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Prediction of <i>D</i><i>s</i><i>t</i> During Solar Minimum Using In Situ Measurements at L5

Rachel Bailey, Christian Möstl, M. A. Reiss, A. J. Weiss, Ute Amerstorfer, Tanja Amerstorfer, Jürgen Hinterreiter, W. Magnes, Roman Leonhardt

2020Space Weather19 citationsDOIOpen Access PDF

Abstract

Abstract Geomagnetic storms resulting from high‐speed streams can have significant negative impacts on modern infrastructure due to complex interactions between the solar wind and geomagnetic field. One measure of the extent of this effect is the Kyoto D s t index. We present a method to predict D s t from data measured at the Lagrange 5 (L5) point, which allows for forecasts of solar wind development 4.5 days in advance of the stream reaching the Earth. Using the STEREO‐B satellite as a proxy, we map data measured near L5 to the near‐Earth environment and make a prediction of the D s t from this point using the Temerin‐Li D s t model enhanced from the original using a machine learning approach. We evaluate the method accuracy with both traditional point‐to‐point error measures and an event‐based validation approach. The results show that predictions using L5 data outperform a 27‐day solar wind persistence model in all validation measures but do not achieve a level similar to an L1 monitor. Offsets in timing and the rapidly changing development of B z in comparison to B x and B y reduce the accuracy. Predictions of D s t from L5 have a root‐mean‐square error of 9 nT, which is double the error of 4 nT using measurements conducted near the Earth. The most useful application of L5 measurements is shown to be in predicting the minimum D s t for the next 4 days. This method is being implemented in a real‐time forecast setting using STEREO‐A as an L5 proxy and has implications for the usefulness of future L5 missions.

Topics & Concepts

Earth's magnetic fieldGeomagnetic stormSolar windMean squared errorProxy (statistics)MeteorologySpace weatherComputer scienceWind speedEnvironmental scienceAlgorithmRemote sensingMathematicsGeologyPhysicsStatisticsMagnetic fieldMachine learningQuantum mechanicsIonosphere and magnetosphere dynamicsSolar and Space Plasma DynamicsGeomagnetism and Paleomagnetism Studies
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