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  • 1.
    Liemohn, Michael W.
    et al.
    Univ Michigan, Dept Climate & Space Sci & Engn, Ann Arbor, MI USA.
    McCollough, James P.
    US Air Force, Space Vehicles Directorate, Res Lab, Kirtland AFB, NM USA.
    Jordanova, Vania K.
    Los Alamos Natl Lab, Space Sci & Applicat, Los Alamos, NM USA.
    Ngwira, Chigomezyo M.
    Catholic Univ Amer, Dept Phys, Washington, DC USA; NASA, Goddard Space Flight Ctr, Greenbelt, MD USA.
    Morley, Steven K.
    Los Alamos Natl Lab, Space Sci & Applicat, Los Alamos, NM USA.
    Cid, Consuelo
    Univ Alcala De Henares, Dept Phys & Math, Madrid, Spain.
    Tobiska, W. Kent
    Space Environm Technol, Pacific Palisades, CA USA.
    Wintoft, Peter
    Swedish Inst Space Phys, Lund, Sweden.
    Ganushkina, Natalia Yu
    Univ Michigan, Dept Climate & Space Sci & Engn, Ann Arbor, MI USA; Finnish Meteorol Inst, Helsinki, Finland.
    Welling, Daniel T.
    Univ Michigan, Dept Climate & Space Sci & Engn, Ann Arbor, MI USA; Univ Texas Arlington, Arlington, TX USA.
    Bingham, Suzy
    UK Met Off, Exeter, Devon, England.
    Balikhin, Michael A.
    Univ Sheffield, Dept Automat Control & Syst Engn, Sheffield, S Yorkshire, England.
    Opgenoorth, Hermann
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Swedish Institute of Space Physics, Uppsala Division.
    Engel, Miles A.
    Los Alamos Natl Lab, Space Sci & Applicat, Los Alamos, NM USA.
    Weigel, Robert S.
    George Mason Univ, Dept Phys & Astron, Fairfax, VA USA.
    Singer, Howard J.
    NOAA, Space Weather Predict Ctr, Boulder, CO USA.
    Buresova, Dalia
    CAS, Inst Atmospher Phys, Prague, Czech Republic.
    Bruinsma, Sean
    Dept Space Geodesy CNES, Toulouse, France.
    Zhelayskaya, Irina S.
    GFZ German Res Ctr Geosci, Potsdam, Germany; Univ Potsdam, Inst Phys & Astron, Potsdam, Germany.
    Shprits, Yuri Y.
    GFZ German Res Ctr Geosci, Potsdam, Germany; Univ Potsdam, Inst Phys & Astron, Potsdam, Germany; UCLA, Dept Earth & Space Sci, Los Angeles, CA USA.
    Vasile, Ruggero
    GFZ German Res Ctr Geosci, Potsdam, Germany.
    Model Evaluation Guidelines for Geomagnetic Index Predictions2018In: Space Weather: The international journal of research and applications, ISSN 1542-7390, E-ISSN 1542-7390, Vol. 16, no 12, p. 2079-2102Article in journal (Refereed)
    Abstract [en]

    Geomagnetic indices are convenient quantities that distill the complicated physics of some region or aspect of near‐Earth space into a single parameter. Most of the best‐known indices are calculated from ground‐based magnetometer data sets, such as Dst, SYM‐H, Kp, AE, AL, and PC. Many models have been created that predict the values of these indices, often using solar wind measurements upstream from Earth as the input variables to the calculation. This document reviews the current state of models that predict geomagnetic indices and the methods used to assess their ability to reproduce the target index time series. These existing methods are synthesized into a baseline collection of metrics for benchmarking a new or updated geomagnetic index prediction model. These methods fall into two categories: (1) fit performance metrics such as root‐mean‐square error and mean absolute error that are applied to a time series comparison of model output and observations and (2) event detection performance metrics such as Heidke Skill Score and probability of detection that are derived from a contingency table that compares model and observation values exceeding (or not) a threshold value. A few examples of codes being used with this set of metrics are presented, and other aspects of metrics assessment best practices, limitations, and uncertainties are discussed, including several caveats to consider when using geomagnetic indices.

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