基于优选多系统GNSS信号时空分布特征的自适应水汽层析模型

GNSS tomography is a promising means of measuring and monitoring of water vapor in the troposphere due to its 24 hours availability, global coverage and low cost. With the development of Global Navigation Satellite Systems (GNSS), the commonly used static tomographic model, which is based on the single system, is not consistent with the fact that the distribution of GNSS signals quickly changes over time. Moreover, the traditional tomography model does not take into account the optimized multi-GNSS signals. To address the above issues, the adaptive water vapor tomography model based on the space-time distribution features of the optimized multi-GNSS signals was proposed in this project. The innovation model includes following aspects: (1) the determination of tomographic fields fitting the real distribution of GNSS signals on different tomographic planes at different tomographic epochs; (2) the discretization of the tomographic fields is dynamic based on the signal density; (3) the optimized method is introduced in the GNSS signal sampling to improve the quality of the tomographic equations and the tomographic results. The successful implementation of the project can drive the development of GNSS meteorology in the study of weather and climate change. Therefore, the proposed research is of great scientific significance and significant value in technology innovation.

GNSS水汽层析凭借全天候、高覆盖率以及低成本的优势迅速获得重视，是目前最具应用前景的水汽探测技术之一。随着多系统GNSS的快速发展，基于单系统的静态GNSS水汽层析传统模型已无法满足目前的需求。针对目前传统层析模型与动态变化的GNSS信号观测区域不匹配问题，以及多系统GNSS信号数据优化筛选问题，项目提出基于优选多系统GNSS信号时空分布特征的自适应水汽层析模型，核心内容如下：（1）构建动态匹配GNSS观测信号分布的自适应层析区域；（2）基于GNSS信号密度实现层析区域自适应划分和水汽参数化；（3）基于优选GNSS信号方法，获取高质量GNSS水汽层析方程组，提高层析方程解算结果的质量。项目的顺利实施有望推动GNSS气象学在天气和气候研究方面的发展，具有重要科学意义和应用价值。

GNSS水汽层析技术具有反演高时空分辨率三维大气水汽的能力，是目前最具应用前景的大气水汽探测技术之一。随着多源气象产品的不断发展，该项技术极具潜力成为融合卫星导航技术和多源数据，进行高精度三维大气水汽感知的跨学科技术。项目提出基于优选多系统GNSS信号时空分布特征的自适应水汽层析模型，核心内容如下：（1）构建动态匹配GNSS观测信号分布的自适应层析区域；（2）基于GNSS信号密度实现层析区域自适应划分和水汽参数化；（3）基于优选GNSS信号方法，获取高质量GNSS水汽层析方程组，提高层析方程解算结果的质量。项目取得主要成果如下：（1）改善了水汽层析模型的垂直切割方法，使大气水汽分布模型更加合理；（2）多源数据融合补充了水汽层析的初始数据，完善了水汽层析的先验信息；（3）改进了传统的水汽层析模型参数化方法，可自适应进行不同区域的水汽层析。项目旨在促进“GNSS+气象”各相关学科进一步交叉融合，齐力共进发展高精度高时空分辨率的三维大气水汽监测技术，丰富全球气象三维大气水汽数据库。