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Ensemble downscaling in coupled solar wind-magnetosphere modeling for space weather forecasting.

Owens MJ, Horbury TS, Wicks RT, McGregor SL, Savani NP, Xiong M - Space Weather (2014)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Space Environment Physics Group, Department of Meteorology, University of Reading Reading, UK.

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.

Key points: 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.

No MeSH data available.


Related in: MedlinePlus

A coupled numerical scheme for predicting space weather using photospheric magnetic field observations. Using model solar wind output to drive magnetospheric simulations requires significant spatial and temporal downscaling of the time series.
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fig01: A coupled numerical scheme for predicting space weather using photospheric magnetic field observations. Using model solar wind output to drive magnetospheric simulations requires significant spatial and temporal downscaling of the time series.

Mentions: By driving magnetospheric simulations with predictions from coronal and solar wind models, as opposed to L1 spacecraft observations, the forecast lead time can be extended from under an hour to 2–4 days, the solar wind travel time from the Sun to Earth. Figure 1 shows a chain of coupled numerical models for space weather forecasting, ultimately driven by remotely sensed photospheric magnetic field observations, though white light observations of the corona are also being investigated as initialization method. Individual model components are those used in Luhmann et al. [2004] and Merkin et al. [2007], listed here only as examples. Such “Sun-to-Earth” forecasting of geomagnetic storms can be broadly considered a two-step process: forecasting the solar wind conditions in near-Earth space, and forecasting the subsequent magnetospheric response to those solar wind conditions.


Ensemble downscaling in coupled solar wind-magnetosphere modeling for space weather forecasting.

Owens MJ, Horbury TS, Wicks RT, McGregor SL, Savani NP, Xiong M - Space Weather (2014)

A coupled numerical scheme for predicting space weather using photospheric magnetic field observations. Using model solar wind output to drive magnetospheric simulations requires significant spatial and temporal downscaling of the time series.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4508929&req=5

fig01: A coupled numerical scheme for predicting space weather using photospheric magnetic field observations. Using model solar wind output to drive magnetospheric simulations requires significant spatial and temporal downscaling of the time series.
Mentions: By driving magnetospheric simulations with predictions from coronal and solar wind models, as opposed to L1 spacecraft observations, the forecast lead time can be extended from under an hour to 2–4 days, the solar wind travel time from the Sun to Earth. Figure 1 shows a chain of coupled numerical models for space weather forecasting, ultimately driven by remotely sensed photospheric magnetic field observations, though white light observations of the corona are also being investigated as initialization method. Individual model components are those used in Luhmann et al. [2004] and Merkin et al. [2007], listed here only as examples. Such “Sun-to-Earth” forecasting of geomagnetic storms can be broadly considered a two-step process: forecasting the solar wind conditions in near-Earth space, and forecasting the subsequent magnetospheric response to those solar wind conditions.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Space Environment Physics Group, Department of Meteorology, University of Reading Reading, UK.

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.

Key points: 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.

No MeSH data available.


Related in: MedlinePlus