GPT2模型用于SDCORS反演可降水汽精度评估

摘要

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.%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3.05mm,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.