| GPT2模型用于SDCORS反演可降水汽精度评估 |
| |
| 引用本文： | 刘智敏,李斐,郭金运,李洋洋.GPT2模型用于SDCORS反演可降水汽精度评估[J].大地测量与地球动力学,2018,38(3):305-309. |
| |
| 作者姓名： | 刘智敏 李斐 郭金运 李洋洋 |
| |
| 作者单位： | 山东科技大学测绘科学与工程学院;海岛(礁)测绘技术国家测绘地理信息局重点实验室; |
| |
| 摘 要： | 利用山东区域及邻近探空站，分析GPT2模型估算气象参数（气温和气压）的精度，并将GPT2模型应用于SDCORS反演可降水汽中，分析评估其精度。研究表明，GPT2模型估算气温和气压的偏差均值分别为-1.61 ℃和0.53 Pa，标准差均值分别为2.84 ℃和4.42 Pa，均方根误差均值分别为3.27 ℃和4.49 Pa；GPT2模型估算的气象参数解算的SDCORS/PWV的偏差均值为1.22 mm，标准差均值为3.05 mm，均方根误差均值为3.46 mm，较GPT模型精度高，可靠性强。对于未配备气象传感器的CORS站，基于GPT2模型估算气温和气压，有助于利用区域CORS反演可降水汽，有效实现对大气可降水量的监测与预报。
|
Accuracy Analysis on GPS Precipitable Water Vapor Inversion Using GPT2 Models |
| |
| LIU Zhimin,LI Fei,GUO Jinyun,LI Yangyang.Accuracy Analysis on GPS Precipitable Water Vapor Inversion Using GPT2 Models[J].Journal of Geodesy and Geodynamics,2018,38(3):305-309. |
| |
| Authors: | LIU Zhimin LI Fei GUO Jinyun LI Yangyang |
| |
| Abstract: | The meteorological parameters (air temperature and air pressure) estimated by GPT2 are analyzed and assessed using adjacent radio stations in the area of Shandong province. Further, GPT2 model is applied to SDCORS precipitable water vapor inversion. The research shows that the average deviation of air temperature and air pressure estimated by the GPT2 model are -1.61 ℃ and 0.53 Pa, the average standard deviation is 2.84 ℃ and 4.42 Pa, and the mean root mean square error is 3.27 ℃ and 4.49 Pa, respectively. Additionally, the mean deviation of SDCORS/PWV is 1.22 mm, the mean value is 3.05 mm, the mean square error is 3.46 mm, which is higher than that of GPT model, and the reliability is strong. For CORS stations without meteorological sensors, based on the GPT2 model to estimate the temperature and pressure, it is helpful to use the regional CORS to invert the precipitable water vapor, and to further effectively monitor and forecast the atmospheric precipitable water. |
| |
| Keywords: | |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《大地测量与地球动力学》浏览原始摘要信息 |
| 点击此处可从《大地测量与地球动力学》下载免费的PDF全文 |
|
