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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

(left) Reliability diagrams for (top) RMP and (bottom) Jrφ resulting from undownscaled, single downscale, and ensemble downscale model-like time series. (right) Ensemble spread error diagrams. See text for discussion.
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fig07: (left) Reliability diagrams for (top) RMP and (bottom) Jrφ resulting from undownscaled, single downscale, and ensemble downscale model-like time series. (right) Ensemble spread error diagrams. See text for discussion.

Mentions: Table1 lists the statistical properties of the magnetospheric response to the various solar wind time series. In general, the effect of this single realization of downscaling is to bring the mean and standard deviations of magnetospheric parameters closer to those obtained from the use of actual ACE observations (this is further illustrated in the reliability diagrams in Figure 7, discussed in the next section). Despite this statistical improvement, this single downscaling instance also increases the point-by-point error (i.e., the mean-square error increases, and the linear correlation decreases). Note, however, that point-by-point analysis may not be the best assessment of the usefulness of a forecast, as it frequently over penalizes forecasts which exhibit the correct variability but contain small timing errors [e.g., Owens et al., 2013, Figure 8]. In the next section, we test the value added to downscaled forecasts using an ensemble approach rather than a single realization.


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)

(left) Reliability diagrams for (top) RMP and (bottom) Jrφ resulting from undownscaled, single downscale, and ensemble downscale model-like time series. (right) Ensemble spread error diagrams. See text for discussion.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig07: (left) Reliability diagrams for (top) RMP and (bottom) Jrφ resulting from undownscaled, single downscale, and ensemble downscale model-like time series. (right) Ensemble spread error diagrams. See text for discussion.
Mentions: Table1 lists the statistical properties of the magnetospheric response to the various solar wind time series. In general, the effect of this single realization of downscaling is to bring the mean and standard deviations of magnetospheric parameters closer to those obtained from the use of actual ACE observations (this is further illustrated in the reliability diagrams in Figure 7, discussed in the next section). Despite this statistical improvement, this single downscaling instance also increases the point-by-point error (i.e., the mean-square error increases, and the linear correlation decreases). Note, however, that point-by-point analysis may not be the best assessment of the usefulness of a forecast, as it frequently over penalizes forecasts which exhibit the correct variability but contain small timing errors [e.g., Owens et al., 2013, Figure 8]. In the next section, we test the value added to downscaled forecasts using an ensemble approach rather than a single realization.

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