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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

The 2 day solar wind interval used in this study. The observed 64 s ACE solar wind time series is shown in black and as grey-shaded regions, while the model-like time series, obtained from an 8 hour filter of the ACE data, is shown in blue. The downscaled model-like series is shown in red. (top to bottom) The radial (BX) and out-of-ecliptic (BZ) components of the magnetic field in GSE coordinates, followed by the radial solar wind speed, /VX/, the proton density, nP, and the proton temperature, TP.
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fig04: The 2 day solar wind interval used in this study. The observed 64 s ACE solar wind time series is shown in black and as grey-shaded regions, while the model-like time series, obtained from an 8 hour filter of the ACE data, is shown in blue. The downscaled model-like series is shown in red. (top to bottom) The radial (BX) and out-of-ecliptic (BZ) components of the magnetic field in GSE coordinates, followed by the radial solar wind speed, /VX/, the proton density, nP, and the proton temperature, TP.

Mentions: The Advanced Composition Explorer (ACE) spacecraft provides continual in situ measurements of the solar wind in near-Earth space. The entire ACE magnetic field and plasma data set (1998–2011) at 64 s resolution, the spin period of the spacecraft, is used to produce probability distribution functions (PDFs) of ΔX, point-to-point changes in solar wind parameter X (as per Borovsky [2008] and Owens et al. [2011]), in the three components of magnetic field vector, three components of the proton velocity vector, proton density, and temperature. These are shown in Figure 3. From the PDFs, we generate cumulative distribution functions (CDFs). These are used to introduce high-frequency noise to solar wind model time series. To demonstrate and test this process, we select a 2 day interval from 4 January 2000 to 6 January 2000. Two days is chosen as it is a reasonable run time for a magnetospheric simulation. The black line in Figure 4 shows that this interval is rather unremarkable in character though does feature both fast and slow solar wind.


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)

The 2 day solar wind interval used in this study. The observed 64 s ACE solar wind time series is shown in black and as grey-shaded regions, while the model-like time series, obtained from an 8 hour filter of the ACE data, is shown in blue. The downscaled model-like series is shown in red. (top to bottom) The radial (BX) and out-of-ecliptic (BZ) components of the magnetic field in GSE coordinates, followed by the radial solar wind speed, /VX/, the proton density, nP, and the proton temperature, TP.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig04: The 2 day solar wind interval used in this study. The observed 64 s ACE solar wind time series is shown in black and as grey-shaded regions, while the model-like time series, obtained from an 8 hour filter of the ACE data, is shown in blue. The downscaled model-like series is shown in red. (top to bottom) The radial (BX) and out-of-ecliptic (BZ) components of the magnetic field in GSE coordinates, followed by the radial solar wind speed, /VX/, the proton density, nP, and the proton temperature, TP.
Mentions: The Advanced Composition Explorer (ACE) spacecraft provides continual in situ measurements of the solar wind in near-Earth space. The entire ACE magnetic field and plasma data set (1998–2011) at 64 s resolution, the spin period of the spacecraft, is used to produce probability distribution functions (PDFs) of ΔX, point-to-point changes in solar wind parameter X (as per Borovsky [2008] and Owens et al. [2011]), in the three components of magnetic field vector, three components of the proton velocity vector, proton density, and temperature. These are shown in Figure 3. From the PDFs, we generate cumulative distribution functions (CDFs). These are used to introduce high-frequency noise to solar wind model time series. To demonstrate and test this process, we select a 2 day interval from 4 January 2000 to 6 January 2000. Two days is chosen as it is a reasonable run time for a magnetospheric simulation. The black line in Figure 4 shows that this interval is rather unremarkable in character though does feature both fast and slow solar wind.

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