| 1. |
- Liemohn, Michael W., et al.
(författare)
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Model Evaluation Guidelines for Geomagnetic Index Predictions
- 2018
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Ingår i: Space Weather. - 1542-7390. ; 16:12, s. 2079-2102
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Tidskriftsartikel (refereegranskat)abstract
- Geomagnetic indices are convenient quantities that distill the complicated physics of some region or aspect of near‐Earth space into a single parameter. Most of the best‐known indices are calculated from ground‐based magnetometer data sets, such as Dst, SYM‐H, Kp, AE, AL, and PC. Many models have been created that predict the values of these indices, often using solar wind measurements upstream from Earth as the input variables to the calculation. This document reviews the current state of models that predict geomagnetic indices and the methods used to assess their ability to reproduce the target index time series. These existing methods are synthesized into a baseline collection of metrics for benchmarking a new or updated geomagnetic index prediction model. These methods fall into two categories: (1) fit performance metrics such as root‐mean‐square error and mean absolute error that are applied to a time series comparison of model output and observations and (2) event detection performance metrics such as Heidke Skill Score and probability of detection that are derived from a contingency table that compares model and observation values exceeding (or not) a threshold value. A few examples of codes being used with this set of metrics are presented, and other aspects of metrics assessment best practices, limitations, and uncertainties are discussed, including several caveats to consider when using geomagnetic indices.
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| 2. |
- Wintoft, Peter
(författare)
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Space Weather Physics, Prediction and classification of solar wind structures and geomagnetic activity using artificial neural networks.
- 1997
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis concerns the application of artificial neural network techniques to space weather physics. The networks applied include multi-layer error-backpropagation, radial basis function, and self-organized maps. Different parts in the solar-terrestrial chain are analysed with the emphasis on developing methods for real time predictions of geomagnetic activity. The neural networks are general models which utilize learning algorithms to adjust the free parameters of the models based on data samples. The models used here rely heavily on observations of solar magnetic fields, measurements of solar wind plasma and magnetic fields, and indices of geomagnetic activity. The thesis consists of an introductory part followed by 5 papers. The introduction describes part of the solar-terrestrial physics that is relevant to the papers and includes a summary of the applied neural networks used. Papers I and II describe the application of multi-layer error-backpropagation networks to the solar wind-magnetosphere coupling, where the geomagnetic activity is described by the Dst index. It is shown that real time predictions of the Dst index can be made one hour in advance. Papers III and IV examine the possibility to predict the daily average solar wind velocity from solar magnetic field observations. The model consists of a potential field model describing the solar coronal magnetic fields and a radial basis function neural network for the mapping from the corona to the solar wind. Paper V considers the analysis of hourly average solar wind structures at 1 AU using self-organizing maps. It is found that it is possible to identify specific solar wind events on the self-organized maps that are associated to geomagnetic storms occurring several hours later.
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| 3. |
- Wintoft, Peter
(författare)
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Study of the solar wind coupling to the time difference horizontal geomagnetic field
- 2005
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Ingår i: Annales Geophysicae. - 1432-0576. ; 23:5, s. 1949-1957
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Tidskriftsartikel (refereegranskat)abstract
- The local ground geomagnetic field fluctuations (AB) are dominated by high frequencies and 83% of the power is located at periods of 32 min or less. By forming 10-min root-mean-square (RMS) of A B a major part of this variation is captured. Using measured geomagnetic induced currents (GIC), from a power grid transformer in Southern Sweden, it is shown that the 10-min standard deviation GIC may be computed from a linear model using the RMS AX and Delta Y at Brorfelde (BFE: 11.67 degrees E, 55.63 degrees N), Denmark, and Uppsala (UPS: 17.35 degrees E, 59.90 degrees N), Sweden, with a correlation of 0.926 +/- 0.015. From recurrent neural network models, that are driven by solar wind data, it is shown that the log RMS Delta X and Delta Y at the two locations may be predicted up to 30 min in advance with a correlation close to 0.8: 0.78 +/- 0.02 for both directions at BFE; 0.81 +/- 0.02 and 0.80 +/- 0.02 in the X- and Y-directions, respectively, at UPS. The most important inputs to the models are the 10-min averages of the solar wind magnetic field component B, and velocity V, and the 10-min standard deviation of the proton number density a,. The average proton number density n has no influence.
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