@article{SmirnovBerrendorfShpritsetal.2020,
  author    = {Smirnov, Artem and Berrendorf, Max and Shprits, Yuri Y. and Kronberg, Elena A. and Allison, Hayley J. and Aseev, Nikita and Zhelavskaya, Irina and Morley, Steven K. and Reeves, Geoffrey D. and Carver, Matthew R. and Effenberger, Frederic},
  title     = {Medium energy electron flux in earth's outer radiation belt (MERLIN)},
  series = {Space weather : the international journal of research and applications},
  volume    = {18},
  journal   = {Space weather : the international journal of research and applications},
  number    = {11},
  publisher = {American geophysical union, AGU},
  address   = {Washington},
  issn      = {1542-7390},
  doi       = {10.1029/2020SW002532},
  pages     = {20},
  year      = {2020},
  abstract  = {The radiation belts of the Earth, filled with energetic electrons, comprise complex and dynamic systems that pose a significant threat to satellite operation. While various models of electron flux both for low and relativistic energies have been developed, the behavior of medium energy (120-600 keV) electrons, especially in the MEO region, remains poorly quantified. At these energies, electrons are driven by both convective and diffusive transport, and their prediction usually requires sophisticated 4D modeling codes. In this paper, we present an alternative approach using the Light Gradient Boosting (LightGBM) machine learning algorithm. The Medium Energy electRon fLux In Earth's outer radiatioN belt (MERLIN) model takes as input the satellite position, a combination of geomagnetic indices and solar wind parameters including the time history of velocity, and does not use persistence. MERLIN is trained on >15 years of the GPS electron flux data and tested on more than 1.5 years of measurements. Tenfold cross validation yields that the model predicts the MEO radiation environment well, both in terms of dynamics and amplitudes o f flux. Evaluation on the test set shows high correlation between the predicted and observed electron flux (0.8) and low values of absolute error. The MERLIN model can have wide space weather applications, providing information for the scientific community in the form of radiation belts reconstructions, as well as industry for satellite mission design, nowcast of the MEO environment, and surface charging analysis.},
  language  = {en}
}
@article{LiemohnMcColloughJordanovaetal.2018,
  author    = {Liemohn, Michael W. and McCollough, James P. and Jordanova, Vania K. and Ngwira, Chigomezyo M. and Morley, Steven K. and Cid, Consuelo and Tobiska, W. Kent and Wintoft, Peter and Ganushkina, Natalia Yu and Welling, Daniel T. and Bingham, Suzy and Balikhin, Michael A. and Opgenoorth, Hermann J. and Engel, Miles A. and Weigel, Robert S. and Singer, Howard J. and Buresova, Dalia and Bruinsma, Sean and Zhelavskaya, Irina and Shprits, Yuri Y. and Vasile, Ruggero},
  title     = {Model Evaluation Guidelines for Geomagnetic Index Predictions},
  series = {Space Weather: The International Journal of Research and Applications},
  volume    = {16},
  journal   = {Space Weather: The International Journal of Research and Applications},
  number    = {12},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {1542-7390},
  doi       = {10.1029/2018SW002067},
  pages     = {2079 -- 2102},
  year      = {2018},
  abstract  = {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. Plain Language Summary One aspect of space weather is a magnetic signature across the surface of the Earth. The creation of this signal involves nonlinear interactions of electromagnetic forces on charged particles and can therefore be difficult to predict. The perturbations that space storms and other activity causes in some observation sets, however, are fairly regular in their pattern. Some of these measurements have been compiled together into a single value, a geomagnetic index. Several such indices exist, providing a global estimate of the activity in different parts of geospace. Models have been developed to predict the time series of these indices, and various statistical methods are used to assess their performance at reproducing the original index. Existing studies of geomagnetic indices, however, use different approaches to quantify the performance of the model. This document defines a standardized set of statistical analyses as a baseline set of comparison tools that are recommended to assess geomagnetic index prediction models. It also discusses best practices, limitations, uncertainties, and caveats to consider when conducting a model assessment.},
  language  = {en}
}