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Qingzhi Zhao
College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China

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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: 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.

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 July 2020 in Journal of Atmospheric and Solar-Terrestrial Physics
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High-precision precipitable water vapor (PWV) can be obtained using the global navigation satellite system (GNSS). However, the spatial resolution of the GNSS-derived PWV is insufficient, particularly for areas without sufficient GNSS stations. Although other techniques, such as radiosonde sounding, can also be used to obtain PWV, generating PWV images with high precision and high spatial resolution remains difficult. Therefore, this research focuses on the generation of such PWV images. A hybrid PWV fusion (HPF) model is proposed, which combines the polynomial fitting and spherical harmonic functions using the GNSS-/radiosonde-derived PWV at specific stations and European Centre for Medium-range Weather Forecasting (ECMWF)-derived PWV at grid-based points. The initial HPF model value is calculated based on the global pressure and temperature model 2 wet (GPT2w) model. Additionally, the large PWV difference and small PWV residual are estimated using the polynomial fitting and spherical harmonic functions, respectively. An optimized method of determining the weightings for the different PWV sources is also applied in resolving the established HPF model. PWV derived from 254 GNSS stations of the Crustal Movement Observation Network of China (CMONOC), 78 radiosonde stations of the Integrated Global Radiosonde Archive Version 2 (IGRA2) data set, and 961 grid points of the ECMWF ERA-Interim data sets over China are used to perform the experiment. Numerical results verify the accuracy and reliability of the proposed HPF model with an average root mean square (RMS) of approximately 1.75 mm for the entire 2014 in China, and under 3 mm in southeast China and in the summer in the selected four regions. This result indicates the good performance of the proposed HPF model and meets the requirement of weather nowcasting.

ACS Style

Qingzhi Zhao; Zheng Du; Wanqiang Yao; Yibin Yao. Hybrid precipitable water vapor fusion model in China. Journal of Atmospheric and Solar-Terrestrial Physics 2020, 208, 105387 .

AMA Style

Qingzhi Zhao, Zheng Du, Wanqiang Yao, Yibin Yao. Hybrid precipitable water vapor fusion model in China. Journal of Atmospheric and Solar-Terrestrial Physics. 2020; 208 ():105387.

Chicago/Turabian Style

Qingzhi Zhao; Zheng Du; Wanqiang Yao; Yibin Yao. 2020. "Hybrid precipitable water vapor fusion model in China." Journal of Atmospheric and Solar-Terrestrial Physics 208, no. : 105387.

Journal article
Published: 22 June 2020 in Journal of Atmospheric and Solar-Terrestrial Physics
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Water vapor tomography technique is a research point in global navigation satellite system (GNSS) meteorology. However, GNSS tomography technique has an inherent issue of design matrix sparsity, which leads to the ill-posed problem in the inversion of normal equation. Singular value decomposition (SVD) method is commonly used to resolve this problem but performs poorly due to the large fluctuation in tomographic results caused by the small singular values. To overcome the above issue, this study proposes an improved ridge estimation (IRE) method, which only revises the relatively small singular values and retains the large singular values unchanged on the basis of the minimum deviation principle in the processing of regularization matrix. An adjustment coefficient is introduced to determine the node of small singular value with large influence on the tomographic result. Twelve GNSS stations from Hong Kong Satellite Positioning Reference Station Network (SatRef) were used to validate the performance of the proposed method over the period of day of year (Doy) 84–101, 2014. The established tomography model in SatRef was resolved using SVD and IRE methods, respectively. Statistical results show that the accuracy of average root mean square error (RMSE) of IRE method is improved by 16.7% compared with that of SVD method when the corresponding radiosonde is considered as the reference. Additionally, the comparisons of water vapor profile, relative error (RE) and the reconstructed SWV derived from IRE and SVD methods were also carried out and the numerical result indicates a good performance of the proposed IRE method. Such results verify that the applicability of proposed IRE method in this paper for GNSS water vapor tomography.

ACS Style

Qingzhi Zhao; Zufeng Li; Wanqiang Yao; Yibin Yao. An improved ridge estimation (IRE) method for troposphere water vapor tomography. Journal of Atmospheric and Solar-Terrestrial Physics 2020, 207, 105366 .

AMA Style

Qingzhi Zhao, Zufeng Li, Wanqiang Yao, Yibin Yao. An improved ridge estimation (IRE) method for troposphere water vapor tomography. Journal of Atmospheric and Solar-Terrestrial Physics. 2020; 207 ():105366.

Chicago/Turabian Style

Qingzhi Zhao; Zufeng Li; Wanqiang Yao; Yibin Yao. 2020. "An improved ridge estimation (IRE) method for troposphere water vapor tomography." Journal of Atmospheric and Solar-Terrestrial Physics 207, no. : 105366.

Journal article
Published: 08 June 2020 in Remote Sensing
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Global Navigation Satellite System (GNSS) tomography has developed into an efficient tool for sensing the high spatiotemporal variability of atmospheric water vapor. The integration of GNSS top signals and side rays for tropospheric tomography systems using a novel height factor model (HFM) is proposed and discussed in this paper. Within the HFM, the sectional slant wet delay (SWD) of inside signals (the part of the side signal inside the tomography area), which is considered a key factor for modeling side rays, is separated into isotropic and anisotropic components. Correspondingly, two height factors are defined to calculate the isotropic and anisotropic part of tropospheric delays in the HFM. In addition, the dynamic tomography top boundary is first analyzed and determined based on 30-year radiosonde data to reasonably divide signals into top and side rays. Four special experimental schemes based on different tomography regions of Hong Kong are performed to assess the proposed HFM method, the results of which show increases of 33.42% in the mean utilization of rays, as well as decreases of 0.46 g/m3 in the average root mean square error (RMSE), compared to the traditional approach, revealing the improvement of tomography solutions when the side signals are included in the modeling. Furthermore, compared with the existing correction model for modeling side rays, the water vapor profiles retrieved from the proposed improved model are closer to the radiosonde data, which highlights the advantages of the proposed HFM for optimizing the GNSS tomography model.

ACS Style

Wenyuan Zhang; Shubi Zhang; Nan Ding; Qingzhi Zhao. A Tropospheric Tomography Method with a Novel Height Factor Model Including Two Parts: Isotropic and Anisotropic Height Factors. Remote Sensing 2020, 12, 1848 .

AMA Style

Wenyuan Zhang, Shubi Zhang, Nan Ding, Qingzhi Zhao. A Tropospheric Tomography Method with a Novel Height Factor Model Including Two Parts: Isotropic and Anisotropic Height Factors. Remote Sensing. 2020; 12 (11):1848.

Chicago/Turabian Style

Wenyuan Zhang; Shubi Zhang; Nan Ding; Qingzhi Zhao. 2020. "A Tropospheric Tomography Method with a Novel Height Factor Model Including Two Parts: Isotropic and Anisotropic Height Factors." Remote Sensing 12, no. 11: 1848.

Original article
Published: 16 March 2020 in GPS Solutions
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The GNSS tropospheric tomography technique has been proven to be a powerful tool for three-dimensional water vapor reconstruction. In most previous studies, the signals leaving the side face of the tomography area are ignored as having invalid information, which wastes valuable observations and decreases signal coverage of the research area. To include the contribution of such signals to the final tomographic result, an improved tropospheric tomography approach, which makes the most use of GNSS observations by combining the data derived from the empirical Global Pressure and Temperature 2 wet model, is proposed. Compared to the conventional method, the proposed method can adaptively use the signals penetrating from the model’s side face to the tomographic model, which increases the number of voxels crossed by rays and improves the stability of the tomography model. Numerical results in Hong Kong over the period of day of year 124–150, 2013 show that the internal accuracy of the tomographic model based on the proposed method increases by 9.8% when compared to the conventional method. The RMS errors of the integrated water vapor derived from the proposed method are 4.1 and 4.6 mm, respectively, while the values derived from the conventional method are 5.0 and 5.4 mm, respectively, when compared to the radiosonde and European Centre for Medium-Range Weather Forecasts (ECMWF) products. In addition, compared to the conventional method, the accuracy of the water vapor density profile derived from the tomographic result of the proposed method has been enhanced by 25% and 12.5% when the radiosonde and ECMWF data are considered as the reference, respectively. Such results indicate a good performance of the proposed method for GNSS troposphere tomography.

ACS Style

Qingzhi Zhao; Wanqiang Yao; Yibin Yao; Xin Li. An improved GNSS tropospheric tomography method with the GPT2w model. GPS Solutions 2020, 24, 1 -13.

AMA Style

Qingzhi Zhao, Wanqiang Yao, Yibin Yao, Xin Li. An improved GNSS tropospheric tomography method with the GPT2w model. GPS Solutions. 2020; 24 (2):1-13.

Chicago/Turabian Style

Qingzhi Zhao; Wanqiang Yao; Yibin Yao; Xin Li. 2020. "An improved GNSS tropospheric tomography method with the GPT2w model." GPS Solutions 24, no. 2: 1-13.

Journal article
Published: 15 February 2020 in Remote Sensing
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Southeast China, a non-core region influenced by the El Niño–Southern Oscillation (ENSO), has been seldom investigated before. However, the occurrence of ENSO will affect the redistribution of precipitation and the temperature (T) spatial pattern on a global scale. This condition will further lead to flood or drought disasters in Southeast China. Therefore, the method of monitoring the occurrence of ENSO is important and is the focus of this paper. The spatiotemporal characteristics of precipitable water vapor (PWV) and T are first analyzed during ENSO using the empirical orthogonal function (EOF). The results showed that a high correlation spatiotemporal consistency exist between PWV and T. The response thresholds of PWV and T to ENSO are determined by moving the window correlation analysis (MWCA). If the sea surface temperature anomaly (SSTA) at the Niño 3.4 region exceeded the ranges of (−1.17°C, 1.04°C) and (−1.15°C, 1.09°C), it could cause the anomalous change of PWV and T in Southeast China. Multichannel singular spectral analysis (MSSA) is introduced to analyze the multi-type signals (tendency, period, and anomaly) of PWV and T over the period of 1979–2017. The results showed that the annual abnormal signal and envelope line fluctuation of PWV and T agreed well in most cases with the change in SSTA. Therefore, a standard PWV and T index (SPTI) is proposed on the basis of the results to monitor ENSO events. The PWV and T data derived from the grid-based European Center for Medium-Range Weather Forecasting (ECMWF) reanalysis products and GNSS/RS stations in Southeast China were used to validate the performance of the proposed SPTI. Experimental results revealed that the time series of average SPTI calculated in Southeast China corresponded well to that of SSTA with a correlation coefficient of 0.66 over the period of 1979–2017. The PWV values derived from the Global Navigation Satellite System (GNSS) and radiosonde data at two specific stations (WUHN and 45004) were also used to calculate the SPTI. The results showed that the correlation coefficients between SPTI and SSTA were 0.73 and 0.71, respectively. Such results indicate the capacity of the proposed SPTI to monitor the ENSO in Southeast China.

ACS Style

Qingzhi Zhao; Yang Liu; Wanqiang Yao; Xiongwei Ma; Yibin Yao. A Novel ENSO Monitoring Method using Precipitable Water Vapor and Temperature in Southeast China. Remote Sensing 2020, 12, 649 .

AMA Style

Qingzhi Zhao, Yang Liu, Wanqiang Yao, Xiongwei Ma, Yibin Yao. A Novel ENSO Monitoring Method using Precipitable Water Vapor and Temperature in Southeast China. Remote Sensing. 2020; 12 (4):649.

Chicago/Turabian Style

Qingzhi Zhao; Yang Liu; Wanqiang Yao; Xiongwei Ma; Yibin Yao. 2020. "A Novel ENSO Monitoring Method using Precipitable Water Vapor and Temperature in Southeast China." Remote Sensing 12, no. 4: 649.

Journal article
Published: 13 February 2020 in Remote Sensing
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Global Navigation Satellite System (GNSS) tomography is a popular method for measuring and modelling water vapor in the troposphere. Presently, most studies use a cuboid-shaped tomographic region in their modelling, which represents the modelling region for all measurement epochs. This region is defined by the distribution of the GNSS signals skywards from a network of ground based GNSS stations for all epochs of measurements. However, in reality at each epoch the shape of the GNSS tomographic region is more likely to be an inverted cone. Unfortunately, this fixed conic tomographic region does not properly reflect the fact that the GNSS signal changes quickly over time. Therefore a dynamic or adaptive tomographic region is better suited. In this study, a new approach that adjusts the GNSS tomographic model to adapt the size of the GNSS network is proposed, which referred to as The High Flexibility GNSS Tomography (HFGT). Test data from different numbers of the GNSS stations are used and the results from HFGT are compared against that of radiosonde data (RS) to assess the accuracy of the HFGT approach. The results showed that the new approach is feasible for different numbers of the GNSS stations when a sufficient and uniformed distribution of GNSS signals is used. This is a novel approach for GNSS tomography.

ACS Style

Yuchen Wang; Nan Ding; Yu Zhang; Long Li; Xiaoyan Yang; Qingzhi Zhao. A New Approach of the Global Navigation Satellite System Tomography for Any Size of GNSS Network. Remote Sensing 2020, 12, 617 .

AMA Style

Yuchen Wang, Nan Ding, Yu Zhang, Long Li, Xiaoyan Yang, Qingzhi Zhao. A New Approach of the Global Navigation Satellite System Tomography for Any Size of GNSS Network. Remote Sensing. 2020; 12 (4):617.

Chicago/Turabian Style

Yuchen Wang; Nan Ding; Yu Zhang; Long Li; Xiaoyan Yang; Qingzhi Zhao. 2020. "A New Approach of the Global Navigation Satellite System Tomography for Any Size of GNSS Network." Remote Sensing 12, no. 4: 617.

Journal article
Published: 03 February 2020 in IEEE Transactions on Geoscience and Remote Sensing
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Except for its known aspects of positioning, navigation, and timing (PNT), the Global Navigation Satellite System (GNSS) has extended its application to the rainfall forecasting. GNSS-derived zenith total delay (ZTD) or precipitable water vapor (PWV) has been used as a single factor to predict the occurrence of rainfall; however, the rainfall is highly correlated with myriad atmospheric parameters, which cannot be perfectly reflected by a single predictor. In this article, an improved rainfall forecasting model (IRFM) is proposed to forecast the rainfall. The IRFM considers five predictors: monthly PWV value, seasonal PWV/ZTD variations, and their first derivatives: it can forecast rainfall using a single predicator or an arbitrary combination of those predicators. The merit of IRFM is reducing the false forecasted rainfall (FFR) events and missed detected rainfall (MDR) events as much as possible while guaranteeing the true detected rainfall (TDR) events. An optimized selecting principle of predictors’ threshold has been determined using the percentile method. The test experiment has been performed using five GNSS stations derived from continuously operating reference system (CORS) network of Zhejiang province, China. The analysis reveals that the IRFM considering five predictors provides a better performance than that only using a single predictor or a combination of arbitrary predictors. The statistical result shows the average TDR value of more than 95%, FFR value of less than 30%, and MDR of less than 5%, respectively. Compared to the existing rainfall forecasting methods using ZTD or PWV, the IRFM reduces the FFR and MDR, respectively, with the lowest values, while the TDR value is the highest.

ACS Style

Qingzhi Zhao; Yang Liu; Xiongwei Ma; Wanqiang Yao; Yibin Yao; Xin Li. An Improved Rainfall Forecasting Model Based on GNSS Observations. IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 4891 -4900.

AMA Style

Qingzhi Zhao, Yang Liu, Xiongwei Ma, Wanqiang Yao, Yibin Yao, Xin Li. An Improved Rainfall Forecasting Model Based on GNSS Observations. IEEE Transactions on Geoscience and Remote Sensing. 2020; 58 (7):4891-4900.

Chicago/Turabian Style

Qingzhi Zhao; Yang Liu; Xiongwei Ma; Wanqiang Yao; Yibin Yao; Xin Li. 2020. "An Improved Rainfall Forecasting Model Based on GNSS Observations." IEEE Transactions on Geoscience and Remote Sensing 58, no. 7: 4891-4900.

Journal article
Published: 03 February 2020 in Applied Sciences
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Ecological restoration and climate change in the Loess Plateau region have become research hotspots. Climate change and anthropogenic activities have led to spatial–temporal pattern variations in vegetation and extreme climatic indices and meteorological factors. Therefore, obtaining a better understanding is necessary of the internal relations between vegetation and meteorological factors. In this paper, the interplay between vegetation index and various factors, including extreme climatic indices and meteorological factors, during a long-term time series is investigated using Mann–Kendall trend analysis, and Pearson correlation coefficient analysis. The mechanisms of interaction between vegetation growth and various factors in the Loess Plateau are then analyzed. Results reveal that (i) the rapid growth of vegetation during 2000–2015 has made a major contribution to the growth trend of the Loess Plateau in the past 33 years (1982–2015). During 2000–2015, the increase of vegetation may inhibit the increase of extreme warm index and the decrease of extreme cold index; (ii) a warm and dry climate developed with decreasing relative humidity and increasing temperature; (iii) the normalized vegetation index (NDVI) is strongly correlated with extreme climatic indices and meteorological factors, especially precipitable water vapor (PWV), with a correlation coefficient of 0.94; and (iv) the daily temperature range, diurnal temperature range and sunshine duration (SSD) exerted different time-delay effects on vegetation growth in the Loess Plateau. The above findings provide an essential theoretical basis for ecological policy formulation in the Loess Plateau.

ACS Style

Qingzhi Zhao; Xiongwei Ma; Liang Liang; Wanqiang Yao. Spatial–Temporal Variation Characteristics of Multiple Meteorological Variables and Vegetation over the Loess Plateau Region. Applied Sciences 2020, 10, 1000 .

AMA Style

Qingzhi Zhao, Xiongwei Ma, Liang Liang, Wanqiang Yao. Spatial–Temporal Variation Characteristics of Multiple Meteorological Variables and Vegetation over the Loess Plateau Region. Applied Sciences. 2020; 10 (3):1000.

Chicago/Turabian Style

Qingzhi Zhao; Xiongwei Ma; Liang Liang; Wanqiang Yao. 2020. "Spatial–Temporal Variation Characteristics of Multiple Meteorological Variables and Vegetation over the Loess Plateau Region." Applied Sciences 10, no. 3: 1000.

Journal article
Published: 28 January 2020 in Journal of Atmospheric and Solar-Terrestrial Physics
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Despite being a systematic error source in global navigation satellite system (GNSS) positioning and navigation, tropospheric delay is a key parameter in GNSS meteorology. Therefore, deriving a zenith tropospheric delay (ZTD) value as accurately as possible from an empirical model without using auxiliary data is a prerequisite for the high-precision application of GNSS positioning and navigation. To reach the goal above, this paper proposes an improved model to estimate the ZTD based on the Global Pressure and Temperature 2 wet (GPT2w) model, which is called the improved GPT2w (IGPT2w) model. The GPT2w-derived ZTD is first calculated as the initial value of the IGPT2w model, and the time series of ZTD residual can thus be obtained between the GNSS- and GPT2w-derived ZTDs over GNSS stations. Analysis of the long time series variation of ZTD residuals using the multichannel singular spectrum analysis method reveals evident periodic signals. The Lomb–Scargle method is then used to determine the specific values of these periodic signals, and different periods are identified at various GNSS stations. Therefore, a ZTD residual model that considers annual, semi-annual, and seasonal periods is established. The IGPT2w model, in which the ZTD value is obtained by combining the estimated ZTD residual and the GPT2w-derived ZTD, can be acquired. A total of 188 GNSS stations in China throughout 2015 to 2017 are selected to validate the IGPT2w model. In the model, the GNSS-derived ZTD is obtained using the GAMIT/GLOBK software, and the accuracy is validated using radiosonde data with root mean square and bias of 1.9 and 0.1 cm, respectively, at 33 collocated stations in China. Statistical results reveal that the accuracy of the IGPT2w-derived ZTD is improved by 13.7% compared with that of the GPT2w-derived ZTD when the GNSS-derived ZTD is regarded as the reference. Such result indicates that the proposed IGPT2w model outperforms the GPT2w model in China.

ACS Style

Zheng Du; Qingzhi Zhao; Wanqiang Yao; Yibin Yao. Improved GPT2w (IGPT2w) model for site specific zenith tropospheric delay estimation in China. Journal of Atmospheric and Solar-Terrestrial Physics 2020, 198, 105202 .

AMA Style

Zheng Du, Qingzhi Zhao, Wanqiang Yao, Yibin Yao. Improved GPT2w (IGPT2w) model for site specific zenith tropospheric delay estimation in China. Journal of Atmospheric and Solar-Terrestrial Physics. 2020; 198 ():105202.

Chicago/Turabian Style

Zheng Du; Qingzhi Zhao; Wanqiang Yao; Yibin Yao. 2020. "Improved GPT2w (IGPT2w) model for site specific zenith tropospheric delay estimation in China." Journal of Atmospheric and Solar-Terrestrial Physics 198, no. : 105202.

Journal article
Published: 31 December 2019 in Sensors
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The rapid variation of atmospheric water vapor is important for a regional hydrologic cycle and climate change. However, it is rarely investigated in China, due to the lack of a precipitable water vapor (PWV) dataset with high temporal resolution. Therefore, this study focuses on the generation of an hourly PWV dataset using Global Navigation Satellite System (GNSS) observations derived from the Crustal Movement Observation Network of China. The zenith total delay parameters estimated by GAMIT/GLOBK software are used and validated with an average root mean square (RMS) error of 4–5 mm. The pressure (P) and temperature (T) parameters used to calculate the zenith hydrostatic delay (ZHD) and weighted average temperature of atmospheric water vapor (Tm) are derived from the fifth-generation reanalysis dataset of the European Centre for Medium-Range Weather Forecasting (ECMWF ERA5) products. The values of P and T at the GNSS stations are obtained by interpolation in the horizontal and vertical directions using empirical formulas. Tm is calculated at the GNSS stations using the improved global pressure and temperature 2 wet (IGPT2w) model in China with an RMS of 3.32 K. The interpolated P and T are validated by interpolating the grid-based ERA5 data into radiosonde stations. The average RMS and bias of P and T in China are 2.71/−1.11 hPa and 1.88/−0.51 K, respectively. Therefore, the error in PWV with a theoretical RMS of 1.85 mm over the period of 2011–2017 in China can be obtained. Finally, the hourly PWV dataset in China is generated and the practical accuracy of the generated PWV dataset is validated using the corresponding AERONET and radiosonde data at specific stations. Numerical results reveal that the average RMS values of the PWV dataset in the four geographical regions of China are less than 3 mm. A case analysis of the PWV diurnal variations as a response to the EI Niño event of 2015–2016 is performed. Results indicate the capability of the hourly PWV dataset of monitoring the rapid water vapor changes in China.

ACS Style

Qingzhi Zhao; Pengfei Yang; Wanqiang Yao; Yibin Yao. Hourly PWV Dataset Derived from GNSS Observations in China. Sensors 2019, 20, 231 .

AMA Style

Qingzhi Zhao, Pengfei Yang, Wanqiang Yao, Yibin Yao. Hourly PWV Dataset Derived from GNSS Observations in China. Sensors. 2019; 20 (1):231.

Chicago/Turabian Style

Qingzhi Zhao; Pengfei Yang; Wanqiang Yao; Yibin Yao. 2019. "Hourly PWV Dataset Derived from GNSS Observations in China." Sensors 20, no. 1: 231.

Journal article
Published: 30 December 2019 in IEEE Access
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Considering the noncore region influenced by El Niño-Southern Oscillation (ENSO) events, China is hardly investigated in terms of the vegetation variation during the ENSO period.Therefore, this study focused on increasing knowledge of vegetation growth and variation during the ENSO period.The novelty of this paper is introduced the moving window correlation analysis method to determine the thresholds of vegetation response to sea surface temperature anomaly (SSTa) and southern oscillation index (SOI) and analyze the main reasons for such variations.China was divided into seven areas based on different weather conditions, and the variations in vegetation growth in various areas during the ENSO period were analyzed. The response of vegetation to ENSO events in China was analyzed from the perspectives of precipitable water vapor (PWV), temperature, and precipitation, thereby revealing the interplay of multi-factors on vegetation growth.The main conclusions include (1) a positive vegetation response to El Niño exists all over China with thresholds of SSTa ≥ 1.87°C and SOI ≤ –3.27hPa, whereas a negative response of vegetation variation to La Niña exists with thresholds of SSTa ≤ –1.05°C and SOI ≥ 1.7hPa; (2) the correlations (p < 0.05) of PWV to normalized difference vegetation index (NDVI) (PWV-NDVI), temperature-NDVI, and precipitation-NDVI reached 0.84, 0.86, and 0.68, respectively, and PWV, temperature, and precipitation were negatively/positively abnormal during El Niño/La Niña periods; (3) in coastal areas of Southeast China, correlations between NDVI and PWV/temperature/ precipitation are poor and the opposite anomalies of PWV/temperature/ precipitation existed when compared to other areas of China.

ACS Style

Qingzhi Zhao; Xiongwei Ma; Wanqiang Yao; Yang Liu; Yibin Yao. Anomaly Variation of Vegetation and Its Influencing Factors in Mainland China During ENSO Period. IEEE Access 2019, 8, 721 -734.

AMA Style

Qingzhi Zhao, Xiongwei Ma, Wanqiang Yao, Yang Liu, Yibin Yao. Anomaly Variation of Vegetation and Its Influencing Factors in Mainland China During ENSO Period. IEEE Access. 2019; 8 (99):721-734.

Chicago/Turabian Style

Qingzhi Zhao; Xiongwei Ma; Wanqiang Yao; Yang Liu; Yibin Yao. 2019. "Anomaly Variation of Vegetation and Its Influencing Factors in Mainland China During ENSO Period." IEEE Access 8, no. 99: 721-734.

Journal article
Published: 27 December 2019 in Remote Sensing
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Typhoons can be serious natural disasters for the sustainability and development of society. The development of a typhoon usually involves a pre-existing weather disturbance, warm tropical oceans, and a large amount of moisture. This implies that a large variation in the atmospheric water vapor over the path of a typhoon can be used to study the characteristics of the typhoon. This is the reason that the variation in precipitable water vapor (PWV) is often used to capture the signature of a typhoon in meteorology. This study investigates the usability of real-time PWV retrieved from global navigation satellite systems (GNSS) for typhoons’ characterizations, and especially, the following aspects were investigated: (1) The correlation between PWV and atmospheric parameters including pressure, temperature, precipitation, and wind speed; (2) water vapor transportation during a typhoon period; and (3) the correlation between the movement of a typhoon and the transportation of water vapor. The case study selected for this research was Super Typhoon Mangkhut that occurred in mid-September 2018 in Hong Kong. The PWV time series were obtained from a conversion of GNSS-derived zenith total delays (ZTDs) using observations at 10 stations selected from the Hong Kong GNSS continuously operating reference stations (CORS) network, which are also located along the path of the typhoon. The Bernese GNSS Software (ver. 5.2) was used to obtain the ZTDs; and the root mean square (RMS) of the differences between the GNSS-ZTDs and International GNSS Service post-processed ZTDs time series was less than 8 mm. The RMS of the differences between the GNSS-PWVs (i.e., the ZTDs converted PWVs) and radiosonde-derived PWVs (RS-PWVs) time series was less than 2 mm. The changes in PWV reflect the variation in wind speed during the typhoon period to a certain degree, and their correlation coefficient was 0.76, meaning a significant positive correlation. In addition, a new approach was proposed to estimate the direction and speed of a typhoon’s movement using the time difference of PWV arrival at different sites. The direction and speed estimated agreed well with the ones published by the China Meteorological Administration. These results suggest that GNSS-derived PWV has a great potential for the monitoring and even prediction of typhoon events, especially for near real-time warnings.

ACS Style

Qimin He; Kefei Zhang; Suqin Wu; Qingzhi Zhao; XiaoMing Wang; Zhen Shen; Longjiang Li; Moufeng Wan; Xiaoyang Liu. Real-Time GNSS-Derived PWV for Typhoon Characterizations: A Case Study for Super Typhoon Mangkhut in Hong Kong. Remote Sensing 2019, 12, 104 .

AMA Style

Qimin He, Kefei Zhang, Suqin Wu, Qingzhi Zhao, XiaoMing Wang, Zhen Shen, Longjiang Li, Moufeng Wan, Xiaoyang Liu. Real-Time GNSS-Derived PWV for Typhoon Characterizations: A Case Study for Super Typhoon Mangkhut in Hong Kong. Remote Sensing. 2019; 12 (1):104.

Chicago/Turabian Style

Qimin He; Kefei Zhang; Suqin Wu; Qingzhi Zhao; XiaoMing Wang; Zhen Shen; Longjiang Li; Moufeng Wan; Xiaoyang Liu. 2019. "Real-Time GNSS-Derived PWV for Typhoon Characterizations: A Case Study for Super Typhoon Mangkhut in Hong Kong." Remote Sensing 12, no. 1: 104.

Journal article
Published: 16 December 2019 in Sensors
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Standardized precipitation evapotranspiration index (SPEI) is an acknowledged drought monitoring index, and the evapotranspiration (ET) used to calculated SPEI is obtained based on the Thornthwaite (TH) model. However, the SPEI calculated based on the TH model is overestimated globally, whereas the more accurate ET derived from the Penman–Monteith (PM) model recommended by the Food and Agriculture Organization of the United Nations is unavailable due to the lack of a large amount of meteorological data at most places. Therefore, how to improve the accuracy of ET calculated by the TH model becomes the focus of this study. Here, a revised TH (RTH) model is proposed using the temperature (T) and precipitable water vapor (PWV) data. The T and PWV data are derived from the reanalysis data and the global navigation satellite system (GNSS) observation, respectively. The initial value of ET for the RTH model is calculated based on the TH model, and the time series of ET residual between the TH and PM models is then obtained. Analyzed results reveal that ET residual is highly correlated with PWV and T, and the correlate coefficient between PWV and ET is −0.66, while that between T and ET for cases of T larger or less than 0 °C are −0.54 and 0.59, respectively. Therefore, a linear model between ET residual and PWV/T is established, and the ET value of the RTH model can be obtained by combining the TH-derived ET and estimated ET residual. Finally, the SPEI calculated based on the RTH model can be obtained and compared with that derived using PM and TH models. Result in the Loess Plateau (LP) region reveals the good performance of the RTH-based SPEI when compared with the TH-based SPEI over the period of 1979–2016. A case analysis in April 2013 over the LP region also indicates the superiority of the RTH-based SPEI at 88 meteorological and 31 GNSS stations when the PM-based SPEI is considered as the reference.

ACS Style

Qingzhi Zhao; Xiongwei Ma; Wanqiang Yao; Yang Liu; Zheng Du; Pengfei Yang; Yibin Yao; Yao. Improved Drought Monitoring Index Using GNSS-Derived Precipitable Water Vapor over the Loess Plateau Area. Sensors 2019, 19, 5566 .

AMA Style

Qingzhi Zhao, Xiongwei Ma, Wanqiang Yao, Yang Liu, Zheng Du, Pengfei Yang, Yibin Yao, Yao. Improved Drought Monitoring Index Using GNSS-Derived Precipitable Water Vapor over the Loess Plateau Area. Sensors. 2019; 19 (24):5566.

Chicago/Turabian Style

Qingzhi Zhao; Xiongwei Ma; Wanqiang Yao; Yang Liu; Zheng Du; Pengfei Yang; Yibin Yao; Yao. 2019. "Improved Drought Monitoring Index Using GNSS-Derived Precipitable Water Vapor over the Loess Plateau Area." Sensors 19, no. 24: 5566.

Journal article
Published: 29 November 2019 in Remote Sensing
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Some seasonal natural floods can be attributed to typhoons that bring a large amount of atmospheric water vapor, and variations in atmospheric water vapor can be reflected in the precipitable water vapor (PWV). Therefore, monitoring typhoons based on the anomalous variations of the PWV is the focus of this paper. The anomalous variations of ERA5(fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting)-derived PWV with other atmospheric parameters related to typhoons, such as precipitation, pressure, and wind, were first analyzed during typhoon periods. After that, a typhoon-monitoring method with and without considering the typhoon’s acceleration was proposed according to the time of the maximum value of the PWV during the typhoon period in this paper. Corresponding experiments based on the measured and simulated data were performed to evaluate the proposed method. The experimental measurement of Typhoon Hato revealed that the velocity of the typhoon’s movement estimated by the proposed method was close to the observed value, and the maximum difference between the estimated and observed values was less than 3 km/h. A simulated experiment was also carried out in which the acceleration of the typhoon’s movement was also considered. The simulated results verified the reliability and feasibility of the proposed method. The estimated velocity and acceleration of the typhoon’s movement were almost equal to the true values under the cases of using different numbers of stations and selecting various typhoon locations. Such results obtained above indicate that the method proposed in this paper has a significant potential application value for typhoon monitoring.

ACS Style

Qingzhi Zhao; Xiongwei Ma; Wanqiang Yao; Yibin Yao. A New Typhoon-Monitoring Method Using Precipitation Water Vapor. Remote Sensing 2019, 11, 2845 .

AMA Style

Qingzhi Zhao, Xiongwei Ma, Wanqiang Yao, Yibin Yao. A New Typhoon-Monitoring Method Using Precipitation Water Vapor. Remote Sensing. 2019; 11 (23):2845.

Chicago/Turabian Style

Qingzhi Zhao; Xiongwei Ma; Wanqiang Yao; Yibin Yao. 2019. "A New Typhoon-Monitoring Method Using Precipitation Water Vapor." Remote Sensing 11, no. 23: 2845.