Estimation and assessment of solar wind propagation time from the Lagrange point L1 to Earth’s bow shock

Abstract

The solar wind (SW) passing the Earth is an important driver of electrodynamic processes in the Earth’s magnetosphere–ionosphere–thermosphere (MIT) system. Since SW observations near Earth (at the bow shock) are very sparse, research and operational applications typically rely on measurements of SW monitors at the Lagrange point L1. The data of these monitors, which provide almost continuous datasets, need to be propagated in time to the bow shock conditions in order to be most useful for MIT studies. The most widely used data source for propagated SW data is provided by OMNIWeb. Near-Earth (NE) SW observations are highly relevant for the validation of the propagated SW estimates. This work uses the NE SW observations to propose a novel method for the estimation of the SW propagation delay. It is based on careful data assessment and a complex combination of correlation analysis and validation metrics. The developed algorithm generates a large dataset of 53,880 events in the period from 22 December 2017 to 30 April 2024, which provides the SW delay along with a list of metrics indicating the quality of the match between the SW structures at L1 and the bow shock. This dataset shows higher reliability in the SW delay estimates than the OMNIWeb data because it focuses on the comparison of structures in the SW. Using the dataset of the period from December 2017 to February 2018, the statistically estimated delay in comparison with the OMNIWeb data reveals that approximately 50% of the delays are computed very accurately with less than 5 min uncertainty, and 80% of the OMNIWeb data delay is reasonably accurate with less than 10 min difference from the statistically estimated delay, providing the best match. However, more than 5% of the OMNIWeb data shows large differences of more than 20 min from the dataset. Thus, it can be concluded that in many cases, the uncertainty in the OMNIWeb delay estimate is larger than the value provided with the data. The generated dataset of SW delay estimates provides an ideal foundation for validating and improving solar wind propagation models.