The interaction between the solar wind and the Earth’s magnetosphere–ionosphere system is very complex, being essentially the result of the interplay between an external driver, the solar wind, and internal processes to the magnetosphere–ionosphere system. In this framework, modelling the Earth’s magnetosphere–ionosphere response to the changes of the solar wind conditions requires a correct identification of the causality relations between the different parameters/quantities used to monitor this coupling. Nowadays, in the framework of complex dynamical systems, both linear statistical tools and Granger causality models drastically fail to detect causal relationships between time series. Conversely, information theory-based concepts can provide powerful model-free statistical quantities capable of disentangling the complex nature of the causal relationships. In this work, we discuss how to deal with the problem of measuring causal information in the solar wind–magnetosphere–ionosphere system. We show that a time delay of about 30–60 min is found between solar wind and magnetospheric and ionospheric overall dynamics as monitored by geomagnetic indices, with a great information transfer observed between the z component of the interplanetary magnetic field and geomagnetic indices, while a lower transfer is found when other solar wind parameters are considered. This suggests that the best candidate for modelling the geomagnetic response to solar wind changes is the interplanetary magnetic field component $B_z$ . A discussion of the relevance of our results in the framework of Space Weather is also provided.

Several techniques have been developed in the last two decades to forecast the occurrence of Solar Proton Events (SPEs), mainly based on the statistical association between the 10 MeV proton flux and precursor parameters. The Empirical model for Solar Proton Events Real Time Alert (ESPERTA; Laurenza et al., 2009) provides a quite good and timely prediction of SPEs after the occurrence of $\geq$M2 soft x-ray (SXR) bursts, by using as input parameters the flare heliolongitude, the SXR and the 1 MHz radio fluence. Here, we reinterpret the ESPERTA model in the framework of machine learning and perform a cross validation, leading to a comparable performance. Moreover, we find that, by applying a cut-off on the M2 flares heliolongitude, the False Alarm Rate (FAR) is reduced. The cut-off is set to where the cumulative distribution of M2 flares associated with SPEs shows a break which reflects the poor magnetic connection between the Earth and eastern hemisphere flares. The best performance is obtained by using the SMOTE algorithm, leading to probability of detection of 0.83 and a FAR of 0.39. Nevertheless, we demonstrate that a relevant FAR on the predictions is a natural consequence of the sample base rates. From a Bayesian point of view, we find that the FAR explicitly contains the prior knowledge about the class distributions. This is a critical issue of any statistical approach, which requires to perform the model validation by preserving the class distributions within the training and test datasets.

The interplanetary magnetic field carried out from the Sun by the solar wind displays fluctuations over a wide range of scales. While at large scales, say at frequencies lower than 0.1–1 Hz, fluctuations display the universal character of fully developed turbulence with a well-defined Kolmogorov-like inertial range, the physical and dynamical properties of the small-scale regime as well as their connection with the large-scale ones are still a debated topic. In this work we investigate the near-Sun magnetic field fluctuations at subproton scales by analyzing the Markov property of fluctuations and recovering basic information about the nature of the energy transfer across different scales. By evaluating the Kramers–Moyal coefficients we find that fluctuations in the subproton range are well described as a Markovian process with Probability Density Functions (PDFs) modeled via a Fokker–Planck (FP) equation. Furthermore, we show that the shape of the PDFs is globally scale-invariant and similar to the one recovered for the stationary solution of the FP equation at different scales. The relevance of our results on the Markovian character of subproton scale fluctuations is also discussed in connection with the occurrence of turbulence in this domain.

This paper presents the project Comprehensive spAce wEather Studies for the ASPIS prototype Realization (CAESAR), which aims to tackle the relevant aspects of Space Weather (SWE) science and develop a prototype of the scientific data centre for Space Weather of the Italian Space Agency (ASI) called ASPIS (ASI SPace Weather InfraStructure). To this end, CAESAR involves the majority of the SWE Italian community, bringing together 10 Italian institutions as partners, and a total of 92 researchers. The CAESAR approach encompasses the whole chain of phenomena from the Sun to Earth up to planetary environments in a multidisciplinary, comprehensive, and unprecedented way. Detailed and integrated studies are being performed on a number of well-observed “target SWE events”, which exhibit noticeable SWE characteristics from several SWE perspectives. CAESAR investigations synergistically exploit a great variety of different products (datasets, codes, models), both long-standing and novel, that will be made available in the ASPIS prototype: this will consist of a relational database (DB), an interface, and a wiki-like documentation structure. The DB will be accessed through both a Web graphical interface and the ASPIS.py module, i.e., a library of functions in Python, which will be available for download and installation. The ASPIS prototype will unify multiple SWE resources through a flexible and adaptable architecture, and will integrate currently available international SWE assets to foster scientific studies and advance forecasting capabilities.

The Parker Solar Probe mission provides a unique opportunity to characterize several features of the solar wind at different heliocentric distances. Recent findings have shown a transition in the inertial range spectral and scaling properties around 0.4–0.5 au when moving away from the Sun. Here we provide, for the first time, how to reconcile these observational results on the radial evolution of the magnetic and velocity field fluctuations with two scenarios drawn from the magnetohydrodynamic theory. The observed breakdown is the result of the radial evolution of magnetic field fluctuations and plasma thermal expansion affecting the distribution between magnetic and velocity fluctuations. The two scenarios point toward an evolving nature of the coupling between fields that can be also reconciled with Kraichnan and Kolmogorov pictures of turbulence. Our findings have important implications for turbulence studies and modeling approaches.

The Empirical model for Solar Proton Events Real Time Alert (ESPERTA) exploits three solar parameters (flare longitude, soft X-ray fluence, and radio fluence) to provide a timely prediction for the occurrence of solar proton events (SPEs, i.e., when the $>$10MeV proton flux is $\geq$10 pfu) after the emission of a $\geq$ M2 flare. In addition, it makes a prediction for the more geoeffective SPEs for which the $>$10 MeV proton flux is $\geq$ 100 pfu. In this paper, we study two different ways to upgrade the ESPERTA model and implement it in real time: 1) by using ground based observations from the LOFAR stations; 2) by applying a novel machine learning algorithm to flare-based parameters to provide early warnings of SPE occurrence together with a fine-tuned radiation storm level. As a last step, we perform a preliminary study using a neural network to forecast the proton flux profile to complement the ESPERTA tool. We evaluate the models over flare and SPE data the last two solar cycles and discuss the performance and the limits and advantages of the three methods.