Timing of the solar wind propagation delay between L1 and Earth based on machine learning

Abstract

Erroneous GNSS positioning, failures in spacecraft operations and power outages due to geomagnetically induced currents are severe threats originating from space weather. Having knowledge of potential impacts on modern society in advance is key for many end-user applications. This covers not only the timing of severe geomagnetic storms but also predictions of substorm onsets at polar latitudes. In this study we aim at contributing to the timing problem of space weather impacts and propose a new method to predict the solar wind propagation delay between Lagrangian point L1 and the Earth based on machine learning, specifically decision tree models. The propagation delay is measured from the identification of interplanetary discontinuities detected by the Advanced Composition Explorer (ACE) and their subsequent sudden commencements in the magnetosphere recorded by ground-based magnetometers. A database of the propagation delay has been constructed on this principle including 380 interplanetary shocks with data ranging from 1998 to 2018. The feature set of the machine learning approach consists of six features, namely the three components of each the solar wind speed and position of ACE around L1. The performance assessment of the machine learning model is examined on the basis of a 10-fold cross-validation. The machine learning results are compared to physics-based models, i.e., the flat propagation delay and the more sophisticated method based on the normal vector of solar wind discontinuities (vector delay). After hyperparameter optimization, the trained gradient boosting (GB) model is the best machine learning model among the tested ones. The GB model achieves an RMSE of 4.5 min with respect to the measured solar wind propagation delay and also outperforms the physical flat and vector delay models by 50 % and 15 % respectively. To increase the confidence in the predictions of the trained GB model, we perform a operational validation, provide drop-column feature importance and analyse the feature impact on the model output with Shapley values. The major advantage of the machine learning approach is its simplicity when it comes to its application. After training, values for the solar wind speed and spacecraft position from only one datapoint have to be fed into the algorithm for a good prediction.

Solar Wind—Magnetosphere Coupling Functions: Pitfalls, Limitations, and Applications

“…Techniques such as these are likely to offer solutions to many of the limitations of traditional coupling function‐terrestrial observation correlation analysis, particularly in the limitations of preconditioning and the effects of the pre‐existing state of the magnetosphere. In addition, application of machine learning techniques should avoid common problems such as overfitting (e.g., Baumann & McCloskey, 2021; Camporeale, 2019). However, other limitations and sources of noise may be unwittingly carried forward into these techniques.…”

On the Considerations of Using Near Real Time Data for Space Weather Hazard Forecasting

“…Space weather forecasts are typically driven by data obtained upstream of the Earth at the L1 point (e.g., Baker et al., 1990; Chu et al., 2021; Forsyth et al., 2020; Keesee et al., 2020; Smith, Forsyth, Rae, Garton, et al., 2021; Wing et al., 2005), approximately 1.5 million km ahead of the Earth. Given the speed of the solar wind, this provides between 20 and 90 min of warning before solar wind plasma encounters the Earth (Baumann & McCloskey, 2021). To account for the variable time delay between the plasma measured at L1 and the arrival of that plasma at the Earth, many forecast models propagate the measurement to a fixed point relative to the Earth, the bow shock for example, (e.g., Baumann & McCloskey, 2021; Cash et al., 2016).…”

“…Given the speed of the solar wind, this provides between 20 and 90 min of warning before solar wind plasma encounters the Earth (Baumann & McCloskey, 2021). To account for the variable time delay between the plasma measured at L1 and the arrival of that plasma at the Earth, many forecast models propagate the measurement to a fixed point relative to the Earth, the bow shock for example, (e.g., Baumann & McCloskey, 2021; Cash et al., 2016). Such methods are present in commonly used scientific datasets, such as the OMNI database (https://omniweb.gsfc.nasa.gov/) (Weimer & King, 2008), and will have implications for data continuity.…”

Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data

“…The study by Nguyen et al (2019) was, however, one of the first to address this topic, and one in a growing number of studies on the application of machine learning in the space sciences. The prediction of the B z component from upstream in situ observations (Reiss et al, 2021), forecasting global geomagnetic activity (Topliff et al, 2020) or the timing of the solar wind propagation delay between the Lagrangian Point L1 and Earth (Baumann & McCloskey, 2021) are just a few examples of how machine learning is being applied in the area of space weather.…”