GNSS大气加权平均温度经验模型精化方法的建立和分析
加权平均温度Tm作为对流层湿延迟转换为大气可降水量的关键参数，在GNSS气象学研究中发挥着重要作用。Tm经验模型的构建，可以通过将测站位置和时间信息作为输入参数快速获取Tm估值，但其精度往往受限，尤其在某些局部区域。本文提出了一种Tm经验模型精化方法，引入了地表气温数据，通过最小二乘快速获取精化系数，达到Tm的误差补偿作用。基于我国及邻近区域180个探空测站2011—2015年的数据，本文构建了基于GPT3的精化模型，并对其进行分析。数值结果表明，与Bevis模型、区域线性模型和GPT3模型相比，本文提出的精化模型估计Tm的精度分别提高了16.2%、13.5%和21.1%。另外，基于GPT3的...
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| Published in | Ce hui xue bao Vol. 51; no. 11; pp. 2339 - 2345 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | Chinese English |
| Published |
Beijing
Surveying and Mapping Press
01.11.2022
中国矿业大学(北京)地球科学与测绘工程学院,北京 100083%武汉大学测绘学院,湖北 武汉430079%国家基础地理信息中心,北京 100830 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-1595 1001-1595 |
| DOI | 10.11947/j.AGCS.2022.20210269 |
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| Abstract | 加权平均温度Tm作为对流层湿延迟转换为大气可降水量的关键参数，在GNSS气象学研究中发挥着重要作用。Tm经验模型的构建，可以通过将测站位置和时间信息作为输入参数快速获取Tm估值，但其精度往往受限，尤其在某些局部区域。本文提出了一种Tm经验模型精化方法，引入了地表气温数据，通过最小二乘快速获取精化系数，达到Tm的误差补偿作用。基于我国及邻近区域180个探空测站2011—2015年的数据，本文构建了基于GPT3的精化模型，并对其进行分析。数值结果表明，与Bevis模型、区域线性模型和GPT3模型相比，本文提出的精化模型估计Tm的精度分别提高了16.2%、13.5%和21.1%。另外，基于GPT3的精化模型估计Tm表现出最优的时空分布结果，显著提高了高纬度地区Tm估计精度，有效解决了GPT3模型只能表现Tm季节性变化的缺陷。本文方法计算公式简便，可以快速推广至任意Tm经验模型，具有较高的使用价值。 |
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| AbstractList | P228; 加权平均温度Tm作为对流层湿延迟转换为大气可降水量的关键参数,在GNSS气象学研究中发挥着重要作用.Tm经验模型的构建,可以通过将测站位置和时间信息作为输入参数快速获取Tm估值,但其精度往往受限,尤其在某些局部区域.本文提出了一种Tm经验模型精化方法,引入了地表气温数据,通过最小二乘快速获取精化系数,达到Tm的误差补偿作用.基于我国及邻近区域180个探空测站2011—2015年的数据,本文构建了基于GPT3的精化模型,并对其进行分析.数值结果表明,与Bevis模型、区域线性模型和GPT3模型相比,本文提出的精化模型估计Tm的精度分别提高了16.2％、13.5％和21.1％.另外,基于GPT3的精化模型估计Tm表现出最优的时空分布结果,显著提高了高纬度地区Tm估计精度,有效解决了G P T 3模型只能表现Tm季节性变化的缺陷.本文方法计算公式简便,可以快速推广至任意Tm经验模型,具有较高的使用价值. 加权平均温度Tm作为对流层湿延迟转换为大气可降水量的关键参数，在GNSS气象学研究中发挥着重要作用。Tm经验模型的构建，可以通过将测站位置和时间信息作为输入参数快速获取Tm估值，但其精度往往受限，尤其在某些局部区域。本文提出了一种Tm经验模型精化方法，引入了地表气温数据，通过最小二乘快速获取精化系数，达到Tm的误差补偿作用。基于我国及邻近区域180个探空测站2011—2015年的数据，本文构建了基于GPT3的精化模型，并对其进行分析。数值结果表明，与Bevis模型、区域线性模型和GPT3模型相比，本文提出的精化模型估计Tm的精度分别提高了16.2%、13.5%和21.1%。另外，基于GPT3的精化模型估计Tm表现出最优的时空分布结果，显著提高了高纬度地区Tm估计精度，有效解决了GPT3模型只能表现Tm季节性变化的缺陷。本文方法计算公式简便，可以快速推广至任意Tm经验模型，具有较高的使用价值。 |
| Author | 杨飞 郭际明 陈明 章迪 |
| AuthorAffiliation | 中国矿业大学(北京)地球科学与测绘工程学院,北京 100083%武汉大学测绘学院,湖北 武汉430079%国家基础地理信息中心,北京 100830 |
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| Author_FL | ZHANG Di CHEN Ming YANG Fei GUO Jiming |
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| Snippet | 加权平均温度Tm作为对流层湿延迟转换为大气可降水量的关键参数，在GNSS气象学研究中发挥着重要作用。Tm经验模型的构建，可以通过将测站位置和时间信息作为输入参数快速获... P228; 加权平均温度Tm作为对流层湿延迟转换为大气可降水量的关键参数,在GNSS气象学研究中发挥着重要作用.Tm经验模型的构建,可以通过将测站位置和时间信息作为输入参数快速... |
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| SubjectTerms | Accuracy Atmospheric models Empirical analysis Error compensation Mathematical models Meteorology Model accuracy Parameters Radiosondes Seasonal variations Spatial distribution Surface temperature Temporal distribution Water vapor |
| Title | GNSS大气加权平均温度经验模型精化方法的建立和分析 |
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