Space Science Reviews

, Volume 212, Issue 1–2, pp 585–600 | Cite as

The MIGHTI Wind Retrieval Algorithm: Description and Verification

  • Brian J. Harding
  • Jonathan J. Makela
  • Christoph R. Englert
  • Kenneth D. Marr
  • John M. Harlander
  • Scott L. England
  • Thomas J. Immel
Article
Part of the following topical collections:
  1. The Ionospheric Connection Explorer (ICON) mission

Abstract

We present an algorithm to retrieve thermospheric wind profiles from measurements by the Michelson Interferometer for Global High-resolution Thermospheric Imaging (MIGHTI) instrument on NASA’s Ionospheric Connection Explorer (ICON) mission. MIGHTI measures interferometric limb images of the green and red atomic oxygen emissions at 557.7 nm and 630.0 nm, spanning 90–300 km. The Doppler shift of these emissions represents a remote measurement of the wind at the tangent point of the line of sight. Here we describe the algorithm which uses these images to retrieve altitude profiles of the line-of-sight wind. By combining the measurements from two MIGHTI sensors with perpendicular lines of sight, both components of the vector horizontal wind are retrieved. A comprehensive truth model simulation that is based on TIME-GCM winds and various airglow models is used to determine the accuracy and precision of the MIGHTI data product. Accuracy is limited primarily by spherical asymmetry of the atmosphere over the spatial scale of the limb observation, a fundamental limitation of space-based wind measurements. For 80% of the retrieved wind samples, the accuracy is found to be better than 5.8 m/s (green) and 3.5 m/s (red). As expected, significant errors are found near the day/night boundary and occasionally near the equatorial ionization anomaly, due to significant variations of wind and emission rate along the line of sight. The precision calculation includes pointing uncertainty and shot, read, and dark noise. For average solar minimum conditions, the expected precision meets requirements, ranging from 1.2 to 4.7 m/s.

Keywords

Thermospheric winds Limb imaging Interferometry Inverse theory 

Notes

Acknowledgements

B.J.H. was supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1144245. ICON is supported by NASA’s Explorers Program through contracts NNG12FA45C and NNG12FA42I. This work uses pyglow, a Python package that wraps several upper atmosphere climatological models. The pyglow package is open-sourced and available at https://github.com/timduly4/pyglow/. The authors thank the ICON team for helpful comments. The primary contact for the ICON MIGHTI Level 2 wind products is Jonathan J. Makela (jmakela@illinois.edu).

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Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Brian J. Harding
    • 1
  • Jonathan J. Makela
    • 1
  • Christoph R. Englert
    • 2
  • Kenneth D. Marr
    • 2
  • John M. Harlander
    • 3
    • 6
  • Scott L. England
    • 4
  • Thomas J. Immel
    • 5
  1. 1.Department of Electrical and Computer EngineeringUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  2. 2.Space Science DivisionNaval Research LaboratoryWashingtonUSA
  3. 3.Department of PhysicsSt. Cloud State UniversitySt. CloudUSA
  4. 4.Department of Aerospace and Ocean EngineeringVirginia Polytechnic Institute and State UniversityBlacksburgUSA
  5. 5.Space Sciences LaboratoryUniversity of California BerkeleyBerkeleyUSA
  6. 6.Space Systems Research CorporationAlexandriaUSA

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