In Global Navigation Satellite (GNSS)-based positioning, a user within a region covered by a network of reference stations can take advantage of the network-estimated parameters. The use of State Space Representation (SSR) parameters as GNSS-augmentation is valuable for Network-Real Time Kinematic (N-RTK) positioning and enables the ambiguity resolution for Precise Point Positioning (PPP) in the so-called PPP-RTK. SSR atmospheric corrections, i.e. tropospheric and ionospheric delays, are commonly estimated for the approximate user position by interpolation from values estimated for the reference stations. Widely used techniques are Inverse Distance Weighted, Ordinary Kriging and Weighted Least Squares (WLS). In this work, we analyze the interpolation quality of such techniques during severe weather events and Traveling Ionospheric Disturbances (TID). Furthermore, we propose modified WLS methods taking advantage of the physical atmospheric behavior during such events. Here, we exploit the use of Numerical Weather Models for tropospheric horizontal gradients information, and estimated TID parameters like wavelength and direction of propagation. Firstly, the interpolation is assessed using simulations considering artificial and real network geometries. Secondly, the proposed techniques are evaluated in post-processing using real SSR parameters generated by network computation of GNSS measurements. As examples, two severe weather events in North Europe in 2017, and one TID event over Japan in 2019 have been analyzed. The interpolation of SSR tropospheric and ionospheric parameters is evaluated. Considering the reference station positions as rover locations, the application of the modified WLS approach reduces the root mean square error in up to 80$\%$ of the cases during sharp weather fluctuations. Also, the average error can be decreased in 64$\%$ of the cases during the TID event investigated. Improvements up to factors larger than two are observed. Furthermore, specific cases are isolated showing particular tropospheric variations where significant errors (e.g. larger than 1 cm) can be reduced up to 20$\%$ of the total amount. Finally, tropospheric and ionospheric messages are proposed to transmit to the user the information needed to implement the suggested interpolation properly.

在基于全球导航卫星 (GNSS) 的定位中，参考站网络覆盖区域内的用户可以利用网络估计参数。使用状态空间表示 (SSR) 参数作为 GNSS 增强对于网络实时运动学 (N-RTK) 定位很有价值，并且可以在所谓的 PPP-RTK 中实现精确点定位 (PPP) 的模糊度解决。SSR 大气校正，即对流层和电离层延迟，通常是通过从参考站的估计值中插值来估计近似用户位置的。广泛使用的技术是反距离加权、普通克里金法和加权最小二乘法 (WLS)。在这项工作中，我们分析了在恶劣天气事件和移动电离层干扰 (TID) 期间此类技术的插值质量。此外，我们提出了改进的 WLS 方法，利用此类事件期间的物理大气行为。在这里，我们利用数值天气模型来获取对流层水平梯度信息以及估计的 TID 参数（如波长和传播方向）。首先，使用考虑人工和真实网络几何形状的模拟来评估插值。其次，使用由 GNSS 测量的网络计算生成的真实 SSR 参数在后处理中评估所提出的技术。例如，分析了 2017 年北欧的两次恶劣天气事件和 2019 年日本的一次 TID 事件。对 SSR 对流层和电离层参数的插值进行了评估。将参考站位置视为流动站位置，改进的 WLS 方法的应用将均方根误差降低了多达 80$\%$天气剧烈波动期间的案例。此外，平均误差可以减少 64$\%$在 TID 事件期间调查的案件。观察到的改进高达大于 2 的因子。此外，具体案例是孤立的，显示特定的对流层变化，其中显着误差（例如大于 1 厘米）可以减少到 20$\%$的总金额。最后，提出了对流层和电离层消息，以向用户传输正确实施建议插值所需的信息。