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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

Same as Figure 5 but with the red line showing the downscaled ensemble mean and pink-shaded region showing the spread between maximum and minimum values of ensemble members.
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fig06: Same as Figure 5 but with the red line showing the downscaled ensemble mean and pink-shaded region showing the spread between maximum and minimum values of ensemble members.

Mentions: As the noise added by the downscaling scheme is produced by a random number generator, it is trivial to produce multiple noise realizations. By running the magnetospheric model multiple times with each downscaled model-like time series, we can produce a simple ensemble forecast [Leutbecher and Palmer, 2008] of magnetospheric conditions. Figure 6 shows the results from an ensemble of five magnetospheric model runs driven by five individual model-like downscales (including the one shown in Figure 5), in a similar format to Figure 5. The red line shows the ensemble mean, while the pink-shaded area shows the spread between the maximum and minimum values of ensemble members. In an operational forecast capacity, more than five ensemble members would ideally be used.


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)

Same as Figure 5 but with the red line showing the downscaled ensemble mean and pink-shaded region showing the spread between maximum and minimum values of ensemble members.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig06: Same as Figure 5 but with the red line showing the downscaled ensemble mean and pink-shaded region showing the spread between maximum and minimum values of ensemble members.
Mentions: As the noise added by the downscaling scheme is produced by a random number generator, it is trivial to produce multiple noise realizations. By running the magnetospheric model multiple times with each downscaled model-like time series, we can produce a simple ensemble forecast [Leutbecher and Palmer, 2008] of magnetospheric conditions. Figure 6 shows the results from an ensemble of five magnetospheric model runs driven by five individual model-like downscales (including the one shown in Figure 5), in a similar format to Figure 5. The red line shows the ensemble mean, while the pink-shaded area shows the spread between the maximum and minimum values of ensemble members. In an operational forecast capacity, more than five ensemble members would ideally be used.

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