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Title: Improving Empirical Magnetic Field Models by Fitting to In Situ Data Using an Optimized Parameter Approach

Abstract

A method for comparing and optimizing the accuracy of empirical magnetic field models using in situ magnetic field measurements is presented in this paper. The optimization method minimizes a cost function—τ—that explicitly includes both a magnitude and an angular term. A time span of 21 days, including periods of mild and intense geomagnetic activity, was used for this analysis. A comparison between five magnetic field models (T96, T01S, T02, TS04, and TS07) widely used by the community demonstrated that the T02 model was, on average, the most accurate when driven by the standard model input parameters. The optimization procedure, performed in all models except TS07, generally improved the results when compared to unoptimized versions of the models. Additionally, using more satellites in the optimization procedure produces more accurate results. This procedure reduces the number of large errors in the model, that is, it reduces the number of outliers in the error distribution. The TS04 model shows the most accurate results after the optimization in terms of both the magnitude and direction, when using at least six satellites in the fitting. It gave a smaller error than its unoptimized counterpart 57.3% of the time and outperformed the best unoptimized model (T02)more » 56.2% of the time. Its median percentage error in |B| was reduced from 4.54% to 3.84%. Finally, the difference among the models analyzed, when compared in terms of the median of the error distributions, is not very large. However, the unoptimized models can have very large errors, which are much reduced after the optimization.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]
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  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE; LANL Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1416290
Alternate Identifier(s):
OSTI ID: 1414008
Report Number(s):
LA-UR-17-27140
Journal ID: ISSN 1542-7390
Grant/Contract Number:  
AC52-06NA25396
Resource Type:
Accepted Manuscript
Journal Name:
Space Weather
Additional Journal Information:
Journal Volume: 15; Journal Issue: 12; Journal ID: ISSN 1542-7390
Publisher:
American Geophysical Union
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; 58 GEOSCIENCES; Heliospheric and Magnetospheric Physics

Citation Formats

Brito, Thiago V., and Morley, Steven K. Improving Empirical Magnetic Field Models by Fitting to In Situ Data Using an Optimized Parameter Approach. United States: N. p., 2017. Web. doi:10.1002/2017SW001702.
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Brito, Thiago V., & Morley, Steven K. Improving Empirical Magnetic Field Models by Fitting to In Situ Data Using an Optimized Parameter Approach. United States. https://doi.org/10.1002/2017SW001702
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Brito, Thiago V., and Morley, Steven K. Wed . "Improving Empirical Magnetic Field Models by Fitting to In Situ Data Using an Optimized Parameter Approach". United States. https://doi.org/10.1002/2017SW001702. https://www.osti.gov/servlets/purl/1416290.
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@article{osti_1416290,
title = {Improving Empirical Magnetic Field Models by Fitting to In Situ Data Using an Optimized Parameter Approach},
author = {Brito, Thiago V. and Morley, Steven K.},
abstractNote = {A method for comparing and optimizing the accuracy of empirical magnetic field models using in situ magnetic field measurements is presented in this paper. The optimization method minimizes a cost function—τ—that explicitly includes both a magnitude and an angular term. A time span of 21 days, including periods of mild and intense geomagnetic activity, was used for this analysis. A comparison between five magnetic field models (T96, T01S, T02, TS04, and TS07) widely used by the community demonstrated that the T02 model was, on average, the most accurate when driven by the standard model input parameters. The optimization procedure, performed in all models except TS07, generally improved the results when compared to unoptimized versions of the models. Additionally, using more satellites in the optimization procedure produces more accurate results. This procedure reduces the number of large errors in the model, that is, it reduces the number of outliers in the error distribution. The TS04 model shows the most accurate results after the optimization in terms of both the magnitude and direction, when using at least six satellites in the fitting. It gave a smaller error than its unoptimized counterpart 57.3% of the time and outperformed the best unoptimized model (T02) 56.2% of the time. Its median percentage error in |B| was reduced from 4.54% to 3.84%. Finally, the difference among the models analyzed, when compared in terms of the median of the error distributions, is not very large. However, the unoptimized models can have very large errors, which are much reduced after the optimization.},
doi = {10.1002/2017SW001702},
journal = {Space Weather},
number = 12,
volume = 15,
place = {United States},
year = {Wed Oct 25 00:00:00 EDT 2017},
month = {Wed Oct 25 00:00:00 EDT 2017}
}
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