Precipitable water vapor (PWV), despite being the key factor for assessing climate change over the Tibetan Plateau (TP), is undersampled in this region due to the complex terrain and climate conditions therein. The fifth‐generation European Center for Medium‐Range Weather Forecasts Reanalysis (ERA5) and Second Modern‐Era Retrospective Analysis for Research and Applications (MERRA‐2) data sets are the two newly released global reanalyzes that provide hourly PWV products with a high spatiotemporal resolution for water vapor research. The accuracies of these two PWV products on the TP require a comprehensive and cautious assessment. In this study, the hourly PWV values derived from ERA5 and MERRA‐2 are systematically assessed by a comparison with 33 Global Navigation Satellite System (GNSS) stations from 2017 to 2018 over the TP. To improve the reliability of this evaluation, an enhanced T_m model and a PWV vertical correction model, namely, TP‐T_m and TP‐PWVC, respectively, are developed, and both models show better performance when compared against other widely used models. The accuracies of the ERA5‐ and MERRA‐2‐derived PWV at the 33 GNSS stations are very high; ERA5 has the smallest mean bias and root mean square (RMS) error of −0.08 and 1.77 mm, respectively, compared to those of 0.53 and 2.12 mm for MERRA‐2. In addition, the biases and RMS errors show significant regional and seasonal differences. The performance of ERA5 is slightly better than MERRA‐2, mainly due to its higher spatial resolution. The spatial distributions of the PWV derived from the two reanalyzes are consistent with the GNSS‐derived PWV distribution. The diurnal PWV variations derived from ERA5 are in good agreement with the diurnal variation of the GNSS‐derived PWV, whereas significant differences are observed for MERRA‐2 at several stations. These results indicate that ERA5 can provide more reliable hourly PWV estimates over the TP, which can help users further understand the strengths and weaknesses of these two PWV products and promote climate change research.

中文翻译：

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使用两个增强模型对地面GNSS观测值评估青藏高原ERA5和MERRA-2每小时的PWV产品

尽管可降水量的水汽（PWV）是评估青藏高原（TP）气候变化的关键因素，但由于该地区的地形和气候条件复杂，因此该地区的采样率较低。第五代欧洲中程天气预报重新分析中心（ERA5）和第二代研究和应用的现代时代回顾分析（MERRA-2）数据集是两个最新发布的全球重新分析，它们为时空PWV产品提供了较高的时空分布。水蒸气研究的分辨率。TP上这两种PWV产品的准确性需要进行全面而谨慎的评估。在这项研究中，通过与2017年至2018年在TP上与33个全球导航卫星系统（GNSS）台站进行比较，系统地评估了从ERA5和MERRA-2得出的每小时PWV值。T _m模型和PWV垂直校正模型，即TP -T _m分别开发了TP-PWVC和TP-PWVC，与其他广泛使用的模型相比，这两个模型均显示出更好的性能。33个GNSS站的ERA5和MERRA-2衍生的PWV的准确性很高。ERA5的平均偏差和均方根（RMS）误差最小，分别为-0.08和1.77 mm，而MERRA-2的均方根误差和均方根误差为0.53和2.12 mm。此外，偏差和RMS误差显示出明显的区域和季节差异。ERA5的性能略高于MERRA-2，这主要是由于其较高的空间分辨率。两次重新分析得出的PWV的空间分布与GNSS得出的PWV分布一致。ERA5衍生的PWV的昼夜变化与GNSS衍生的PWV的昼夜变化非常吻合，而在多个站点观察到的MERRA-2则存在显着差异。这些结果表明，ERA5可以提供比TP更可靠的每小时PWV估算值，这可以帮助用户进一步了解这两种PWV产品的优缺点，并促进气候变化研究。