大气水汽是天气形成和传播的物质基础，高度参与了全球水循环和能量交换。在长时间尺度上，水汽的变化更是被认为有助于气候变化的重要的反馈机制，与云、降水、辐射和陆面过程密切相关。由于水汽分布在时间和空间上的易变性，定义大气可降水量(Precipitable Water Vapor, PWV)作为表示和量化水汽的常用指标，该指标也成为监测大气活动的关键因子。随着各种水汽探测手段的发展和全球PWV数据的积累共享，时空不连续和分辨率粗糙成为限制其进一步应用的最大挑战。本文以PWV在短临天气预报和气候研究等方面的各种应用需求为出发点，共分高空间分辨率PWV反演、高时间分辨率PWV反演和高时空分辨率PWV反演三部分内容，以期通过联合多数据源的优势解决高分辨率的高精度水汽数据获取难题，主要工作和内容总结如下：

(1) 卫星遥感技术是高空间分辨率水汽监测的有效途径，其获取的PWV受遥感技术和仪器质量的影响并不如全球导航卫星系统(Global Navigation Satellite System, GNSS)精确。针对上述问题，本文在充分考虑水汽的时间渐变和季节周期特性后，提出了一种基于GNSS约束的MERSI/FY-3A PWV季节自适应校准方法，以提升卫星水汽产品质量的方式促进国产FY-3A多年水汽资料再应用。实验结果表明该校准方法建模数据成本较低，可以快速更新校准系数以适应PWV时空动态变化特征。

(2) 完整的PWV时间序列需要稳健的再处理方法获取，其中缺失填补和分辨率提升均是基于水汽数据特征推导出来的适当假设。由于再分析数据集的长时间跨度和完整空间覆盖优点，本文引入最新一代数据集ERA5提出了时空加权(Spatio-temporal Weighted, STW)方法。在充分考虑了不同水汽数据源间的空间关联、数据差异和时间相关等特性后，实现高时间分辨率的PWV完整序列获取。实验结果表明STW方法在各种尺度的缺失填补和分辨率提升方面均优于现有的时间插值和模型替换两种方法。

(3) 在多种可用水汽数据源的条件下，充分发挥现有技术的优势是获取高时空分辨率PWV的最佳解决方法。这种数据融合可以有效削弱或消除不同类型PWV数据的局限，改善单一数据反演中的时空不连续问题，但是只能满足空间或时间尺度的水汽应用需求。因此，本文针对现状提出理论上可兼顾时空分辨率和精度的双步PWV融合(Two-step based PWV fusion, TPF)方法，先空间后时间逐步实现高时空分辨率PWV反演。实验结果表明：双步PWV融合方法联合了多技术的水汽信息，可以在保证精度的前提下有效提升水汽监测的时空分辨率。降雨期间的性能评估结果可以进一步验证双步PWV融合方法的可靠性。

Atmospheric water vapour is the material basis for weather formation and propagation that is highly participates in the global water cycle and energy exchange. On long time scales, changes in water vapour are considered to be an important feedback mechanism contributing to climate change, which is closely related to cloud, precipitation, radiation and land surface processes. Due to the spatial and temporal variations in water vapour distribution, Precipitable Water Vapor (PWV) is defined as a common metric for representing and quantifying water vapour, and this metric has become a key factor in monitoring atmospheric activities. With the development of various approaches of measuring water vapour and the accumulation and sharing of global PWV data, spatial and temporal discontinuities and coarse resolution have become the biggest challenges limiting its further application. Starting from the various application requirements of PWV in short-term weather forecasting and climate research, this paper is divided into three parts: PWV retrieval with high spatial resolution, PWV retrieval with high temporal resolution and PWV retrieval with high spatio-temporal resolution, with a view to solving the problem of acquiring high-resolution and high-precision PWV data by combining the advantages of multiple data sources. The mainly research in this study are as follows:

(1) Satellite remote sensing technology is an effective approach to monitor water vapour at high spatial resolution, but the PWV acquired by it is not as accurate as the Global Navigation Satellite System (GNSS) due to the influence of remote sensing technology and instrument quality. To address the above problems, this paper proposes a seasonal adaptive calibration method for MERSI/FY-3A PWV based on GNSS after considering the temporal gradient and seasonal variation of water vapor, and promotes the re-application of of FY-3A multi-year PWV data by improving the quality of satellite products. The experimental results show that the seasonal adaptive calibration method has a low cost of modeling data and can quickly update the calibration coefficient to adapt to the spatio-temporal dynamic variations of PWV characteristics.

(2) The complete time series of PWV requires robust reprocessing methods, especially filling gaps and resolution improvement are based on appropriate assumptions derived from the data characteristics. Due to the advantages of long time periods and complete spatial coverage of the reanalysis dataset, the spatio-temporal Weighted (STW) method is proposed in this paper by introducing the latest generation dataset ERA5. After taking into account the spatial association, data variation and temporal correlation among different water vapour data sources, the complete time series of PWV is obtained with high temporal resolution. The experimental results show that the STW method outperforms the existing linear interpolation and period model methods in terms of filling gaps and resolution improvement at various schemes.

(3) Under the condition of multiple available water vapor data sources, taking advantage of existing technologies is the best solution to obtain high spatio-temporal resolution PWV. Such data fusion can effectively weaken or eliminate the limitations of different types of PWV data and improve the spatial and temporal discontinuities of a single technique, but can only meet the needs of water vapour applications at spatial or temporal scales. Therefore, this paper proposes a Two-step based PWV fusion (TPF) method that can combine spatial and temporal resolution and accuracy, and gradually realizes PWV retrieval with high spatio-temporal resolution in a step-by-step process. The experimental results show that the TPF method can effectively improve the spatio-temporal resolution of water vapor monitoring with guaranteed accuracy by combining the water vapor information from multiple technologies. The performance evaluation during rainfall can further validate the reliability of the TPF method.

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