In global navigation satellite system (GNSS) meteorology, the weighted mean temperature (T_m) is a variable parameter in the conversion between zenith wet delay errors of GNSS and precipitable water vapor. The combined models of T_m, which are modeled with a combination of T_m seasonal variations and relationships between T_m and site meteorological measurements (mainly site measured temperature), have been proven to be of relatively higher accuracy. In this study, an improved combined model for T_m called the NN-II model was developed and is the second generation of the NN model. Similar to the NN model, NN-II is a combined model and is modeled by using the neural network model. The NN model was only designed for T_m estimates near the surface, while NN-II was designed for T_m estimates from the surface to almost the top of the troposphere. Compared with the NN model, the NN-II model shows some advanced features in terms of model design: modeled T_m data cover from the surface to almost the top of the troposphere, a more accurate seasonal T_m from the GTrop-T_m model is used, and the input variables are refined. Due to these refinements, the bias and RMSE of NN-II for global T_m from the surface to almost the top of the troposphere are 0.08 K and 3.34 K, respectively, and this new model shows 29.1% and 40.6% improved accuracies compared to those of the GTrop-T_m model and the NN model, respectively. The accuracy advantage is maintained over different heights of the troposphere on a global scale.

中文翻译：

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用于预测加权平均温度的第二代神经网络模型

在全球导航卫星系统（GNSS）气象学中，加权平均温度（T _m）是GNSS的天顶湿延迟误差与可沉淀水汽之间转换的可变参数。T _m的组合模型已被证明具有较高的准确性，该组合模型是结合T _m的季节性变化以及T _m与现场气象测量值（主要是现场测量的温度）之间的关系进行建模的。在这项研究中，T _m的改进组合模型开发了称为NN-II模型的模型，它是NN模型的第二代。与NN模型类似，NN-II是组合模型，并使用神经网络模型进行建模。该神经网络模型只设计为Ť_米表面附近的估计，而NN-II被设计用于Ť_米估计从表面到对流层的几乎顶部。与NN模型相比，NN-II模型的节目在模型设计方面的一些高级功能：建模Ť_米从表面到对流层的几乎顶端数据盖，更准确的季节性Ť_米从GTrop- Ť_米使用模型，并细化输入变量。由于进行了这些改进，NN-II对于从表面到对流层几乎顶部的整体T _m的偏差和RMSE分别为0.08 K和3.34 K，并且与相比，该新模型显示出提高了29.1％和40.6％的精度GTrop- T _m模型和NN模型的那些。在全球范围内，在对流层的不同高度上都保持了精度优势。