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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

Magnetospheric response to different solar wind time series. Black, blue, and red lines show the observed, undownscaled, and downscaled model-like time series, respectively. (top) The magnetopause standoff distance at local noon (RMP). (bottom) Ionospheric Joule heating computed from radial current and electric potential (JRφ. Solid (dashed) horizontal lines show the observed 50th (90th) percentile values.
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fig05: Magnetospheric response to different solar wind time series. Black, blue, and red lines show the observed, undownscaled, and downscaled model-like time series, respectively. (top) The magnetopause standoff distance at local noon (RMP). (bottom) Ionospheric Joule heating computed from radial current and electric potential (JRφ. Solid (dashed) horizontal lines show the observed 50th (90th) percentile values.

Mentions: This process could be applied to any metric of magnetospheric disturbance, ideally chosen to specifically emphasize the desired forecasting application. In this study we use two simple diagnostics of the global magnetospheric state, which are routinely calculated as part of the CCMC's simulation runs, namely, the magnetopause standoff distance at local noon, RMP, and ionospheric Joule heating computed from radial current and electric potential, JRφ. For the purposes of demonstrating the downscaling validation process, the details of these properties are somewhat irrelevant; what is important is how these parameters compare between runs using the observed solar wind, undownscaled model-like, and downscaled model-like time series, shown as black, blue, and red lines in Figure 5, respectively.


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)

Magnetospheric response to different solar wind time series. Black, blue, and red lines show the observed, undownscaled, and downscaled model-like time series, respectively. (top) The magnetopause standoff distance at local noon (RMP). (bottom) Ionospheric Joule heating computed from radial current and electric potential (JRφ. Solid (dashed) horizontal lines show the observed 50th (90th) percentile values.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig05: Magnetospheric response to different solar wind time series. Black, blue, and red lines show the observed, undownscaled, and downscaled model-like time series, respectively. (top) The magnetopause standoff distance at local noon (RMP). (bottom) Ionospheric Joule heating computed from radial current and electric potential (JRφ. Solid (dashed) horizontal lines show the observed 50th (90th) percentile values.
Mentions: This process could be applied to any metric of magnetospheric disturbance, ideally chosen to specifically emphasize the desired forecasting application. In this study we use two simple diagnostics of the global magnetospheric state, which are routinely calculated as part of the CCMC's simulation runs, namely, the magnetopause standoff distance at local noon, RMP, and ionospheric Joule heating computed from radial current and electric potential, JRφ. For the purposes of demonstrating the downscaling validation process, the details of these properties are somewhat irrelevant; what is important is how these parameters compare between runs using the observed solar wind, undownscaled model-like, and downscaled model-like time series, shown as black, blue, and red lines in Figure 5, respectively.

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