| Popis: |
Having precise knowledge of the near-Earth solar wind (SW) and the embedded interplanetary magnetic field (IMF) is of critical importance to space weather operation due to the usage of SW and IMF in almost all magnetospheric and ionospheric models. The most widely used data source, OMNI, propagates SW properties from Lagrangian point L1 to the Earth’s bow shock by estimating the propagation time of the SW. However, the time difference between OMNI timeshifted IMF and the best match-up of IMF can reach ˜15 min. Firstly, we aim to develop an improved statistical algorithm to contribute to the SW propagation delay problem of space weather prediction. The algorithm focuses on matching SW features around the L1 point and upstream of the bow shock by computing the variance, cross-correlation coefficient, the plateau-shaped magnitude index, and the non-dimensional measure of average error index between the measurements at the two locations. The obtained propagation times are then compared to OMNI. Factors that limit the OMNI accuracy are also examined. Secondly, the automatic algorithm allows us to generate large sets of input and target variables using multiple spacecraft pairs at L1 and near-Earth locations to train, validate, and test machine learning models to specify and forecast near-Earth SW conditions. Finally, we offer a machine learning (ML) approach to specify and predict the propagation time from L1 monitors to a given location upstream or at the bow shock and forecast near-Earth SW conditions with the gradient boosting and random forest prediction models in the form of an ensemble of decision trees. |
