Comparing Information Theory Analysis With Cross‐Correlation and Minimum Variance Analysis of the Solar Wind Structures.

The space weather effects at the Earth's magnetosphere are mostly driven by the solar wind that carries the interplanetary magnetic field (IMF). In this paper, we use 2 years of data in the solar wind from lunar orbiting ARTEMIS and MMS spacecraft upstream of the Earth's bow shock to study the structure of the IMF. We determine the lag times of IMF structures and their dependence on spacecraft positions by conducting an information theory analysis and comparing it with two traditional analysis methods: cross‐correlation (CC) analysis and minimum variance of magnetic field analysis (MVAB). For the events with long time intervals (i.e., >4 hr) and with small‐spatial separation between the MMS and ARTEMIS along the yGSM‐direction (i.e., <40Re, where Re is the Earth's radius), the lag times based on the CC and the mutual information (MI) analyses statistically agree with each other, with p‐values of 1.675 × 10−7 and 4.833 × 10−9, with the confidence of 95%. Both the results based on MI and CC have a large deviation from the results from MVAB. For some of the events, such a deviation could be improved by taking the fast mode speed into account; however, p‐tests showed that they were not statistically significant to the 95% confidence level. Plain Language Summary: Typically, single‐point measurements from the Earth‐Sun Lagrange Point‐1 are used to forecast space weather. However, it is important to understand that multi‐point measurements are required to further understand and predict geo‐effective solar wind structures. Three analysis methods using multi‐point measurements from the MMS and THEMIS missions of large solar wind structures as they move from regions around the Moon to regions around Earth's bow shock, are presented and compared in this paper. Information theory analysis (specifically Mutual Information) determines dependence between two variables by quantifying the entropy (uncertainty) in the two variables. Minimum variance analysis calculates the direction of least change in a magnetic field and assumes the solar wind is propagating in that direction. Lag time is calculated by taking the dot products of this direction with both the displacement vector between the two spacecraft, to get a distance, and the ion velocity (from ARTEMIS) to get a speed. The distance is divided by the speed to get a lag time. Cross‐Correlation analysis is a traditional method for measuring similarity between two variables. This paper shows that information theory analysis along with cross‐correlation analysis could be used successfully for space weather forecasting. Key Points: The solar wind data by MMS and THEMIS are analyzed by using information theory to understand the propagation of solar wind structuresMutual information, linear cross‐correlation, and minimum variance analyses sometimes give different lag‐timesInformation theory analysis along with the traditional method of cross‐correlation analysis could be used successfully for space weather forecasting [ABSTRACT FROM AUTHOR]