Registered Data

[00787] Space Weather: Modeling, Surrogates and Uncertainty Quantification

  • Session Time & Room : 5D (Aug.25, 15:30-17:10) @F308
  • Type : Proposal of Minisymposium
  • Abstract : Electronic technologies that govern modern life, such as the Global Positioning System, are dependent on satellite technologies, which require accurate space weather models with quantified uncertainties to operate safely and efficiently. Uncertainties in space weather models are wide-ranging. They can stem from how the models are driven, e.g., parameters, initial conditions, forcing, and from the treatment of the internal physics and numerics. For example, predictions of thermospheric density must account for model-form and parametric uncertainty in models of thermal conductivity and Nitric Oxide cooling. This minisymposium presents broad class of novel UQ methods for the exciting application of space weather.
  • Organizer(s) : Boris Kramer, Enrico Camporeale
  • Classification : 37Exx, 62Fxx, 62Gxx, 76Xxx
  • Minisymposium Program :
    • 00787 (1/1) : 5D @F308 [Chair: Boris Kramer]
      • [02020] Model Calibration for Ensemble CME Simulation with the SWMF
        • Format : Talk at Waseda University
        • Author(s) :
          • Hongfan Chen (University of Michigan)
          • Yang Chen (University of Michigan)
          • Xun Huan (University of Michigan)
          • Bartholomeus van der Holst (University of Michigan)
          • Shasha Zou (University of Michigan)
          • Zhenguang Huang (University of Michigan)
          • Nishtha Sachdeva (University of Michigan)
          • Aniket Jivani (University of Michigan)
          • Daniel Iong (University of Michigan)
          • Ward Manchester (University of Michigan)
          • Gabor Toth (University of Michigan)
          • Yifu An (University of Michigan)
        • Abstract : The Space Weather Modeling Framework $\left(SWMF\right)$ enables ensemble coronal mass ejection $\left(CME\right)$ simulation based on coupled first principles and/or empirical models. The main challenge of calibrating unknown parameters of such physics-based models lies in high computational complexity and potential model inadequacy. In this talk, we present model calibration for the parameters of Gibson-Low flux-rope-based CMEs. Leveraging machine learning tools, we quantify the uncertainty in flux-rope parameters by assimilating in-situ and remote observations.
      • [02101] Model reduction with data assimilation for thermospheric mass density forecasting
        • Format : Talk at Waseda University
        • Author(s) :
          • Peng Mun Siew (Massachusetts Institute of Technology)
          • Richard Linares (Massachusetts Institute of Technology)
        • Abstract : Earth atmospheric drag remains one of the main sources of uncertainties for orbit prediction of space objects residing in the Low Earth Orbit. In this work, we explore the usage of machine learning-based techniques to develop a data-driven dynamic reduced-order model for real-time forecasting of the thermospheric density field. The high-dimensional thermospheric density field is projected onto a lower-dimensional latent space using nonlinear embedding via the deep encoder network.
      • [01696] Bayesian Parameter Estimation for Ambient Solar Wind Models
        • Format : Online Talk on Zoom
        • Author(s) :
          • Opal Issan (University of California San Diego)
          • Boris Kramer (University of California San Diego)
          • Enrico Camporeale (National Oceanic and Atmospheric Administration)
        • Abstract : The solar wind is an essential driver of space weather geomagnetic storms. A significant challenge in using first-principle solar wind models is estimating input parameters that can not be directly measured. Thus, we need to quantify the uncertainty of such input parameters on the solar wind. We perform global sensitivity analysis to understand which parameters influence the model output the most and learn the posterior distribution of the most influential input parameters via Bayesian inference.
      • [02759] A multi-fidelity boosted method with built-in uncertainty quantification and its application to geomagnetic storms prediction
        • Format : Online Talk on Zoom
        • Author(s) :
          • Andong Hu (CIRES, CU Boulder)
          • Enrico camporeale (CIRES, CU Boulder)
          • Brian swiger (CIRES, CU Boulder)
        • Abstract : An multi-fidelity based Gated Recurrent Unit (GRU) method is developed to assist ensemble technique to forecast extreme space weather events and their reliability. We have implemented this method on two space weather applications, i.e., 1) a one-to-six-hour lead-time model that predicts the value of Disturbance storm time (Dst) using solar wind (SW) data; and 2) an geoelectric field model with multi-hour leading time using SW and SuperMag data.