基于探空数据的对流层延迟模型精度评估

采用IGRA提供的2017年81个无线电探空站的探空资料,对4种对流层延迟模型在中国区域的精度进行综合评估与分析。结果表明,GPT2w模型的性能要优于依赖气象参数的Saastamoinen模型及基于球谐函数的GZTD和UNB3m模型;GPT2w模型的偏差均值MB(mean bias)和均方根误差RMSE分别为-0.8 cm和4.1 cm,各测站的MB和RMSE分别处于-2~2 cm和1.3~7.9 cm之间。UNB3m模型在中国区域存在较大的MB和RMSE,模型的RMSE最大可达10.2 cm。4种模型的精度对测站纬度具有一致的敏感性,表现为随测站纬度的升高而降低;模型精度呈明显季节性变化,且不同模型对季节的敏感程度有所差异;对流层湿延迟难以精确建模导致模型精度在夏季(RMSE为6~9 cm)低于冬季(RMSE为2~2.5 cm)。

Evaluation on Precision of Tropospheric Delay Models Based on Radiosonde Data

Based on the radiosonde records at 81 sounding stations from integrated global radiosonde archive (IGRA) throughout 2017, we conduct a comprehensive evaluation and analysis of the precision of four tropospheric delay models over China. Results show that GPT2w model outperforms the other models, including the Saastamoinen model that is dependent on meteorological elements, GZTD, as well as UNB3m with the format of spherical harmonic function. Mean bias (MB) and root mean square error (RMSE) of GPT2w model are －0.8 cm and 4.1 cm, respectively, and they range from －2 to 2 cm, and from 1.3 to 7.9 cm at all sites, respectively. The UNB3m model has the largest MB and RMSE among the four models, with the maximal RMSE reaching 10.2 cm. In addition, the four models have consistent sensitivity to the latitude of the stations, presenting as decreasing accuracy with increasing latitudes. The precision of the models has a prominent seasonal variability, presenting as different seasonal sensitivities among the four models. The low precision in modeling of tropospheric wet delay causes the lower accuracy of the models in summer (RMSE: 6~9 cm), in contrast with that in winter (RMSE: 2~2.5 cm).