Zenith wet delay(ZWD)is a key parameter for the precise positioning of global navigation satellite systems(GNSS)and occupies a central role in meteorological research.Currently,most models only consider the periodic v...Zenith wet delay(ZWD)is a key parameter for the precise positioning of global navigation satellite systems(GNSS)and occupies a central role in meteorological research.Currently,most models only consider the periodic variability of the ZWD,neglecting the effect of nonlinear factors on the ZWD estimation.This oversight results in a limited capability to reflect the rapid fluctuations of the ZWD.To more accurately capture and predict complicated variations in ZWD,this paper developed the CRZWD model by a combination of the GPT3 model and random forests(RF)algorithm using 5-year atmospheric profiles from 70 radiosonde(RS)stations across China.Taking the external 25 test stations data as reference,the root mean square(RMS)of the CRZWD model is 29.95 mm.Compared with the GPT3 model and another model using backpropagation neural network(BPNN),the accuracy has improved by 24.7%and 15.9%,respectively.Notably,over 56%of the test stations exhibit an improvement of more than 20%in contrast to GPT3-ZWD.Further temporal and spatial characteristic analyses also demonstrate the significant accuracy and stability advantages of the CRZWD model,indicating the potential prospects for GNSS-based applications.展开更多
提出一种高精度的ZWD模型(tianjin_zwd,TZ)。TZ基于2016-2018年逐小时气压分层的ERA5,欧洲中尺度气象预报中心第五代再分析产品数据,采用BP神经网络建立。然后,根据2019年的ERA5产品导出的ZWD对TZ模型进行了验证。结果表明:相比GPT3模型...提出一种高精度的ZWD模型(tianjin_zwd,TZ)。TZ基于2016-2018年逐小时气压分层的ERA5,欧洲中尺度气象预报中心第五代再分析产品数据,采用BP神经网络建立。然后,根据2019年的ERA5产品导出的ZWD对TZ模型进行了验证。结果表明:相比GPT3模型,TZ模型可提供更贴近真值的ZWD估值;并且,其RMSE由5.0 cm (GPT3)降至4.5 cm,表明10%的精度提升。上述结果表明TZ模型实现了更优的预测性能,该模型的构建策略可为全国其他地区的ZWD建模提供借鉴。展开更多
基金supported by the National Natural Science Foundation of China[42030109,42074012]the Scientific Study Project for institutes of Higher Learning,Ministry of Education,Liaoning Province[LJKMZ20220673]+2 种基金the Project supported by the State Key Laboratory of Geodesy and Earths'Dynamics,Innovation Academy for Precision Measurement Science and Technology[SKLGED2023-3-2]Liaoning Revitalization Talent Program[XLYC2203162]Natural Science Foundation of Hebei Province in China[D2023402024].
文摘Zenith wet delay(ZWD)is a key parameter for the precise positioning of global navigation satellite systems(GNSS)and occupies a central role in meteorological research.Currently,most models only consider the periodic variability of the ZWD,neglecting the effect of nonlinear factors on the ZWD estimation.This oversight results in a limited capability to reflect the rapid fluctuations of the ZWD.To more accurately capture and predict complicated variations in ZWD,this paper developed the CRZWD model by a combination of the GPT3 model and random forests(RF)algorithm using 5-year atmospheric profiles from 70 radiosonde(RS)stations across China.Taking the external 25 test stations data as reference,the root mean square(RMS)of the CRZWD model is 29.95 mm.Compared with the GPT3 model and another model using backpropagation neural network(BPNN),the accuracy has improved by 24.7%and 15.9%,respectively.Notably,over 56%of the test stations exhibit an improvement of more than 20%in contrast to GPT3-ZWD.Further temporal and spatial characteristic analyses also demonstrate the significant accuracy and stability advantages of the CRZWD model,indicating the potential prospects for GNSS-based applications.
文摘提出一种高精度的ZWD模型(tianjin_zwd,TZ)。TZ基于2016-2018年逐小时气压分层的ERA5,欧洲中尺度气象预报中心第五代再分析产品数据,采用BP神经网络建立。然后,根据2019年的ERA5产品导出的ZWD对TZ模型进行了验证。结果表明:相比GPT3模型,TZ模型可提供更贴近真值的ZWD估值;并且,其RMSE由5.0 cm (GPT3)降至4.5 cm,表明10%的精度提升。上述结果表明TZ模型实现了更优的预测性能,该模型的构建策略可为全国其他地区的ZWD建模提供借鉴。
