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  • Data for reconstruction analysis of global ionospheric outflow patterns

    work
    Creator:
    Liemohn, Michael W
    Description:
    Earth’s upper atmosphere above 500 km altitude constantly loses charged particles to outer space in a process called ionospheric outflow. This outflow is important for the dynamics of the near-Earth space environment (“space weather”) yet is poorly understood on a global scale. A mission is needed to observe the global patterns of ionospheric outflow and its relation to space weather driving conditions. The science objectives of such a mission could include not only the reconstruction of global outflow patterns but also the relation of these patterns to geomagnetic activity and the spatial and temporal nature of outflow composition. A study is presented to show that four well-placed spacecraft would be sufficient for reasonable outflow reconstructions.
    Keyword:
    ionosphere, magnetosphere, satellite mission concept, and space weather
    Citation to related publication:
    Liemohn, M. W., Jörg-Micha Jahn, Raluca Ilie, Natalia Y. Ganushkina, Daniel T. Welling, Heather Elliott, Meghan Burleigh, Kaitlin Doublestein, Stephanie Colon-Rodriguez, Pauline Dredger, & Philip Valek (2024). Reconstruction analysis of global ionospheric outflow patterns. Journal of Geophysical Research Space Physics, 129, e2023JA032238.  https://doi/org/10.1029/2024JA032238
    Discipline:
    Science

  • Data for Analysis of features in a sliding threshold of observation for numeric evaluation (STONE) curve

    work
    Creator:
    Liemohn, Michael W, Adam, Joshua G, and Ganushkina, Natalia Y
    Description:
    Many statistical tools have been developed to aid in the assessment of a numerical model’s quality at reproducing observations. Some of these techniques focus on the identification of events within the data set, times when the observed value is beyond some threshold value that defines it as a value of keen interest. An example of this is whether it will rain, in which events are defined as any precipitation above some defined amount. A method called the sliding threshold of observation for numeric evaluation (STONE) curve sweeps the event definition threshold of both the model output and the observations, resulting in the identification of threshold intervals for which the model does well at sorting the observations into events and nonevents. An excellent data-model comparison will have a smooth STONE curve, but the STONE curve can have wiggles and ripples in it. These features reveal clusters when the model systematically overestimates or underestimates the observations. This study establishes the connection between features in the STONE curve and attributes of the data-model relationship. The method is applied to a space weather example.
    Keyword:
    space physics, statistical methods, and STONE curve
    Citation to related publication:
    Liemohn, M. W., Adam, J. G., & Ganushkina, N. Y. (2022). Analysis of features in a sliding threshold of observation for numeric evaluation (STONE) curve. Space Weather, 20, e2022SW003102.  https://doi.org/10.1029/2022SW003102
    Discipline:
    Science