title: Ensemble downscaling in coupled solar wind-magnetosphere modeling for space weather forecasting.
abstract: Advanced forecasting of space weather requires simulation of the whole Sun-to-Earth system, which necessitates driving magnetospheric models with the outputs from solar wind models. This presents a fundamental difficulty, as the magnetosphere is sensitive to both large-scale solar wind structures, which can be captured by solar wind models, and small-scale solar wind "noise," which is far below typical solar wind model resolution and results primarily from stochastic processes. Following similar approaches in terrestrial climate modeling, we propose statistical "downscaling" of solar wind model results prior to their use as input to a magnetospheric model. As magnetospheric response can be highly nonlinear, this is preferable to downscaling the results of magnetospheric modeling. To demonstrate the benefit of this approach, we first approximate solar wind model output by smoothing solar wind observations with an 8 h filter, then add small-scale structure back in through the addition of random noise with the observed spectral characteristics. Here we use a very simple parameterization of noise based upon the observed probability distribution functions of solar wind parameters, but more sophisticated methods will be developed in the future. An ensemble of results from the simple downscaling scheme are tested using a model-independent method and shown to add value to the magnetospheric forecast, both improving the best estimate and quantifying the uncertainty. We suggest a number of features desirable in an operational solar wind downscaling scheme.
  
  
  CONCLUSIONS
  Solar wind models must be downscaled in order to drive magnetospheric models Ensemble downscaling is more effective than deterministic downscaling The magnetosphere responds nonlinearly to small-scale solar wind fluctuations.
authors.affiliation: Space Environment Physics Group, Department of Meteorology, University of Reading Reading, UK.
authors.forename: M J
authors.initials: MJ
authors.lastname: Owens
authors.affiliation: Space and Atmospheric Physics, Imperial College London London, UK.
authors.forename: T S
authors.initials: TS
authors.lastname: Horbury
authors.affiliation: NASA Goddard Space Flight Center Greenbelt, Maryland, USA ; Astronomy Department, University of Maryland College Park, Maryland, USA.
authors.forename: R T
authors.initials: RT
authors.lastname: Wicks
authors.affiliation: Department of Physics and Astronomy, Dartmouth College Hanover, New Hampshire, USA.
authors.forename: S L
authors.initials: SL
authors.lastname: McGregor
authors.affiliation: NASA Goddard Space Flight Center Greenbelt, Maryland, USA ; School of Physics, Astronomy, Computational Sciences, George Mason University Fairfax, Virginia, USA.
authors.forename: N P
authors.initials: NP
authors.lastname: Savani
authors.affiliation: State Key Laboratory for SpaceWeather, Center for Space Science and Applied Research, Chinese Academy of Sciences Beijing, China.
authors.forename: M
authors.initials: M
authors.lastname: Xiong
keywords: numerical modelling; space weather; stochastic processes