东南大学学报（英文版）2021，Vol.37Issue(1)：84-90,7.DOI:10.3969/j.issn.1003-7985.2021.01.011
一种用于估计中国及毗邻区域加权平均温度的神经网络方法
A neural network method for estimating weighted mean temperature over China and adjacent areas
摘要
Abstract
To improve the applicability of the global pressure and temperature 2 wet(GPT2w)model in estimating the weighted mean temperature in China and adjacent areas,the error compensation technology based on the neural network was proposed,and a total of 374 800 meteorological profiles measured from 2006 to 2015 of 100 radiosonde stations distributed in China and adjacent areas were used to establish an enhanced empirical model for estimating the weighted mean temperature in this region.The data from 2016 to 2018 of the remaining 92 stations in this region was used to test the performance of the proposed model.Results show that the proposed model is about 14.9％better than the GPT2w model and about 7.6％better than the Bevis model with measured surface temperature in accuracy.The performance of the proposed model is significantly improved compared with the GPT2w model not only at different height ranges,but also in different months throughout the year.Moreover,the accuracy of the weighted mean temperature estimation is greatly improved in the northwestern region of China where the radiosonde stations are very rarely distributed.The proposed model shows a great application potential in the nationwide real-time ground-based global navigation satellite system(GNSS)water vapor remote sensing.关键词
加权平均温度/GPT2w模型/神经网络/误差补偿/GNSS气象学Key words
weighted mean temperature/GPT2w model/neural network/error compensation/GNSS meteorology分类
天文与地球科学引用本文复制引用
龙凤阳,胡伍生,董彦锋,余龙飞..一种用于估计中国及毗邻区域加权平均温度的神经网络方法[J].东南大学学报（英文版）,2021,37(1):84-90,7.基金项目
The National Natural Science Foundation of China(No.41574022),the Postgraduate Research&Practice Innovation Pro-gram of Jiangsu Province(No.KYCX17_0150). （No.41574022）
