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Creators/Authors contains: "Zou, S."

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  1. ; ; ; ; ( , Proceedings of the AAAI Conference on Artificial Intelligence)
    Free, publicly-accessible full text available January 1, 2024
  2. ; ; ; ; ( , International Conference on Machine Learning)
    Free, publicly-accessible full text available January 1, 2024
  3. ; ; ; ( , International Conference on Machine Learning)
    Accepted Manuscript Full Text Available
  4. ; ; ; ; ; ; ( , Physical Review A)
    Full Text Available 
  5. ; ; ; ( , International Conference on Machine Learning)
    Accepted Manuscript Full Text Available
  6. ; ; ; ( , Journal of Geophysical Research: Oceans)
    Full Text Available 
  7. ; ( , Conference on Neural Information Processing Systems)
    Accepted Manuscript Full Text Available
  8. ; ( , Conference on Neural Information Processing Systems)
    Accepted Manuscript Full Text Available
  9. ; ; ( , Advances in neural information processing systems)
    Accepted Manuscript Full Text Available
  10. ; ; ; ; ; ( , Space Weather)
    Abstract

    We use the Space Weather Modeling Framework Geospace configuration to simulate a total of 122 storms from the period 2010–2019. With the focus on the storm main phase, each storm period was run for 54 hr starting from 6 hr prior to the start of the Dst depression. The simulation output of ground magnetic variations, ΔBHin particular, were compared with ground magnetometer station data provided by SuperMAG to statistically assess the Geospace model regional magnetic perturbation prediction performance. Our results show that the regional predictions at mid‐latitudes are quite accurate, but the high‐latitude regional disturbances are still difficult to predict.

     
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