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.
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
The data included are those that were used in the creation of a model described in the manuscript titled "Predictions of Electron Flux in the near-Earth Plasma Sheet from Solar Wind Driving" by Swiger et al., 2022,
published in the Space Weather Journal.
doi: pending, TBD and The manuscript describes the development and assessment of a model that predicts electron flux (from 83 eV to 93 keV energies) in a region of Earth's magnetosphere called the plasma sheet. The model uses inputs of solar wind parameters including, but not limited, to solar wind speed and the interplanetary magnetic field.
Swiger, B. M., Liemohn, M. W., Ganushkina, N. Y., & Dubyagin, S. V. (2022). Energetic electron flux predictions in the near-Earth plasma sheet from solar wind driving. Space Weather, 20, e2022SW003150. https://doi.org/10.1029/2022SW003150
