In this study, the Global Navigation Satellite System （GNSS） network of China is discussed, which can be used to monitor atmospheric precipitable water vapor （PWV）. By the end of 2013, the network had 952 GNSS sit...In this study, the Global Navigation Satellite System （GNSS） network of China is discussed, which can be used to monitor atmospheric precipitable water vapor （PWV）. By the end of 2013, the network had 952 GNSS sites, including 260 belonging to the Crustal Movement Observation Network of China （CMONOC） and 692 belonging to the China Meteorological Administration GNSS network （CMAGN）. Additionally, GNSS observation collecting and data processing procedures are presented and PWV data quality control methods are investigated. PWV levels as determined by GNSS and radiosonde are compared. The results show that GNSS estimates are generally in good agreement with measurements of radio- sondes and water vapor radiometers （WVR）. The PWV retrieved by the national GNSS network is used in weather forecasting, assimilation of data into numerical weather prediction models, the validation of PWV estimates by radiosonde, and plum rain monitoring. The network is also used to monitor the total ionospheric electron content.展开更多
To improve the applicability of the global pressure and temperature 2 wet(GPT2w)model in estimating the weighted mean temperature in China and adjacent areas,the error compensation technology based on the neural netwo...To improve the applicability of the global pressure and temperature 2 wet(GPT2w)model in estimating the weighted mean temperature in China and adjacent areas,the error compensation technology based on the neural network was proposed,and a total of 374800 meteorological profiles measured from 2006 to 2015 of 100 radiosonde stations distributed in China and adjacent areas were used to establish an enhanced empirical model for estimating the weighted mean temperature in this region.The data from 2016 to 2018 of the remaining 92 stations in this region was used to test the performance of the proposed model.Results show that the proposed model is about 14.9%better than the GPT2w model and about 7.6%better than the Bevis model with measured surface temperature in accuracy.The performance of the proposed model is significantly improved compared with the GPT2w model not only at different height ranges,but also in different months throughout the year.Moreover,the accuracy of the weighted mean temperature estimation is greatly improved in the northwestern region of China where the radiosonde stations are very rarely distributed.The proposed model shows a great application potential in the nationwide real-time ground-based global navigation satellite system(GNSS)water vapor remote sensing.展开更多
In recent years,the focus of tropospheric studies has evolved to GNSS meteorology and weather fore-casting.The Zenith Wet Delay(ZWD),which might be assembled to the Integrated Water Vapour(IWV),is an essential compone...In recent years,the focus of tropospheric studies has evolved to GNSS meteorology and weather fore-casting.The Zenith Wet Delay(ZWD),which might be assembled to the Integrated Water Vapour(IWV),is an essential component of the tropospheric delay.Acquiring predicted the ZWD with the required level of accuracy is crucial for weather forecasting.The scope of this study is to use the adaptive neural fuzzy inference system(ANFIS)to predict the ZWD for the following six-hour epoch based exclusively on the present the ZWD value.It was developed and verified using 505 geographically and internationally distributed stations which were used for training and testing from 2008 to 2019.It was assessed based on two criteria.First,the correlation coefficient(R)values were found to be more than 0.8 in 98%of the stations,including those with highest and lowest latitudes,and the remaining 2% of stations located in coastal areas.Second,the Root Mean Square Error(RMSE)values of the differences between the pre-dicted and the actual ZWD were considered to be the more important finding of the study.That is,99.21% of the 505 stations had the RMSE values equal to or less than 3 cm,with only 4 stations having the RMSE values higher(0.2 mm)than 3 cm.Since the results of this study achieved the required degree of ac-curacy from the predicted ZWD to be utilized in weather forecasting,they may also be beneficial for GNSS meteorology.展开更多
The ground-based Global Navigation Satellite System(GNSS)water vapor tomography is increasingly important in GNSS meteorology.As the multi-GNSS and more ground-based GNSS sites can be incorporated into the regional wa...The ground-based Global Navigation Satellite System(GNSS)water vapor tomography is increasingly important in GNSS meteorology.As the multi-GNSS and more ground-based GNSS sites can be incorporated into the regional water vapor tomographic model,determining the tomographic window and sampling rate is crucial for the modelling of the water vapor tomography.These two factors afect not only the number of available signal rays from the satellites,but also the number of tomographic voxels crossed by the signal rays.This study uses Hong Kong as the research area to explore the impact of 12 schemes with diferent tomographic window and sampling rate on the three water vapor tomography methods,including Least squares,Kalman fltering,and Multiplicative Algebraic Reconstruction Technique(MART).Numerical results show that the tomographic results with the three methods get better as the width of the tomographic window decreases and the sampling rate increases in these 12 schemes,and it is found that the Least squares method is most afected by the two factors,followed by Kalman fltering and MART methods.It is recommended to set a tomographic window width of 10 min and a sampling rate of 300 s in a GNSS water vapor tomographic experiment with dense GNSS site like Hong Kong.展开更多
基金financially supported by the Special Fund for Meteorological Scientific Research in the Public Interest(GYHY201406012)the National Natural Science Foundation of China(41275114)a construction fund for CMONOC
文摘In this study, the Global Navigation Satellite System （GNSS） network of China is discussed, which can be used to monitor atmospheric precipitable water vapor （PWV）. By the end of 2013, the network had 952 GNSS sites, including 260 belonging to the Crustal Movement Observation Network of China （CMONOC） and 692 belonging to the China Meteorological Administration GNSS network （CMAGN）. Additionally, GNSS observation collecting and data processing procedures are presented and PWV data quality control methods are investigated. PWV levels as determined by GNSS and radiosonde are compared. The results show that GNSS estimates are generally in good agreement with measurements of radio- sondes and water vapor radiometers （WVR）. The PWV retrieved by the national GNSS network is used in weather forecasting, assimilation of data into numerical weather prediction models, the validation of PWV estimates by radiosonde, and plum rain monitoring. The network is also used to monitor the total ionospheric electron content.
基金The National Natural Science Foundation of China(No.41574022)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX17_0150).
文摘To improve the applicability of the global pressure and temperature 2 wet(GPT2w)model in estimating the weighted mean temperature in China and adjacent areas,the error compensation technology based on the neural network was proposed,and a total of 374800 meteorological profiles measured from 2006 to 2015 of 100 radiosonde stations distributed in China and adjacent areas were used to establish an enhanced empirical model for estimating the weighted mean temperature in this region.The data from 2016 to 2018 of the remaining 92 stations in this region was used to test the performance of the proposed model.Results show that the proposed model is about 14.9%better than the GPT2w model and about 7.6%better than the Bevis model with measured surface temperature in accuracy.The performance of the proposed model is significantly improved compared with the GPT2w model not only at different height ranges,but also in different months throughout the year.Moreover,the accuracy of the weighted mean temperature estimation is greatly improved in the northwestern region of China where the radiosonde stations are very rarely distributed.The proposed model shows a great application potential in the nationwide real-time ground-based global navigation satellite system(GNSS)water vapor remote sensing.
文摘In recent years,the focus of tropospheric studies has evolved to GNSS meteorology and weather fore-casting.The Zenith Wet Delay(ZWD),which might be assembled to the Integrated Water Vapour(IWV),is an essential component of the tropospheric delay.Acquiring predicted the ZWD with the required level of accuracy is crucial for weather forecasting.The scope of this study is to use the adaptive neural fuzzy inference system(ANFIS)to predict the ZWD for the following six-hour epoch based exclusively on the present the ZWD value.It was developed and verified using 505 geographically and internationally distributed stations which were used for training and testing from 2008 to 2019.It was assessed based on two criteria.First,the correlation coefficient(R)values were found to be more than 0.8 in 98%of the stations,including those with highest and lowest latitudes,and the remaining 2% of stations located in coastal areas.Second,the Root Mean Square Error(RMSE)values of the differences between the pre-dicted and the actual ZWD were considered to be the more important finding of the study.That is,99.21% of the 505 stations had the RMSE values equal to or less than 3 cm,with only 4 stations having the RMSE values higher(0.2 mm)than 3 cm.Since the results of this study achieved the required degree of ac-curacy from the predicted ZWD to be utilized in weather forecasting,they may also be beneficial for GNSS meteorology.
基金Beijing Natural Science Foundation(No.8224093)China Postdoctoral Science Foundation(No.2021M703510)+4 种基金Fundamental Research Funds for the Central Universities(No.2021XJDC01)Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province(No.SCSF202109)Open Fund of State Key Laboratory of Geodesy and Earth’s Dynamics Innovation Academy for Precision Measurement Science and Technology(No.SKLGED2022-3-1)Beijing Key Laboratory of Urban Spatial Information Engineering(No.20220117)the National Natural Science Foundation of China(No.42204022).
文摘The ground-based Global Navigation Satellite System(GNSS)water vapor tomography is increasingly important in GNSS meteorology.As the multi-GNSS and more ground-based GNSS sites can be incorporated into the regional water vapor tomographic model,determining the tomographic window and sampling rate is crucial for the modelling of the water vapor tomography.These two factors afect not only the number of available signal rays from the satellites,but also the number of tomographic voxels crossed by the signal rays.This study uses Hong Kong as the research area to explore the impact of 12 schemes with diferent tomographic window and sampling rate on the three water vapor tomography methods,including Least squares,Kalman fltering,and Multiplicative Algebraic Reconstruction Technique(MART).Numerical results show that the tomographic results with the three methods get better as the width of the tomographic window decreases and the sampling rate increases in these 12 schemes,and it is found that the Least squares method is most afected by the two factors,followed by Kalman fltering and MART methods.It is recommended to set a tomographic window width of 10 min and a sampling rate of 300 s in a GNSS water vapor tomographic experiment with dense GNSS site like Hong Kong.
