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T H Zurbuchen - One of the best experts on this subject based on the ideXlab platform.
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analysis of high cadence in situ solar wind Ionic Composition data using wavelet power spectra confidence levels
Astrophysical Journal Supplement Series, 2013Co-Authors: J K Edmondson, B J Lynch, S T Lepri, T H ZurbuchenAbstract:The variability inherent in solar wind Composition has implications for the variability of the physical conditions in its coronal source regions, providing constraints on models of coronal heating and solar wind generation. We present a generalized prescription for constructing a wavelet power significance measure (confidence level) for the purpose of characterizing the effects of missing data in high cadence solar wind Ionic Composition measurements. We describe the data gaps present in the 12 minute Advanced Composition Explorer/Solar Wind Ionic Composition Spectrometer observations of O7 +/O6 + during 2008. The deComposition of the in situ observations into good measurement and no-measurement signals allows us to evaluate the performance of a filler signal, i.e., various prescriptions for filling the data gaps. We construct Monte Carlo simulations of synthetic O7 +/O6 + Composition data and impose the actual data gaps that exist in the observations in order to investigate two different filler signals: one, a linear interpolation between neighboring good data points, and two, the constant mean value of the measured data. Applied to these synthetic data plus filler signal combinations, we quantify the ability of the power spectra significance level procedure to reproduce the ensemble-averaged time-integrated wavelet power per scale of an ideal case, i.e., the synthetic data without imposed data gaps. Finally, we present the wavelet power spectra for the O7 +/O6 + data using the confidence levels derived from both the mean value and linear interpolation data gap filling signals and discuss the results.
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analysis of high cadence in situ solar wind Ionic Composition data using wavelet power spectra confidence levels
arXiv: Solar and Stellar Astrophysics, 2013Co-Authors: J K Edmondson, B J Lynch, S T Lepri, T H ZurbuchenAbstract:The variability inherent in solar wind Composition has implications for the variability of the physical conditions in its coronal source regions, providing constraints on models of coronal heating and solar wind generation. We present a generalized prescription for constructing a wavelet power significance measure (confidence level) for the purpose of characterizing the effects of missing data in high cadence solar wind Ionic Composition measurements. We describe the data gaps present in the 12-minute ACE/SWICS observations of O7+/O6+ during 2008. The deComposition of the in-situ observations into a `good measurement' and a `no measurement' signal allows us to evaluate the performance of a filler signal, i.e., various prescriptions for filling the data gaps. We construct Monte Carlo simulations of synthetic O7+/O6+ Composition data and impose the actual data gaps that exist in the observations in order to investigate two different filler signals: one, a linear interpolation between neighboring good data points, and two, the constant mean value of the measured data. Applied to these synthetic data plus filler signal combinations, we quantify the ability of the power spectra significance level procedure to reproduce the ensemble-averaged time-integrated wavelet power per scale of an ideal case, i.e. the synthetic data without imposed data gaps. Finally, we present the wavelet power spectra for the O7+/O6+ data using the confidence levels derived from both the Mean Value and Linear Interpolation data gap filling signals and discuss the results.
B J Lynch - One of the best experts on this subject based on the ideXlab platform.
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analysis of high cadence in situ solar wind Ionic Composition data using wavelet power spectra confidence levels
Astrophysical Journal Supplement Series, 2013Co-Authors: J K Edmondson, B J Lynch, S T Lepri, T H ZurbuchenAbstract:The variability inherent in solar wind Composition has implications for the variability of the physical conditions in its coronal source regions, providing constraints on models of coronal heating and solar wind generation. We present a generalized prescription for constructing a wavelet power significance measure (confidence level) for the purpose of characterizing the effects of missing data in high cadence solar wind Ionic Composition measurements. We describe the data gaps present in the 12 minute Advanced Composition Explorer/Solar Wind Ionic Composition Spectrometer observations of O7 +/O6 + during 2008. The deComposition of the in situ observations into good measurement and no-measurement signals allows us to evaluate the performance of a filler signal, i.e., various prescriptions for filling the data gaps. We construct Monte Carlo simulations of synthetic O7 +/O6 + Composition data and impose the actual data gaps that exist in the observations in order to investigate two different filler signals: one, a linear interpolation between neighboring good data points, and two, the constant mean value of the measured data. Applied to these synthetic data plus filler signal combinations, we quantify the ability of the power spectra significance level procedure to reproduce the ensemble-averaged time-integrated wavelet power per scale of an ideal case, i.e., the synthetic data without imposed data gaps. Finally, we present the wavelet power spectra for the O7 +/O6 + data using the confidence levels derived from both the mean value and linear interpolation data gap filling signals and discuss the results.
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analysis of high cadence in situ solar wind Ionic Composition data using wavelet power spectra confidence levels
arXiv: Solar and Stellar Astrophysics, 2013Co-Authors: J K Edmondson, B J Lynch, S T Lepri, T H ZurbuchenAbstract:The variability inherent in solar wind Composition has implications for the variability of the physical conditions in its coronal source regions, providing constraints on models of coronal heating and solar wind generation. We present a generalized prescription for constructing a wavelet power significance measure (confidence level) for the purpose of characterizing the effects of missing data in high cadence solar wind Ionic Composition measurements. We describe the data gaps present in the 12-minute ACE/SWICS observations of O7+/O6+ during 2008. The deComposition of the in-situ observations into a `good measurement' and a `no measurement' signal allows us to evaluate the performance of a filler signal, i.e., various prescriptions for filling the data gaps. We construct Monte Carlo simulations of synthetic O7+/O6+ Composition data and impose the actual data gaps that exist in the observations in order to investigate two different filler signals: one, a linear interpolation between neighboring good data points, and two, the constant mean value of the measured data. Applied to these synthetic data plus filler signal combinations, we quantify the ability of the power spectra significance level procedure to reproduce the ensemble-averaged time-integrated wavelet power per scale of an ideal case, i.e. the synthetic data without imposed data gaps. Finally, we present the wavelet power spectra for the O7+/O6+ data using the confidence levels derived from both the Mean Value and Linear Interpolation data gap filling signals and discuss the results.
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Ionic Composition structure of coronal mass ejections in axisymmetric magnetohydrodynamic models
The Astrophysical Journal, 2011Co-Authors: B J Lynch, A A Reinard, Tamitha Mulligan, Katharine K Reeves, Cara E Rakowski, J C Allred, J M Laming, P Macneice, Jon A LinkerAbstract:We present the Ionic charge state Composition structure derived from axisymmetric MHD simulations of coronal mass ejections (CMEs), initiated via the flux-cancellation and magnetic breakout mechanisms. The flux-cancellation CME simulation is run on the Magnetohydrodynamics-on-A-Sphere code developed at Predictive Sciences, Inc., and the magnetic breakout CME simulation is run on ARC7 developed at NASA GSFC. Both MHD codes include field-aligned thermal conduction, radiative losses, and coronal heating terms which make the energy equations sufficient to calculate reasonable temperatures associated with the steady-state solar wind and model the eruptive flare heating during CME formation and eruption. We systematically track a grid of Lagrangian plasma parcels through the simulation data and calculate the coronal density and temperature history of the plasma in and around the CME magnetic flux ropes. The simulation data are then used to integrate the continuity equations for the Ionic charge states of several heavy ion species under the assumption that they act as passive tracers in the MHD flow. We construct two-dimensional spatial distributions of commonly measured Ionic charge state ratios in carbon, oxygen, silicon, and iron that are typically elevated in interplanetary coronal mass ejection (ICME) plasma. We find that the slower CME eruption has relatively enhanced Ionic charge states and the faster CME eruption shows basically no enhancement in charge states—which is the opposite trend to what is seen in the in situ ICME observations. The primary cause of the difference in the Ionic charge states in the two simulations is not due to the different CME initiation mechanisms per se. Rather, the difference lies in their respective implementation of the coronal heating which governs the steady-state solar wind, density and temperature profiles, the duration of the connectivity of the CME to the eruptive flare current sheet, and the contribution of the flare-heated plasma associated with the reconnection jet outflow into the ejecta. Despite the limitations inherent in the first attempt at this novel procedure, the simulation results provide strong evidence in support of the conclusion that enhanced heavy ion charge states within CMEs are a direct consequence of flare heating in the low corona. We also discuss future improvements through combining numerical CME modeling with quantitative Ionic charge state calculations.
J K Edmondson - One of the best experts on this subject based on the ideXlab platform.
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analysis of high cadence in situ solar wind Ionic Composition data using wavelet power spectra confidence levels
Astrophysical Journal Supplement Series, 2013Co-Authors: J K Edmondson, B J Lynch, S T Lepri, T H ZurbuchenAbstract:The variability inherent in solar wind Composition has implications for the variability of the physical conditions in its coronal source regions, providing constraints on models of coronal heating and solar wind generation. We present a generalized prescription for constructing a wavelet power significance measure (confidence level) for the purpose of characterizing the effects of missing data in high cadence solar wind Ionic Composition measurements. We describe the data gaps present in the 12 minute Advanced Composition Explorer/Solar Wind Ionic Composition Spectrometer observations of O7 +/O6 + during 2008. The deComposition of the in situ observations into good measurement and no-measurement signals allows us to evaluate the performance of a filler signal, i.e., various prescriptions for filling the data gaps. We construct Monte Carlo simulations of synthetic O7 +/O6 + Composition data and impose the actual data gaps that exist in the observations in order to investigate two different filler signals: one, a linear interpolation between neighboring good data points, and two, the constant mean value of the measured data. Applied to these synthetic data plus filler signal combinations, we quantify the ability of the power spectra significance level procedure to reproduce the ensemble-averaged time-integrated wavelet power per scale of an ideal case, i.e., the synthetic data without imposed data gaps. Finally, we present the wavelet power spectra for the O7 +/O6 + data using the confidence levels derived from both the mean value and linear interpolation data gap filling signals and discuss the results.
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analysis of high cadence in situ solar wind Ionic Composition data using wavelet power spectra confidence levels
arXiv: Solar and Stellar Astrophysics, 2013Co-Authors: J K Edmondson, B J Lynch, S T Lepri, T H ZurbuchenAbstract:The variability inherent in solar wind Composition has implications for the variability of the physical conditions in its coronal source regions, providing constraints on models of coronal heating and solar wind generation. We present a generalized prescription for constructing a wavelet power significance measure (confidence level) for the purpose of characterizing the effects of missing data in high cadence solar wind Ionic Composition measurements. We describe the data gaps present in the 12-minute ACE/SWICS observations of O7+/O6+ during 2008. The deComposition of the in-situ observations into a `good measurement' and a `no measurement' signal allows us to evaluate the performance of a filler signal, i.e., various prescriptions for filling the data gaps. We construct Monte Carlo simulations of synthetic O7+/O6+ Composition data and impose the actual data gaps that exist in the observations in order to investigate two different filler signals: one, a linear interpolation between neighboring good data points, and two, the constant mean value of the measured data. Applied to these synthetic data plus filler signal combinations, we quantify the ability of the power spectra significance level procedure to reproduce the ensemble-averaged time-integrated wavelet power per scale of an ideal case, i.e. the synthetic data without imposed data gaps. Finally, we present the wavelet power spectra for the O7+/O6+ data using the confidence levels derived from both the Mean Value and Linear Interpolation data gap filling signals and discuss the results.
S T Lepri - One of the best experts on this subject based on the ideXlab platform.
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analysis of high cadence in situ solar wind Ionic Composition data using wavelet power spectra confidence levels
Astrophysical Journal Supplement Series, 2013Co-Authors: J K Edmondson, B J Lynch, S T Lepri, T H ZurbuchenAbstract:The variability inherent in solar wind Composition has implications for the variability of the physical conditions in its coronal source regions, providing constraints on models of coronal heating and solar wind generation. We present a generalized prescription for constructing a wavelet power significance measure (confidence level) for the purpose of characterizing the effects of missing data in high cadence solar wind Ionic Composition measurements. We describe the data gaps present in the 12 minute Advanced Composition Explorer/Solar Wind Ionic Composition Spectrometer observations of O7 +/O6 + during 2008. The deComposition of the in situ observations into good measurement and no-measurement signals allows us to evaluate the performance of a filler signal, i.e., various prescriptions for filling the data gaps. We construct Monte Carlo simulations of synthetic O7 +/O6 + Composition data and impose the actual data gaps that exist in the observations in order to investigate two different filler signals: one, a linear interpolation between neighboring good data points, and two, the constant mean value of the measured data. Applied to these synthetic data plus filler signal combinations, we quantify the ability of the power spectra significance level procedure to reproduce the ensemble-averaged time-integrated wavelet power per scale of an ideal case, i.e., the synthetic data without imposed data gaps. Finally, we present the wavelet power spectra for the O7 +/O6 + data using the confidence levels derived from both the mean value and linear interpolation data gap filling signals and discuss the results.
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analysis of high cadence in situ solar wind Ionic Composition data using wavelet power spectra confidence levels
arXiv: Solar and Stellar Astrophysics, 2013Co-Authors: J K Edmondson, B J Lynch, S T Lepri, T H ZurbuchenAbstract:The variability inherent in solar wind Composition has implications for the variability of the physical conditions in its coronal source regions, providing constraints on models of coronal heating and solar wind generation. We present a generalized prescription for constructing a wavelet power significance measure (confidence level) for the purpose of characterizing the effects of missing data in high cadence solar wind Ionic Composition measurements. We describe the data gaps present in the 12-minute ACE/SWICS observations of O7+/O6+ during 2008. The deComposition of the in-situ observations into a `good measurement' and a `no measurement' signal allows us to evaluate the performance of a filler signal, i.e., various prescriptions for filling the data gaps. We construct Monte Carlo simulations of synthetic O7+/O6+ Composition data and impose the actual data gaps that exist in the observations in order to investigate two different filler signals: one, a linear interpolation between neighboring good data points, and two, the constant mean value of the measured data. Applied to these synthetic data plus filler signal combinations, we quantify the ability of the power spectra significance level procedure to reproduce the ensemble-averaged time-integrated wavelet power per scale of an ideal case, i.e. the synthetic data without imposed data gaps. Finally, we present the wavelet power spectra for the O7+/O6+ data using the confidence levels derived from both the Mean Value and Linear Interpolation data gap filling signals and discuss the results.
Skule Strand - One of the best experts on this subject based on the ideXlab platform.
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water based enhanced oil recovery eor by smart water optimal Ionic Composition for eor in carbonates
Energy & Fuels, 2011Co-Authors: Seyed Jafar Fathi, Tor Austad, Skule StrandAbstract:Reprinted (adapted) with permission from Fathi, S J., Austad, T., Strand S. (2011) Water-Based Enhanced Oil Recovery (EOR) by “Smart Water”: Optimal Ionic Composition for EOR in Carbonates. Energy & Fuels 25(11), pp. 5173–5179. Copyright (2011) American Chemical Society. The article forms part of the Jafar Fathi's PhD thesis : Fathi, J. (2011) Water-Based Enhanced Oil Recovery (EOR) by "Smart Water" in Carbonate Reservoirs. University of Stavanger.
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smart water as a wettability modifier in chalk the effect of salinity and Ionic Composition
Energy & Fuels, 2010Co-Authors: Seyed Jafar Fathi, Tor Austad, Skule StrandAbstract:"Reprinted (adapted) with permission from Fathi, S J., Austad, T., Strand S. (2010) “Smart water” as a wettability modifier in chalk: The effect of salinity and Ionic Composition. Energy & Fuels 24(4), pp. 2514–2519. Copyright 2010 American Chemical Society. The article forms part of Jafar Fathi's PhD thesis: Water-based enhanced oil recovery (EOR) in carbonate reservoirs : initial wetting condition and wettability alteration by "Smart Water", Stavanger : University of Stavanger
