High-Resolution Atmospheric Water Vapor Fields by Spaceborne Geodetic Sensing, Tomographic Fusion, and Atmospheric Modeling

虽然大气中只有4%的水蒸气，但水蒸气是大气中的主要气体之一。水蒸气是一种高效的温室气体，与全球气候变化及其对洪水、干旱、洪水或冰川融化等自然灾害的影响直接相关。作为水文循环的重要组成部分，水汽是云和降水产生和时空分布的主要驱动力。水蒸气的量化仍然是一个挑战：虽然区域大气模型（有限区域模型，LAM）一般允许模拟水文气象变量在空间和时间上的高分辨率分布，其在详细再现其高时空变异性方面的性能仍然有限。同时，只有有限的高分辨率大气水汽验证记录存在。水汽作为气象学和气候学研究中的重要信号，在大地测量和遥感应用中主要被视为噪声源。地球大气层的湿度导致微波信号的时间和空间高度波动的延迟和失真，这是多频测量无法消除的，必须在数据处理过程中加以量化。因此，全球导航卫星系统（GNSS：高时间分辨率）和干涉合成孔径雷达（干涉合成孔径雷达：高空间分辨率）的观测为重建从卫星到地球表面观测点沿着的综合水汽提供了宝贵的贡献。此外，这些数据的复杂的基于断层扫描的评估甚至允许生成空间和时间上的水蒸气分布的3D场。通过使用全球导航卫星系统和基于干涉合成孔径雷达的技术，并结合高分辨率区域大气天气模型和地质统计数据合并技术，拟议项目旨在开发和评价新的方法，以改进对大气水汽分布的时空估计。特别是，将进一步发展基于层析成像的大地测量和遥感数据评价方法，以提高研究中的大气状态变量的垂直和水平分辨率。生成的产品用于与基于大气模式的信息进行比较和同化，以最终获得大气水汽分布的最佳估计。

Although the atmosphere contains only up to 4% water vapor by volume, water vapor is one of the central atmospheric gases. Water vapor is a highly effective greenhouse gas that is directly intertwined with global climate change and its implications for natural disasters such as floods, droughts, deluge or glacier melting. As a vital component of the hydrological cycle, water vapor represents a main driver for the generation and spatio-temporal distribution of clouds and precipitation. The quantification of water vapor remains a challenge: while regional atmospheric models (Limited Area Model, LAM) in general allow to simulate the distribution of hydro-meteorological variables in space and time in high resolution, their performance in reproducing in detail their high spatio-temporal variability remains still limited. At the same time only limited high resolution atmospheric water vapor validation records exist. Acting as an important signal in meteorology and climate research, water vapor principally is regarded as a source of noise in Geodesy and Remote Sensing applications. The humidity of the Earth's atmosphere induces delays and distortions of high temporal and spatial fluctuations in microwave signals, which cannot be eliminated by multi-frequency measurements and have to be quantified during the data processing. Thus observations of Global Navigation Satellite Systems (GNSS: high temporal resolution) and Interferometric Synthetic Aperture Radar (InSAR: high spatial resolution) provide valuable contributions for reconstructing the integrated water vapor along the path from the satellites to the observation site on the Earth's surface. In addition, the sophisticated tomography-based evaluation of these data even allows generating 3D fields of the water vapor distribution in space and time. By using GNSS and InSAR based techniques in combination with high resolution regional atmospheric weather models and geostatistical data merging techniques, the proposed project aims at developing and evaluating new approaches to derive improved spatio-temporal estimates of the atmospheric water vapor distribution. In particular, tomography-based approaches in the evaluation of geodetic and remote sensing data will be further developed to improve the vertical and horizontal resolution of the atmospheric state variable under research. The generated products are used for comparison and assimilation with atmospheric model-based information to finally get an optimal estimation of the atmospheric water vapor distribution.