大气加权平均温度模型在中国区域的精度分析

Accuracy Analysis of Atmospheric Weighted Mean Temperature Models in China's Mainland

大气加权平均温度(T_(m))是地基GNSS水汽探测的关键参数。基于2016-2018年ERA5再分析资料,利用严密积分精确确定了中国区域陆态网测站的T_(m)值;并对Bevis公式和GPT3全球经验模型在中国区域的精度展开了评估分析。结果表明,在中国区域,基于ERA5内插温度的Bevis公式能较好反映T_(m)周日变化,其精度总体稍优于基于ERA-Interim月均值构建的GPT3模型;两个模型计算T_(m)值的年均偏差分别为1.49 K和-1.88 K,年均RMS分别为4.10 K和4.28 K。T_(m)模型值的精度大致呈由东南区域(RMS小于4 K)向西部区域(RMS为4.5~8 K)逐渐降低的趋势;此外,在中国区域两种T_(m)模型的精度具有明显的季节性变化,夏秋季模型精度较高、春冬季模型精度较低。

Atmospheric weighted mean temperature(T_(m))is a key parameter for precipitable water retrieval by using ground-based global navigation satellite system(GNSS).In this study,we accurately determined T_(m) values for the sites of the Crustal Movement Observation Network of China based on the strict integration algorithm.Subsequently,we further assessed and analyzed the accuracy of T_(m) estimates derived by the Bevis formula and GPT3 model in China's Mainland.The results show that the Bevis formula with the interpolated temperature values from ERA5 surface data can better represent the diurnal variation of T_(m).Thus,its accuracy is slightly superior to the GPT3 model,which is based on the monthly ERA-interim reanalysis product.The T_(m) estimates from both models can achieve mean biases of 1.49 K and-1.88 K and RMS errors of 4.10 K and 4.28 K,respectively.The accuracy of T_(m) estimates for both models generally shows a decreased trend from the southeast regions to the western regions.Moreover,there is a clear seasonal variation for the accuracy of both models in China,showing as a higher accuracy in summer and autumn and lower accuracy in spring and winter.