Atmospheric water vapour plays an important role in phenomena related to the global hydrologic cycle and climate change. However, the rapid temporal–spatial variation in global tropospheric water vapour has not been well investigated due to a lack of long-term, high-temporal-resolution precipitable water vapour (PWV). Accordingly, this study generates an hourly PWV dataset for 272 ground-based International Global Navigation Satellite System (GNSS) Service (IGS) stations over the period of 2005–2016 using the zenith troposphere delay (ZTD) derived from global-scale GNSS observation. The root mean square (RMS) of the hourly ZTD obtained from the IGS tropospheric product is approximately 4 mm. A fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting (ECMWF ERA5) is used to obtain hourly surface temperature (T) and pressure (P), which are first validated with GNSS synoptic station data and radiosonde data, respectively. Then, T and P are used to calculate the water vapour-weighted atmospheric mean temperature (Tm) and zenith hydrostatic delay (ZHD), respectively. T and P at the GNSS stations are obtained via an interpolation in the horizontal and vertical directions using the grid-based ERA5 reanalysis dataset. Here, Tm is calculated using a neural network model, whereas ZHD is obtained using an empirical Saastamoinen model. The RMS values of T and P at the collocated 693 radiosonde stations are 1.6 K and 3.1 hPa, respectively. Therefore, the theoretical error of PWV caused by the errors in ZTD, T and P is on the order of approximately 2.1 mm. A practical comparison experiment is performed using 97 collocated radiosonde stations and 23 GNSS stations equipped with meteorological sensors. The RMS and bias of the hourly PWV dataset are 2.87/−0.16 and 2.45/0.55 mm, respectively, when compared with radiosonde and GNSS stations equipped with meteorological sensors. Additionally, preliminary analysis of the hourly PWV dataset during the EI Niño event of 2014–2016 further indicates the capability of monitoring the daily changes in atmospheric water vapour. This finding is interesting and significant for further climate research.

大气水蒸气在与全球水文循环和气候变化有关的现象中起着重要作用。但是，由于缺乏长期的，高时间分辨率的可沉淀水汽（PWV），全球对流层水汽的快速时空变化还没有得到很好的研究。因此，本研究使用从全球规模GNSS观测中得出的天顶对流层延迟（ZTD），在2005-2016年期间为272个地面国际国际导航卫星系统（GNSS）服务（IGS）站生成了每小时PWV数据集。从IGS对流层产品获得的每小时ZTD的均方根（RMS）约为4 mm。欧洲中程天气预报中心（ECMWF ERA5）的第五代再分析数据集用于获取每小时的地表温度（T）和压力（P），首先分别使用GNSS天气站数据和探空仪数据进行了验证。 。然后，使用T和P分别计算水蒸气加权的大气平均温度（Tm）和天顶静水延迟（ZHD）。使用基于网格的ERA5重新分析数据集，通过在水平和垂直方向上进行插值获得GNSS站处的T和P。在这里，Tm是使用神经网络模型计算的，而ZHD是使用经验Saastamoinen模型获得的。并置的693个探空站的T和P的RMS值分别为1.6 K和3.1 hPa。因此，由ZTD的误差引起的PWV的理论误差，T和P大约为2.1mm。使用97个并置的无线电探空仪站和23个装有气象传感器的GNSS站进行了实用的比较实验。与配备气象传感器的探空仪和GNSS站相比，小时PWV数据集的RMS和偏差分别为2.87 / -0.16和2.45 / 0.55 mm。此外，对2014-2016年EINiño事件期间每小时PWV数据集的初步分析进一步表明，能够监测大气水蒸气的每日变化。这一发现是有趣的，对进一步的气候研究具有重要意义。与配备气象传感器的探空仪和GNSS站相比，小时PWV数据集的RMS和偏差分别为2.87 / -0.16和2.45 / 0.55 mm。此外，对2014-2016年EINiño事件期间每小时PWV数据集的初步分析进一步表明，能够监测大气水蒸气的每日变化。这一发现是有趣的，对进一步的气候研究具有重要意义。与配备气象传感器的探空仪和GNSS站相比，小时PWV数据集的RMS和偏差分别为2.87 / -0.16和2.45 / 0.55 mm。此外，对2014-2016年EINiño事件期间每小时PWV数据集的初步分析进一步表明，能够监测大气水蒸气的每日变化。这一发现是有趣的，对进一步的气候研究具有重要意义。