Zenith tropospheric delay (ZTD) is one of the main error sources in space geodetic techniques, such as Global Positioning System (GPS) and satellite altimetry. To qualitatively and quantitatively determine the most suitable model for Antarctica, we analyze the accuracy and applicability of nine models (UNB3m, EGNOS, GPT2 + Saastamoinen, GPT2w + Saastamoinen, GPT3 + Saastamoinen, GPT2 + Hopfield, GPT2w + Hopfield, GPT3 + Hopfield, and IGGtropSH) in Antarctica using 8 years of GPS‐derived ZTD time series from 65 stations. The results show that the GPT2/2w/3 + SAAS models are better than the other six models, with a bias of 0.2, −0.22, and −0.29 cm and root mean square (RMS) of 2.33, 2.31, and 2.36 cm. Based on the decimeter bias and RMS, the UNB3m model and EGNOS model present the worst performance in Antarctica. There are regional characteristics of bias and RMS in the nine models. The GPT2/2w/3 + SAAS models have the smallest regional deviation, and the bias and RMS between subregions (Antarctic Peninsula, Amundsen Sea Embayment, Ross Ice Shelf, Inland area of West Antarctica, Filchner‐Ronne Ice Shelf, and coastal East Antarctica) are all at the 0.2 and 0.7 cm levels, respectively. The GPT2/2w/3 + HOP models have the largest regional deviation, with regional bias and RMS at the levels of 8 and 6 cm, respectively. Our results suggest that the uncertainty of ice sheet elevation derived from satellite altimetry may be partly caused by the spatial‐related bias and error in the ZTD corrections. The bias and RMS of six GPT combined models and the IGGtropSH model present limited seasonal changes, indicating that these models can simulate the seasonal characteristics of ZTD better. The time series of the bias and RMS values of the EGNOS and UNB3m models show obvious seasonal characteristics, which may contaminate the annual ice sheet elevation by approximately 5 cm if used as ZTD corrections.

中文翻译：

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南极不同天顶对流层延迟模型的时空特征评估

天顶对流层延迟（ZTD）是空间大地测量技术（例如全球定位系统（GPS）和卫星测高仪）中的主要误差源之一。为了定性和定量地确定最适合南极洲的模型，我们分析了九种模型（UNB3m，EGNOS，GPT2 + Saastamoinen，GPT2w + Saastamoinen，GPT3 + Saastamoinen，GPT2 + Hopfield，GPT2w + Hopfield，GPT3 + Hopfield，和IGGtropSH）在南极洲使用来自65个站点的8年GPS衍生的ZTD时间序列。结果表明，GPT2 / 2w / 3 + SAAS模型优于其他六个模型，偏差为0.2，-0.22和-0.29 cm，均方根（RMS）为2.33、2.31和2.36 cm。基于分米偏差和RMS，UNB3m模型和EGNOS模型在南极洲表现最差。在这九种模型中，存在偏差和均方根的区域特征。GPT2 / 2w / 3 + SAAS模型具有最小的区域偏差，并且在次区域（南极半岛，Amundsen海巢，Ross冰架，南极西部内陆地区，Filchner-Ronne冰架以及南极东部沿海地区）之间的偏差和RMS ）分别处于0.2厘米和0.7厘米的水平。GPT2 / 2w / 3 + HOP模型的区域偏差最大，区域偏差和RMS分别为8 cm和6 cm。我们的结果表明，来自卫星测高仪的冰盖高程的不确定性可能部分由ZTD校正中与空间相关的偏差和误差引起。六个GPT组合模型和IGGtropSH模型的偏差和RMS呈现有限的季节性变化，表明这些模型可以更好地模拟ZTD的季节特征。EGNOS和UNB3m模型的偏差和RMS值的时间序列显示出明显的季节性特征，如果用作ZTD修正值，则可能会将年度冰盖高度污染约5 cm。