Volume 42 Issue 6
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FENG Taojun, YU Qian, ZHANG Kai. An Algorithm for Retrieving Ionospheric Electron Density from Far Ultraviolet Remote Sensing Based on Maximum Likelihood Estimation (in Chinese). Chinese Journal of Space Science, 2022, 42(6): 1100-1110 doi: 10.11728/cjss2022.06.211115118
Citation: FENG Taojun, YU Qian, ZHANG Kai. An Algorithm for Retrieving Ionospheric Electron Density from Far Ultraviolet Remote Sensing Based on Maximum Likelihood Estimation (in Chinese). Chinese Journal of Space Science, 2022, 42(6): 1100-1110 doi: 10.11728/cjss2022.06.211115118
shu

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

doi: 10.11728/cjss2022.06.211115118

  • Beijing Institute of Spacecraft Environment Engineering, Beijing 100094

  • Received Date: 2021-11-15
  • Accepted Date: 2021-04-15
  • Rev Recd Date: 2022-05-05
    • Available Online: 2022-11-09
    Abstract
  • 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 NmF2 and underestimate the hmF2. The systematic error is within 10% for NmF2 and 5% for hmF2 for different level of solar activity. Determining ionosphere electron density with high precision could help improve the ionospheric model and forecast the space weather.

     

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