Aims. Here we aim to separate the two main contributions of slow and fast solar wind that appear at 1 AU. Methods. The Bi-Gaussian function is proposed as the probability distribution function of the two main components of the solar wind. The positions of the peaks of every simple Gaussian curve are associated with the typical values of every contribution to solar wind. We used the entire data set from the Advanced Composition Explorer (ACE) mission in an analysis of the data set as a whole and as yearly series. Solar cycle dependence is considered to provide more accurate results for the typical values of the different parameters.Results. The distribution of the solar wind at 1 AU is clearly bimodal, not only for velocity, but also for proton density, temperature and magnetic field. New typical values for the main parameters of the slow and fast components of the solar wind at 1 AU are proposed.

Scientific progression in the last decades has made modern society more dependent on technology. Due to the interdependence between different components of technological infrastructure, a severe space weather event could cause a cascading effect in different aspects of modern life, from disruption in electric power grids to spacecraft malfunction and navigation problems. For example, one of the largest magnetic storms of the last century, occurring in March 1989, caused widespread effects in the Hydro-Québec power system in Canada (see e.g., Boteler, 2019). Riley et al. (2018) state that the cost of a worst-case scenario 1-in-100 years magnetic storm would include: (a) 1-2 Trillion USD dollars of economic loss; and (b) 130 million people without electrical power for several years, based on the destruction of several hundred transformers.The main driver of geomagnetic storms is the solar wind; hence, knowledge of the most severe disturbances in the solar wind is essential to both forecast and to potentially mitigate risks related to space weather events. Extreme value theory (EVT) is a statistical method developed to analyze the likelihood of occurrence of rare and severe events (see Coles, 2001; Gumbel, 1958 and references therein). This theory has been applied in different fields, from hydrology and meteorology (see e.g., Gumbel, 1958) to finance (Embrechts & Schmidli, 1994) and public health (Thomas et al., 2016). In recent decades, EVT has been applied to estimate extreme values in different aspects of solar physics and space weather. In particular, extreme value analysis has been applied to the study of extreme geomagnetic storms (

<p>The launch of new spacecraft such as Parker Solar Probe or Solar Orbiter allow us to measure in-situ at different radial distances the physical magnitudes of ICMEs. With that, we are able to quantify the evolution of ICMEs and their substructures at a specific radial distance in order to better understand the interaction processes that occur with the background solar wind.<br />Using multiple spacecraft covering the inner heliosphere, we extract plasma and magnetic field parameters from several ICMEs to relate the physical processes responsible for the formation of the different substructures. We present ICME case studies that prepare for a large statistical analysis.</p>

<p>Society’s dependence on technology has increased during the past years. Therefore, understanding the hazardous events including space weather events that lead to technological problems is now critical. As solar wind is the driver of space weather, identifying extreme solar wind is important. In this work extreme value theory is used to characterize the solar wind parameters most relevant to space weather: interplanetary magnetic field strength and proton speed. This is done using an extreme value distribution for all data above a certain threshold for each parameter. Analysis demonstrates that these thresholds are around 900 km/s for the proton speed and around 95 nT for the interplanetary magnetic field. Based on 20 years of solar wind data, we made an estimation for the interplanetary magnetic field and solar wind proton speed with return periods corresponding to 4 and 6 solar cycles with a 99% confidence interval.</p>

<p>The main goal of this work is to separate the behavior of the two types of quiet solar wind at 1 AU: fast and slow.<br>Our approach is a bi-Gaussian distribution function, formed by the addition of two Gaussian distribution functions, where each one represents one type of wind. We check our approach by fitting the bi-Gaussian to data from ACE spacecraft. We use level 2 data measured during solar cycles 23 and 24 of different solar wind parameters, including proton speed, proton temperature, density and magnetic field. Our results show that the approach is fine and only transient events departs from the proposed function. Moreover, we can show bi modal behavior of the solar wind at 1 AU, not only for the proton speed, but also for the other analyzed parameters. We also check the solar cycle dependence of the different fitting parameters.</p>