近年来，我国大气污染问题日益严重，尤其是气溶胶污染对空气质量的突出影响引起了社会的广泛关注。气溶胶光学厚度（Aerosol Optical Depth, AOD）是表征大气浑浊程度的关键物理量，也是确定气溶胶气候效应的重要因素。因此，对AOD进行准确监测和估计对于研究气候变化至关重要。目前常用的气溶胶光学厚度获取方法包括站点观测、卫星遥感探测、再分析资料获取以及模型模拟等，上述方法各具优势，但整体上存在精度低、分辨率低以及时空分布不完整等缺点。自全球导航卫星系统（Global Navigation Satellite System, GNSS）气象学提出以来，基于GNSS技术反演的对流层延迟（Zenith Tropospheric Delay, ZTD）和大气可降水量（Precipitable Water Vapor, PWV）已经广泛应用于大气污染研究中，目前已有学者研究发现GNSS技术反演的ZTD/PWV与AOD之间存在着密切关系，但尚未有研究直接将GNSS ZTD/PWV应用于气溶胶光学厚度的预测研究中。因此，本文结合GNSS技术高精度、高时间分辨率和时序完整等优势，提出了顾及高程因素的GNSS解算及ZTD/PWV获取方法，并分析了GNSS ZTD/PWV与AOD之间的关系，进一步发展了基于GNSS ZTD/PWV的AOD预测理论与方法。本文主要研究内容如下：
（1）针对现有ZTD经验模型无法映射局部气象问题，且在高程较大区域精度较低的缺陷，提出了一种顾及高程因素的GNSS解算及ZTD/PWV估计方法，实现了中国区域高精度ZTD/PWV反演。首先，通过分析小时分辨率ZTD残差数据的周期特征，以及周期残差与高程之间的关系建立高程与周期残差间的线性模型；其次，将全球气温气压（Global Pressure and Temperature 3, GPT 3）模型计算的ZTD作为模型初值，构建中国区域高精度ZTD估计模型（High-precision ZTD Model for China, CHZ）并将CHZ模型提供的ZTD应用到精密单点定位中；再次，使用中国区域164个GNSS站点和87个RS站点上2012至2018年的ZTD数据对CHZ模型进行验证，结果表明CHZ模型具有较好的精度，采用CHZ模型收敛后的定位精度和收敛时间均有改进；最后，将模型应用于GNSS ZTD/PWV的反演中，发现基于CHZ-PPP反演的ZTD/PWV均具有较好的精度。
（2）基于现有研究缺乏直接利用GNSS PWV进行AOD预测研究，本文基于CHZ模型反演的GNSS PWV数据提出了两种AOD预测模型，两种模型均可以较好的实现高精度AOD预测。首先，通过分析GNSS PWV与AOD 550及五种类型AOD之间的相关性，发现PWV对不同类型AOD具有不同的敏感性且相邻历元AOD间也具有较强的自相关性。基于这一结论，本文构建了两种基于GNSS PWV的加权AOD预测模型，分别为TAF（Total AOD Forecast）和FATF（Five Type-based AOD Forecast）模型。实验表明，TAF和FTAF模型预测的AOD与真值之间具有较好的一致性，其均方根和偏差均较小，表明两个模型均具有较好的性能且FTAF模型略优于TAF模型。
（3）由于构建TAF和FTAF模型时所需PWV参数计算时存在气象参数误差和转换参数误差的影响，提出了一种基于GNSS ZTD的AOD自适应预测方法，更好地实现了高精度、高时间分辨率的AOD预测。首先，使用气溶胶机器人网络获取的AOD对基于网格的第二次现代回顾性研究与应用分析和哥白尼气溶胶监测服务提供的AOD进行对比验证，选出最优的AOD数据集进行后续的研究；其次，通过分析GNSS ZTD与AOD之间的关系，发现利用GNSS ZTD数据进行AOD预测研究具有一定的合理性；最后，在此基础上构建了基于GNSS ZTD的AOD自适应预测方法（Adaptive AOD Forecast, AAF），该方法仅将ZTD和上一历元的AOD作为输入参数，模型系数按月自适应更新。实验表明，AAF模型预测的AOD的内外符合精度均较小，且优于TAF和FTAF模型。
近年来，我国大气污染问题日益严重，尤其是气溶胶污染对空气质量的突出影响引起了社会的广泛关注。气溶胶光学厚度（Aerosol Optical Depth, AOD）是表征大气浑浊程度的关键物理量，也是确定气溶胶气候效应的重要因素。因此，对AOD进行准确监测和估计对于研究气候变化至关重要。目前常用的气溶胶光学厚度获取方法包括站点观测、卫星遥感探测、再分析资料获取以及模型模拟等，上述方法各具优势，但整体上存在精度低、分辨率低以及时空分布不完整等缺点。自全球导航卫星系统（Global Navigation Satellite System, GNSS）气象学提出以来，基于GNSS技术反演的对流层延迟（Zenith Tropospheric Delay, ZTD）和大气可降水量（Precipitable Water Vapor, PWV）已经广泛应用于大气污染研究中，目前已有学者研究发现GNSS技术反演的ZTD/PWV与AOD之间存在着密切关系，但尚未有研究直接将GNSS ZTD/PWV应用于气溶胶光学厚度的预测研究中。因此，本文结合GNSS技术高精度、高时间分辨率和时序完整等优势，提出了顾及高程因素的GNSS解算及ZTD/PWV获取方法，并分析了GNSS ZTD/PWV与AOD之间的关系，进一步发展了基于GNSS ZTD/PWV的AOD预测理论与方法。本文主要研究内容如下：
（1）针对现有ZTD经验模型无法映射局部气象问题，且在高程较大区域精度较低的缺陷，提出了一种顾及高程因素的GNSS解算及ZTD/PWV估计方法，实现了中国区域高精度ZTD/PWV反演。首先，通过分析小时分辨率ZTD残差数据的周期特征，以及周期残差与高程之间的关系建立高程与周期残差间的线性模型；其次，将全球气温气压（Global Pressure and Temperature 3, GPT 3）模型计算的ZTD作为模型初值，构建中国区域高精度ZTD估计模型（High-precision ZTD Model for China, CHZ）并将CHZ模型提供的ZTD应用到精密单点定位中；再次，使用中国区域164个GNSS站点和87个RS站点上2012至2018年的ZTD数据对CHZ模型进行验证，结果表明CHZ模型具有较好的精度，采用CHZ模型收敛后的定位精度和收敛时间均有改进；最后，将模型应用于GNSS ZTD/PWV的反演中，发现基于CHZ-PPP反演的ZTD/PWV均具有较好的精度。
（2）基于现有研究缺乏直接利用GNSS PWV进行AOD预测研究，本文基于CHZ模型反演的GNSS PWV数据提出了两种AOD预测模型，两种模型均可以较好的实现高精度AOD预测。首先，通过分析GNSS PWV与AOD 550及五种类型AOD之间的相关性，发现PWV对不同类型AOD具有不同的敏感性且相邻历元AOD间也具有较强的自相关性。基于这一结论，本文构建了两种基于GNSS PWV的加权AOD预测模型，分别为TAF（Total AOD Forecast）和FATF（Five Type-based AOD Forecast）模型。实验表明，TAF和FTAF模型预测的AOD与真值之间具有较好的一致性，其均方根和偏差均较小，表明两个模型均具有较好的性能且FTAF模型略优于TAF模型。
（3）由于构建TAF和FTAF模型时所需PWV参数计算时存在气象参数误差和转换参数误差的影响，提出了一种基于GNSS ZTD的AOD自适应预测方法，更好地实现了高精度、高时间分辨率的AOD预测。首先，使用气溶胶机器人网络获取的AOD对基于网格的第二次现代回顾性研究与应用分析和哥白尼气溶胶监测服务提供的AOD进行对比验证，选出最优的AOD数据集进行后续的研究；其次，通过分析GNSS ZTD与AOD之间的关系，发现利用GNSS ZTD数据进行AOD预测研究具有一定的合理性；最后，在此基础上构建了基于GNSS ZTD的AOD自适应预测方法（Adaptive AOD Forecast, AAF），该方法仅将ZTD和上一历元的AOD作为输入参数，模型系数按月自适应更新。实验表明，AAF模型预测的AOD的内外符合精度均较小，且优于TAF和FTAF模型。
 

In recent years, air pollution in China has become an increasingly serious problem, and the prominent impact of aerosol pollution on air quality in particular has attracted widespread attention from society. Aerosol Optical Depth (AOD) is a key physical quantity that characterises the degree of atmospheric turbidity and is an important factor in determining the climatic effects of aerosols. Accurate monitoring and estimation of AOD is therefore essential for the study of climate change. Currently, the commonly used methods to obtain the optical thickness of aerosols include station observation, satellite remote sensing, reanalysis data acquisition and model simulation, each of which has its own advantages, but the overall disadvantages include low accuracy, low resolution and incomplete spatial and temporal distribution. Since the introduction of the Global Navigation Satellite System (GNSS) meteorology, GNSS-based inversions of Zenith Tropospheric Delay (ZTD) and Precipitable Water Vapor (PWV) have been widely used for climate change studies, and some scholars have found a close relationship between ZTD/PWV and AOD in GNSS inversions, but no study has directly applied GNSS ZTD/PWV to the prediction of aerosol optical thickness. Therefore, this paper combines the advantages of GNSS technology such as high accuracy, high temporal resolution and time series integrity, proposes a GNSS solution and ZTD/PWV acquisition method that takes into account the elevation factor, and analyses the relationship between the acquired ZTD/PWV and AOD to further explore its innovative application in AOD monitoring and prediction studies. The main research elements of this paper are as follows:
(1) To address the shortcomings that the existing empirical ZTD model cannot map local meteorological problems and has low accuracy in areas with large elevation, a GNSS solution and ZTD/PWV estimation method that takes into account the elevation factor is proposed to achieve a high-precision ZTD/PWV inversion for the Chinese region. Firstly, a linear model between elevation and period residuals is established by analysing the period characteristics of hourly-resolution ZTD residual data and the relationship between the period residuals and elevation; secondly, the ZTD calculated by the GPT3 model is used as the initial value of the model to construct a high-precision ZTD estimation model for China. CHZ) and apply the ZTD provided by the CHZ model to the PPP; again, the CHZ model is validated using ZTD data from 2012 to 2018 on 164 GNSS stations and 87 RS stations in the Chinese region, and the results show that the CHZ model has good accuracy, and the positioning accuracy and convergence time after convergence using the CHZ model are improved. Finally, the model was applied to the inversion of GNSS ZTD/PWV, and it was found that both ZTD/PWV based on CHZ-PPP inversion had better accuracy.
(2) Based on the lack of existing research on AOD monitoring using GNSS PWV directly, this paper proposes two AOD prediction models based on the GNSS PWV data inferred from the above CHZ model, both of which can achieve high accuracy in AOD prediction. First, by analyzing the correlation between GNSS PWV and AOD 550 and other five types of AOD, it is found that PWV has different sensitivities to different types of AOD and strong autocorrelation between adjacent ephemeral AOD. Based on this conclusion, two weighted AOD forecast models Total AOD Forecast (TAF) and Five Type-based AOD Forecast (FATF) based on GNSS PWV were constructed in this paper. The experiments show that the TAF and FTAF models have good agreement between the predicted AOD and the true value, and their RMS, Bias and MAE are smaller, indicating that both models have better performance and the FTAF model is better than the TAF model.
(3) Due to the drawbacks of meteorological parameter errors and transformation parameter errors in the PWV calculation in the above-mentioned TAF and FTAF models, an adaptive AOD prediction method based on GNSS ZTD is proposed based on the above-mentioned research basis, which can better achieve high accuracy and high time resolution AOD prediction. Firstly, the AOD provided by Modern-Era Retrospective Analysis for Research and Applications Version 2 and Copernicus Aerosol Monitoring Service are compared and validated using Aerosol Robotic Network AOD, and the optimal AOD dataset is selected for subsequent studies. Secondly, by analysing the relationship between GNSS ZTD and AOD, it is found that it is reasonable to use GNSS ZTD data for AOD prediction studies. Based on this, an Adaptive AOD Forecast (AAF) method based on GNSS ZTD was constructed, which only takes the ZTD and the AOD of the previous calendar year as input parameters, and the model coefficients are adaptively updated on a monthly basis. Experiments show that the AAF model predicts the AOD with a smaller accuracy of both internal and external conformity and outperforms the TAF and FTAF models.
 

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