Abstract：We retrieve tropospheric delay data from 155 stations from 2014-2018 Crustal Movement Observation Network of China (CMONOC). We use the BP-Adaboost algorithm to integrate multiple weak neural network predictors into a strong one in order to establish a new tropospheric delay model without meteorological parameters. The accuracy of the BP-Adaboost model is evaluated using the tropospheric delay products of 141 CMONOC stations in 2019, 62 stations excluded in modeling and 86 radiosonde stations in China. The results show that the biases of the new model are 0.62 mm, -1.16 mm and 12.32 mm, and the root mean square errors are 25.30 mm, 26.72 mm and 46.29 mm, respectively, which are better than the common models without meteorological parameters. In addition, the BP-Adaboost model could achieve higher accuracy in inland areas or areas above 2 km above sea level, meeting the real-time tropospheric delay correction needs of Chinese satellite navigation users.
