MFCIT further tracks the evolution of the magnetic storm that the ionospheric disturbance expands from the upper to the lower ionosphere layers, and at UT12:00, the disturbance continues to spread to the F2 layer.

ACS Style

Jian Kong; Lulu Shan; Chen Zhou; Yibin Yao; Jiachun An; Zemin Wang. An Improved Computerized Ionospheric Tomography Model Fusing 3-D Multisource Ionospheric Data Enabled Quantifying the Evolution of Magnetic Storm. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 3725 -3736.

AMA Style

Jian Kong, Lulu Shan, Chen Zhou, Yibin Yao, Jiachun An, Zemin Wang. An Improved Computerized Ionospheric Tomography Model Fusing 3-D Multisource Ionospheric Data Enabled Quantifying the Evolution of Magnetic Storm. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (5):3725-3736.

Chicago/Turabian Style

Jian Kong; Lulu Shan; Chen Zhou; Yibin Yao; Jiachun An; Zemin Wang. 2020. "An Improved Computerized Ionospheric Tomography Model Fusing 3-D Multisource Ionospheric Data Enabled Quantifying the Evolution of Magnetic Storm." IEEE Transactions on Geoscience and Remote Sensing 59, no. 5: 3725-3736.

Journal article
Published: 15 September 2020 in Remote Sensing
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Global navigation satellite system (GNSS) tomography can effectively sense the three-dimensional structure of tropospheric water vapor (WV) using the GNSS observations. Numerous studies have utilized a tomographic window to include more epochs of observations, which significantly increases the number of valid signals. However, considering the tomography grid limits, a massive number of valid signals inevitably exhibits linear dependence. This dependence makes it impossible to improve the rank score of the tomography coefficient matrix by blindly introducing a large number of valid rays. Furthermore, excessive valid signals may lead to a high condition number in the coefficient matrix (ill-condition problem), which causes unstable results using the GNSS-WV tomography. Considering these problems, we proposed an improved tomographic approach, which applies a refined tomographic window. It differs from the general tomographic window in that the window is refined to traverse the valid signals available 15 min before and after the target epoch while retaining only the linearly independent parts (characteristic signal). Compared to the conventional method, the proposed method can filter the characteristic signal, which increases the rank score of the coefficient matrix and improves the stability of the tomography model. In this paper, we used GNSS observations from the Hong Kong Satellite Positioning Reference Station Network (SatRef) to validate the performance of the proposed method over the day-of-year (DOY) periods of 130–132, 2019 and 146–148, 2019. The numerical results showed that, by using a refined tomographic window, the proposed method obtained superior WV products in comparison with that of the conventional method.

ACS Style

Yibin Yao; Chen Liu; Chaoqian Xu; Yu Tan; Mingshan Fang. A Refined Tomographic Window for GNSS-Derived Water Vapor Tomography. Remote Sensing 2020, 12, 2999 .

AMA Style

Yibin Yao, Chen Liu, Chaoqian Xu, Yu Tan, Mingshan Fang. A Refined Tomographic Window for GNSS-Derived Water Vapor Tomography. Remote Sensing. 2020; 12 (18):2999.

Chicago/Turabian Style

Yibin Yao; Chen Liu; Chaoqian Xu; Yu Tan; Mingshan Fang. 2020. "A Refined Tomographic Window for GNSS-Derived Water Vapor Tomography." Remote Sensing 12, no. 18: 2999.