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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) The solar wind speed in near-Earth space for Carrington Rotation 1958 (i.e., January 2000). Black: 64 s resolution observations from the Advanced Composition Explorer (ACE) spacecraft. Red: Model predictions based upon Kitt Peak magnetograms. (right) The power spectrum in the same format. Although the large-scale structure of the solar wind is very well reproduced by the numerical modeling scheme during this particular interval, fluctuations below approximately 1 day (shown as the vertical dashed line) are much weaker. This is a fundamental limitation of using magnetogram-derived solar wind properties to drive magnetospheric simulations.
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fig02: (left) The solar wind speed in near-Earth space for Carrington Rotation 1958 (i.e., January 2000). Black: 64 s resolution observations from the Advanced Composition Explorer (ACE) spacecraft. Red: Model predictions based upon Kitt Peak magnetograms. (right) The power spectrum in the same format. Although the large-scale structure of the solar wind is very well reproduced by the numerical modeling scheme during this particular interval, fluctuations below approximately 1 day (shown as the vertical dashed line) are much weaker. This is a fundamental limitation of using magnetogram-derived solar wind properties to drive magnetospheric simulations.

Mentions: As discussed, solar wind models are initiated with coronal models which are constrained by photospheric magnetic field data. This approach is often capable of reproducing the steady state, large-scale structure of the ambient solar wind in near-Earth space [e.g., Owens et al., 2008] (see also Figure 2), though there is obviously much ongoing work to further improve their predictive capability. Initial attempts at simulating large-scale transient structures such as coronal mass ejections are also promising [Titov et al., 2008]. However, using solar wind model output to drive magnetospheric models presents a fundamental problem: the magnetosphere is sensitive to both the large-scale structure, which is captured by solar wind models, and small-scale fluctuations which are far below both typical solar wind model spatial and temporal scales [Borovsky and Funsten, 2003; Merkin et al., 2007]. Much of this solar wind “noise,” loosely defined here as fluctuations below the 1 day time scale (shown as the vertical dashed line in Figure 2), is likely the result of stochastic processes such as turbulence [e.g., Horbury et al., 2001; Alexandrova et al., 2009]. Thus, even substantial improvements/developments in the numerics and physics of solar wind models are unlikely to be able to deterministically forecast these structures. As the solar wind noise can change simulated magnetospheric responses by an order of magnitude [Merkin et al., 2007], a qualitatively different approach is required.


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) The solar wind speed in near-Earth space for Carrington Rotation 1958 (i.e., January 2000). Black: 64 s resolution observations from the Advanced Composition Explorer (ACE) spacecraft. Red: Model predictions based upon Kitt Peak magnetograms. (right) The power spectrum in the same format. Although the large-scale structure of the solar wind is very well reproduced by the numerical modeling scheme during this particular interval, fluctuations below approximately 1 day (shown as the vertical dashed line) are much weaker. This is a fundamental limitation of using magnetogram-derived solar wind properties to drive magnetospheric simulations.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig02: (left) The solar wind speed in near-Earth space for Carrington Rotation 1958 (i.e., January 2000). Black: 64 s resolution observations from the Advanced Composition Explorer (ACE) spacecraft. Red: Model predictions based upon Kitt Peak magnetograms. (right) The power spectrum in the same format. Although the large-scale structure of the solar wind is very well reproduced by the numerical modeling scheme during this particular interval, fluctuations below approximately 1 day (shown as the vertical dashed line) are much weaker. This is a fundamental limitation of using magnetogram-derived solar wind properties to drive magnetospheric simulations.
Mentions: As discussed, solar wind models are initiated with coronal models which are constrained by photospheric magnetic field data. This approach is often capable of reproducing the steady state, large-scale structure of the ambient solar wind in near-Earth space [e.g., Owens et al., 2008] (see also Figure 2), though there is obviously much ongoing work to further improve their predictive capability. Initial attempts at simulating large-scale transient structures such as coronal mass ejections are also promising [Titov et al., 2008]. However, using solar wind model output to drive magnetospheric models presents a fundamental problem: the magnetosphere is sensitive to both the large-scale structure, which is captured by solar wind models, and small-scale fluctuations which are far below both typical solar wind model spatial and temporal scales [Borovsky and Funsten, 2003; Merkin et al., 2007]. Much of this solar wind “noise,” loosely defined here as fluctuations below the 1 day time scale (shown as the vertical dashed line in Figure 2), is likely the result of stochastic processes such as turbulence [e.g., Horbury et al., 2001; Alexandrova et al., 2009]. Thus, even substantial improvements/developments in the numerics and physics of solar wind models are unlikely to be able to deterministically forecast these structures. As the solar wind noise can change simulated magnetospheric responses by an order of magnitude [Merkin et al., 2007], a qualitatively different approach is required.

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