GPT2w(global pressure and temperature 2wet)是目前应用较为广泛的对流层延迟经验模型之一,可提供气压、温度、水汽压等气象参数。为验证和分析GPT2w模型在南极地区的精度,本文利用分布在南极区域的探空站数据和中国第33次南极科考期...GPT2w(global pressure and temperature 2wet)是目前应用较为广泛的对流层延迟经验模型之一,可提供气压、温度、水汽压等气象参数。为验证和分析GPT2w模型在南极地区的精度,本文利用分布在南极区域的探空站数据和中国第33次南极科考期间的实测探空气球数据对模型气压、温度、水汽压参数进行分层精度检验。与探空站数据比较发现,在南极地区地面高度上,GPT2w模型精度较高,与全球其他区域精度较为一致;进一步通过对比1月和7月统计结果,发现Bias和RMS呈现出季节特性;同时发现模型在垂直方向存在较大误差,表现为随着高度的增加,精度随之下降并逐步趋于稳定。实测数据对比方面,首先利用ECMWF(European Centre for Medium-range Weather Forecasts)气压分层数据对实测数据的可靠性进行验证,结果显示,实测数据与ECMWF分层数据符合得较好;同时通过比对发现,GPT2w天内精度在地面高度上仍与月平均精度相当,但垂直方向随着高度的增加精度相比于暖季精度会有所下滑,说明未考虑日周期项变化对模型精度存在一定影响。用探空数据计算的对流层延迟(zenith tropospheric delay,ZTD)来分析GPT2w的计算精度,结果表明GPT2w在南极区域ZTD计算精度在厘米级,与全球其他位置计算精度相当。展开更多
全球温度气压湿度(global pressure and temperature 2 wet, GPT2w)模型常被用于计算某一位置的气温、加权平均温度、气压以及水汽压等各种气象参数,是目前公开的标称精度最高的对流层延迟经验模型。利用中国区域参与全球气象交换的86...全球温度气压湿度(global pressure and temperature 2 wet, GPT2w)模型常被用于计算某一位置的气温、加权平均温度、气压以及水汽压等各种气象参数,是目前公开的标称精度最高的对流层延迟经验模型。利用中国区域参与全球气象交换的86个测站2013—2015年的气象探空数据,对GPT2w得到的各种气象参数进行精度检验及分析。实验结果表明,气温平均偏差为1.31℃,均方根误差为3.62℃;加权平均温度的平均偏差为-1.58 K,均方根误差为4.07 K;气压和水汽压平均偏差的绝对值在1 hPa以内,其均方根误差分别为6.98hPa与3.04 hPa。利用2006—2015年的数据分析了不同纬度模型精度的周期性特征,结果表明,气温、加权平均温度、气压和水汽压的均方根误差均具有一定的年周期特性,且在不同的纬度区域其周期特性不同。总体而言,GPT2w模型在中国地区范围内具有较高的精度和稳定性。展开更多
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
利用地基实测气象资料分析了GPT2w(global pressure and temprature 2 wet)模型估算的气象数据的精度,并将GPT2w估算的气象要素结合地基GNSS测站观测资料进行了大气可降水量(precipitable water vapor,PWV)的反演,结果表明:就BJFS测站...利用地基实测气象资料分析了GPT2w(global pressure and temprature 2 wet)模型估算的气象数据的精度,并将GPT2w估算的气象要素结合地基GNSS测站观测资料进行了大气可降水量(precipitable water vapor,PWV)的反演,结果表明:就BJFS测站在夏秋之季而言,无论是日尺度还是小时尺度上,由GPT2w估算的气象要素来反演的PWV与利用地基实测气象要素来反演的PWV的均方根(root mean square,RMS)优于2 mm,且两种尺度上两者RMS的偏差为亚毫米级,这为地基GNSS测站气象数据缺失时反演PWV提供了一种参考思路。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
文摘GPT2w(global pressure and temperature 2wet)是目前应用较为广泛的对流层延迟经验模型之一,可提供气压、温度、水汽压等气象参数。为验证和分析GPT2w模型在南极地区的精度,本文利用分布在南极区域的探空站数据和中国第33次南极科考期间的实测探空气球数据对模型气压、温度、水汽压参数进行分层精度检验。与探空站数据比较发现,在南极地区地面高度上,GPT2w模型精度较高,与全球其他区域精度较为一致;进一步通过对比1月和7月统计结果,发现Bias和RMS呈现出季节特性;同时发现模型在垂直方向存在较大误差,表现为随着高度的增加,精度随之下降并逐步趋于稳定。实测数据对比方面,首先利用ECMWF(European Centre for Medium-range Weather Forecasts)气压分层数据对实测数据的可靠性进行验证,结果显示,实测数据与ECMWF分层数据符合得较好;同时通过比对发现,GPT2w天内精度在地面高度上仍与月平均精度相当,但垂直方向随着高度的增加精度相比于暖季精度会有所下滑,说明未考虑日周期项变化对模型精度存在一定影响。用探空数据计算的对流层延迟(zenith tropospheric delay,ZTD)来分析GPT2w的计算精度,结果表明GPT2w在南极区域ZTD计算精度在厘米级,与全球其他位置计算精度相当。
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。
文摘利用地基实测气象资料分析了GPT2w(global pressure and temprature 2 wet)模型估算的气象数据的精度,并将GPT2w估算的气象要素结合地基GNSS测站观测资料进行了大气可降水量(precipitable water vapor,PWV)的反演,结果表明:就BJFS测站在夏秋之季而言,无论是日尺度还是小时尺度上,由GPT2w估算的气象要素来反演的PWV与利用地基实测气象要素来反演的PWV的均方根(root mean square,RMS)优于2 mm,且两种尺度上两者RMS的偏差为亚毫米级,这为地基GNSS测站气象数据缺失时反演PWV提供了一种参考思路。
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.
