【摘要】 为了提高全球温压湿模型(GPT2w)在估计中国及毗邻区域加权平均温度中的适用性,采用了基于神经网络的模型误差补偿技术,以分布在中国及毗邻区域的100个探空站2006—2015年的374 800条大气垂直廓线资料为数据源,建立了适用于该地区加权平均温度估计的增强模型.利用分布在该地区的其余92个探空站2016—2018年的数据测试模型性能.结果表明,该模型的精度比GPT2w模型提高了约14.9%,比基于实测气象参数的Bevis模型提高了约7.6%.该模型的性能无论是在各个高度区间,还是在不同季节都比GPT2w模型有明显改进,并且在探空站分布十分稀少的我国西北部地区,加权平均温度的估计精度也得到显著的改善.该模型在开展全国范围内的地基GNSS实时水汽反演中具有巨大的应用潜力.更多还原

【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 GPT2 w 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.更多还原