基于最大似然估计的远紫外遥感反演电离层电子密度算法

An Algorithm for Retrieving Ionospheric Electron Density from Far Ultraviolet Remote Sensing Based on Maximum Likelihood Estimation

原子氧135.6 nm夜气辉主要由氧离子O^(+)与电子的辐射复合反应生成,一些星载远紫外遥感观测任务证实135.6 nm夜气辉可用于反演电离层电子密度。针对远紫外临边遥感观测反演电离层电子密度,分析了135.6 nm夜气辉辐射强度与电子密度之间的非线型前向模型,基于离散反演理论设计了从夜间135.6 nm临边观测数据反演电子密度高度分布的反演算法,算法应用最大似然估计通过迭代求解电离层参数的最佳拟合值。通过仿真计算了TIMED卫星上全球紫外成像仪GUVI观测的反演结果,验证了本反演算法的可行性。对GUVI的实际观测数据进行反演,获得了电子密度高度分布。通过与GUVI数据的电离层参数对比分析得出,本文建立的反演模型使N_(m)F_(2)被高估,同时使h_(m)F_(2)被低估。对于不同的太阳活动强度,N_(m)F_(2)和h_(m)F_(2)的系统误差分别在10%和5%以内,能较精确地获得电离层参数。精确获得电离层电子密度信息对于提高空间天气预报及电离层模型的修正具有重要意义。

The OI 135.6 nm nighttime emission is dominantly produced by radiative recombination of O^(+)ions and electrons.Many previous space-based Far Ultraviolet(FUV)remote sensing experiments have demonstrated that OI 135.6 nm nighttime intensity can be used to infer the ionospheric F region electron density.This paper firstly presents a forward model specifying the nonlinear relationship between electron density and 135.6 nm nightglow intensity.Then,we develop an algorithm to infer the altitude profile of electron density from the nighttime 135.6 nm limb intensity measurements using Discrete Inverse Theory(DIT).The algorithm applies maximum likelihood method to iteratively seek the most probable values of the ionospheric parameters.The viability of this algorithm is verified through performing the simulation of the synthetic 135.6 nm limb observation data generated from forward model using the TIMED/GUVI limb scan configuration.Finally,we invert the realistic GUVI limb observation measurements and obtain the retrieved Electron Density Profile(EDP).The comparison between retrieved ionospheric parameters and GUVI products suggests that the forward model tends to overestimate the N_(m)F_(2)and underestimate the h_(m)F_(2).The systematic error is within 10%for N_(m)F_(2)and 5%for h_(m)F_(2)for different level of solar activity.Determining ionosphere electron density with high precision could help improve the ionospheric model and forecast the space weather.