Time-Dependent Ionospheric Tomography Based on Two-Step Reconstruction and Node Parameterization Algorithm
基于两步重建与节点参数化算法的时变电离层层析成像研究
引文格式 | Citation:
Chen B, Wang X, Zhang Z, et al. Time-Dependent Ionospheric Tomography Based on Two-Step Reconstruction and Node Parameterization Algorithm[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 17: 15789–15805. DOI: 10.1109/JSTARS.2024.3452137.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing(中科院2区Top,IF:5.3)
Tomography
Mathematical models
Ionosphere
Global navigation satellite system
Computational modeling
Numerical models
Electrons
Mathematical models
Ionosphere
Global navigation satellite system
Computational modeling
Numerical models
Electrons
Abstract | 摘要
The precise reconstruction of ionospheric electron density (IED) fields with high spatial and temporal resolutions has always been challenging. Global navigation satellite system (GNSS) tomography is a powerful tool to resolve the spatial structure and temporal behavior of IED. This study proposes a novel two-step algorithm of ionospheric tomography for the reconstruction of IED fields with high time resolution. Linear time-dependent ionospheric tomography model based on the node-based parameterization method is established for the first time. In the two-step reconstruction method, the linear trends of IED over a long time are first inverted. Then, the modeling residuals are adopted to obtain deviation terms. In addition, the design matrix is adaptively adjusted because the vertical variation parameter of IED for each voxel is dynamically updated from the IED profiles after each iteration. The tomography (5 min) is validated with GPS data collected over a one-month period (September 2020) from 629 stations in the USA. According to the GPS, COSMIC-2, and Swarm validations, the proposed tomography approach outperforms voxel-based, traditional node parameterization, and linear time-dependent methods by 10%–40%. The performance of the tomographic modeling is further examined by using a high geomagnetic activity period of April 20–29, 2023 in the high solar activity year. Results show that the tomographic model is robust even during severe geomagnetic storms.
高时空分辨率电离层电子密度(IED)场的精确重建一直是一项挑战。全球导航卫星系统(GNSS)层析技术是解析IED空间结构和时间特性的重要手段。本研究提出了一种新颖的两步法电离层层析算法,用于实现高时间分辨率的IED场重建。首次建立了基于节点参数化方法的线性时变电离层层析模型。该两步重建方法首先反演较长时间内IED的线性趋势,继而利用建模残差获取偏差项。此外,由于每个体素的IED垂直变化参数在每次迭代后都会根据IED剖面动态更新,因此设计矩阵能够进行自适应调整。利用美国境内629个测站在一个月期间(2020年9月)采集的GPS数据,对该层析方法(时间分辨率为5分钟)进行了验证。根据GPS、COSMIC-2和Swarm卫星的验证结果,本文提出的层析方法性能优于基于体素的传统方法、传统节点参数化方法以及线性时变方法,提升幅度达10%–40%。本文进一步利用高太阳活动年份中2023年4月20日至29日这一高磁活动期,检验了层析建模的性能。结果表明,即使在强地磁暴期间,该层析模型仍表现出良好的稳健性。