Abstract. The assimilation of Global Navigation Satellite Systems (GNSS) data has been proven to have a positive impact on the weather forecasts. However, the impact is limited due to the fact that solely the Zenith Total Delays (ZTD) or Integrated Water Vapor (IWV) derived from the GPS satellite constellation are utilized. Assimilation of more advanced products, such as Slant Total Delays (STDs) from more satellite systems may lead to improved forecasts. This study shows a preparation step for the assimilation, i.e. the analysis of the multi-GNSS tropospheric advanced parameters: ZTDs, tropospheric gradients and STDs. Three solutions are taken into consideration: GPS-only, GPS/GLONASS (GR) and GPS/GLONASS/Galileo (GRE). The parameters are compared with two global Numerical Weather Models (NWM): European Centre for Medium Weather Forecast (ECMWF) ERA5 reanalysis and a forecast model ICON run by the German Weather Service. The results show that for ZTDs and horizontal gradients, all three GNSS solutions show similar level of agreement with the NWM data. For ZTDs, the agreement is better for the ERA5 model with biases of approx. 1.5 mm and standard deviations (SDs) of 7.7 mm than for ICON with biases of 3.2 mm and SDs of 10 mm. For tropospheric gradients, the agreement with both NWMs is very similar: the biases are negligible and SDs equal to approx. 0.4 mm. For the STDs, the GPS-only solution has an average bias w.r.t. ERA5 of 4.2 mm with SDs of 25.2 mm. The statistics are very slightly reduced for the GRE solution and further reduced to a bias of 3.5 mm with SDs of 24.5 mm for the Galileo-only observations. This study shows that all systems are of comparable quality. However, the advantage of combining more GNSS systems in the operational data assimilation is the geometry improvement by adding more observations, especially for low elevation angles.

中文翻译：

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在 GFZ Potsdam 实现可操作的多 GNSS 对流层产品

摘要。全球导航卫星系统 (GNSS) 数据的同化已被证明对天气预报具有积极影响。然而，由于仅使用了来自 GPS 卫星星座的天顶总延迟 ( ZTD ) 或综合水汽 ( IWV ) ，因此影响是有限的。同化更先进的产品，例如来自更多卫星系统的倾斜总延迟 ( STD )，可能会改进预测。本研究展示了同化的准备步骤，即多 GNSS 对流层高级参数的分析：ZTD s、对流层梯度和STDs。考虑了三种解决方案：仅 GPS、GPS/GLONASS (GR) 和 GPS/GLONASS/Galileo (GRE)。这些参数与两个全球数值天气模型 (NWM) 进行了比较：欧洲中等天气预报中心 (ECMWF) ERA5 再分析和由德国气象局运行的预测模型 ICON。结果表明，对于ZTD和水平梯度，所有三个 GNSS 解决方案都显示出与 NWM 数据的相似程度。对于ZTD，ERA5 模型的一致性更好，偏差约为 1.5 毫米，标准偏差 (SD) 为 7.7 毫米，而 ICON 的偏差为 3.2 毫米，标准差为 10 毫米。对于对流层梯度，与两个 NWM 的一致性非常相似：偏差可以忽略不计，SD 大约等于 0.4 毫米。对于性病s，仅 GPS 解决方案的平均偏差 wrt ERA5 为 4.2 mm，SD 为 25.2 mm。GRE 解决方案的统计数据略有减少，并进一步减少到 3.5 毫米的偏差，仅伽利略观测的 SD 为 24.5 毫米。这项研究表明，所有系统的质量都相当。然而，在业务数据同化中结合更多 GNSS 系统的优势是通过添加更多观测来改进几何结构，特别是对于低仰角。