针对Global Pressure and Temperature2/Global Pressure and Temperature 2w(GPT2/2w)模型在亚洲区域对流层延迟估计中的适用性问题,该文基于GPT2/2w模型,结合Saastamoinen模型(分别用GPT2S、GPT2w-1S、GPT2w-5S表示)估计亚洲地区2007...针对Global Pressure and Temperature2/Global Pressure and Temperature 2w(GPT2/2w)模型在亚洲区域对流层延迟估计中的适用性问题,该文基于GPT2/2w模型,结合Saastamoinen模型(分别用GPT2S、GPT2w-1S、GPT2w-5S表示)估计亚洲地区2007—2017年10年的天顶对流层延迟(ZTD)并分析其精度与时空分布。使用欧洲定轨中心(CODE)的ZTD产品来验证模型在亚洲地区的精度。分析结果表明GPT2w-1S模型精度最高,偏差(Bias)为0.88 cm,均方根误差(RMSE)为4.63 cm,GPT2w-5S模型精度次之,GPT2S模型最差。受水汽分布影响,时间上,3种模型精度表现出季节特性,冬季精度最好,夏季精度最差;空间上,3种模型在高海拔地区精度较好,模型精度对纬度的依赖性不明显且纬度对3种模型的影响程度差别不大。展开更多
The tropospheric delay has a significant impact on high-accuracy positioning of the Global Navigation Satellite System(GNSS).Traditional solutions have their weaknesses.First,the estimation of tropospheric delay as a ...The tropospheric delay has a significant impact on high-accuracy positioning of the Global Navigation Satellite System(GNSS).Traditional solutions have their weaknesses.First,the estimation of tropospheric delay as a state parameter slows the positioning filter's convergence,especially critical for Precise Point Positioning(PPP).Second,correction-based approaches,including empirical model,meteorological model and GNSS network observations,have their corresponding limitations.The empirical model comprises yearly data-based statistics,which ignores high temporal-variation components,leading to decreased correction accuracy.The meteorological model requires real-time local weather observations.One can enable the network method of the expensive regional infrastructure of GNSS stations,of which performance depends on the rover-network geometry.In this study,we enable a real-time tropospheric regional correction service by polynomial coefficients from the Kalman filtering of multisource data,including the Global Pressure and Temperature 2 wet(GPT2w)model,weather observations from the National Oceanic and Atmospheric Administration(NOAA),and GNSS network observations.After discussing the weighting strategy examined by the regional dataset from Zhejiang Province,we evaluate the performance of the proposed fusion approach with post-processed PPP results as references.We obtained the optimal weightings for the corresponding dataset,and the average accuracy for Zenith Tropospheric Delay(ZTD)is 0.43,and 1.20 cm under static,active,and overall weather conditions,respectively.Compared with the real-time GNSS network ZTD solution,our proposed fusion solution is improved by 48.21%,55.20%,and 41.70%,respectively.In conclusion,the proposed approach makes the best of three traditional correction-based methods to provide optimized real-time tropospheric service.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
文摘针对Global Pressure and Temperature2/Global Pressure and Temperature 2w(GPT2/2w)模型在亚洲区域对流层延迟估计中的适用性问题,该文基于GPT2/2w模型,结合Saastamoinen模型(分别用GPT2S、GPT2w-1S、GPT2w-5S表示)估计亚洲地区2007—2017年10年的天顶对流层延迟(ZTD)并分析其精度与时空分布。使用欧洲定轨中心(CODE)的ZTD产品来验证模型在亚洲地区的精度。分析结果表明GPT2w-1S模型精度最高,偏差(Bias)为0.88 cm,均方根误差(RMSE)为4.63 cm,GPT2w-5S模型精度次之,GPT2S模型最差。受水汽分布影响,时间上,3种模型精度表现出季节特性,冬季精度最好,夏季精度最差;空间上,3种模型在高海拔地区精度较好,模型精度对纬度的依赖性不明显且纬度对3种模型的影响程度差别不大。
基金supported by the National Natural Science Foundation of China under[Grants 42004019 and 41874033].
文摘The tropospheric delay has a significant impact on high-accuracy positioning of the Global Navigation Satellite System(GNSS).Traditional solutions have their weaknesses.First,the estimation of tropospheric delay as a state parameter slows the positioning filter's convergence,especially critical for Precise Point Positioning(PPP).Second,correction-based approaches,including empirical model,meteorological model and GNSS network observations,have their corresponding limitations.The empirical model comprises yearly data-based statistics,which ignores high temporal-variation components,leading to decreased correction accuracy.The meteorological model requires real-time local weather observations.One can enable the network method of the expensive regional infrastructure of GNSS stations,of which performance depends on the rover-network geometry.In this study,we enable a real-time tropospheric regional correction service by polynomial coefficients from the Kalman filtering of multisource data,including the Global Pressure and Temperature 2 wet(GPT2w)model,weather observations from the National Oceanic and Atmospheric Administration(NOAA),and GNSS network observations.After discussing the weighting strategy examined by the regional dataset from Zhejiang Province,we evaluate the performance of the proposed fusion approach with post-processed PPP results as references.We obtained the optimal weightings for the corresponding dataset,and the average accuracy for Zenith Tropospheric Delay(ZTD)is 0.43,and 1.20 cm under static,active,and overall weather conditions,respectively.Compared with the real-time GNSS network ZTD solution,our proposed fusion solution is improved by 48.21%,55.20%,and 41.70%,respectively.In conclusion,the proposed approach makes the best of three traditional correction-based methods to provide optimized real-time tropospheric service.
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.
