厄尔尼诺-南方涛动（El Niño-Southern Oscillation, ENSO）是太平洋上一种海洋大气耦合现象，ENSO通过影响水汽（Precipitable water vapor, PWV）输送过程，导致降水异常，进而引发干旱和洪涝等灾害，研究ENSO引发的旱涝影响对于应对气候变化具有重要意义。随着多种水汽探测技术的发展，如无线电探空（Radiosonde, RS）、全球导航卫星系统（Global Navigation Satellite System, GNSS）、无线电掩星和卫星遥感等技术，为利用PWV分析ENSO引发的旱涝影响研究提供了重要的数据支撑。然而，多种水汽探测技术获取PWV的精度存在不一致问题，因此，本文首先以高精度GNSS PWV和RS PWV为参考对多源PWV精度评估，准确掌握不同水汽探测技术获取PWV的精度，为利用PWV分析ENSO引发的旱涝影响研究提供有效数据；然后，使用高精度PWV联合降水数据计算标准化降水转化指数（Standardized Precipitation Conversion Index, SPCI），利用SPCI分析ENSO引发的旱涝影响特征，为ENSO引发的旱涝影响研究提供新思路。本文的主要研究内容如下：

（1）针对不同水汽探测技术获取PWV精度不一致的问题，提出以高精度GNSS PWV和RS PWV作为参考对多源PWV精度评估的方法。其中，针对GNSS PWV反演过程中实测气象参数不足的缺陷，提出利用非实测气象参数反演高精度GNSS PWV的方法。以此为基础，对欧洲中期天气预报中心（European Centre for Medium-Range Weather Forecasting, ECMWF）的第五代再分析数据集（ECMWF reanalysis v5, ERA5）、FengYun-3A（FY-3A）、Sentinel-3A和无线电掩星电离层与气候气象星座观测系统PWV的精度进行评估，为后续利用PWV定性定量分析ENSO引发的旱涝影响研究提供数据支撑。

（2）针对仅使用PWV无法定性定量分析ENSO引发的旱涝影响特征的问题，提出联合PWV和降水数据计算SPCI，并用于分析ENSO引发旱涝影响特征的方法。首先，利用传统干旱监测指数标准化降水蒸散指数（Standardized Precipitation Evapotranspiration Index, SPEI）与SPCI进行相关性分析，对SPCI在旱涝监测中的可行性进行研究；其次，对SPCI用于研究ENSO引发的旱涝影响的有效性进行检验，采用经验正交函数（Empirical Orthogonal Functions, EOF）方法分析降水、PWV与海洋表面温度之间存在相关性，证明SPCI能够用于ENSO引发的旱涝影响研究；最后，基于SPCI和Niño指数采用相关和互相关分析方法分析ENSO引发旱涝的时空影响特征，为ENSO引发的旱涝影响研究提供新思路。

The El Niño-Southern Oscillation (ENSO) is a coupled ocean-atmosphere phenomenon in the Pacific, which can cause drought or flood in the Pacific, and it is great significance to study the impact of drought and flood caused by ENSO in response to climate change. By affecting the process of precipitable water vapor (PWV), ENSO leads to abnormal precipitation, which in turn leads to disasters such as drought and flood. With the development of various water vapor detection technologies (such as radiosonde (RS), global navigation satellite system (GNSS), radio occultation, satellite remote sensing, etc.), it provides important data support for the study of the impact of drought and flood caused by ENSO using PWV analysis. However, there are inconsistencies in the accuracy of PWV obtained from various water vapor detection technologies. Therefore, this paper firstly evaluates the accuracy of multi-source PWV using high-precision GNSS PWV and RS PWV, aiming to accurately grasp the precision of PWV obtained from different water vapor detection technologies, so as to provide effective data for the study of the impact of drought and flood caused by ENSO through PWV analysis. Secondly, high-precision PWV is used in conjunction with precipitation data to calculate the Standardized Precipitation Conversion Index (SPCI). The impacts characteristics of drought and flood caused by ENSO are analyzed using SPCI, which provided a new method for studying the impacts of drought and flood caused by ENSO. The main research contents of this paper are as follows:

(1) Aiming at the issue of inconsistent accuracy in PWV obtained from various water vapor detection technologies, a method is proposed for evaluating the accuracy of multi-source PWV using high-precision GNSS PWV and RS PWV as reference. To address the shortcoming of insufficient measured meteorological parameters in the GNSS PWV inversion process, a method is proposed for retrieving high-precision GNSS PWV using non-measured meteorological parameters. On this basis, the accuracy of PWV from the European Centre for Medium-Range Weather Forecasting's (ECMWF) fifth-generation reanalysis dataset (ECMWF Reanalysis v5, ERA5), FengYun-3A (FY-3A), Sentinel-3A, and radio occultation ionospheric and meteorological constellation observation systems is evaluated. This provides data support for subsequent qualitative and quantitative analysis of the impacts of drought and flood caused by ENSO using PWV.

(2) Aiming at the issue of being unable to qualitatively and quantitatively analyze the impact characteristics of drought and flood caused by ENSO solely using PWV, a method is proposed to calculate SPCI by combining PWV and precipitation data, and use SPCI to analyze the characteristics of drought and flood impacts caused by ENSO. Firstly, the correlation analysis between the Standardized Precipitation Evapotranspiration Index (SPEI) and SPCI to investigate the feasibility of SPCI in drought and flood monitoring. Secondly, the effectiveness of SPCI in studying the impact of drought and flood caused by ENSO is tested, and the employing Empirical Orthogonal Functions (EOF) was used to analyze the correlation between precipitation, PWV, and sea surface temperature, which proved that SPCI can be used in the study the impacts of drought and flood caused by ENSO. Finally, based on SPCI and the Niño index, correlation and cross-correlation analysis methods are adopted to analyze the spatial and temporal impact characteristics of drought and flood caused by ENSO, providing a new method for studying the impact of drought and flood caused by ENSO.

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