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The solar storms happened in early August 1972 have been recently described as an example of a space weather event due to its technological impacts. Besides the interest of this event for the space weather community, these storms were extensively analyzed by the scientific community in 1970s and 80s due to their relevance considering the enhanced levels of solar activity and also their interplanetary consequences. The passage of several interplanetary shocks was observed in Pioneers 9 and 10 data and also in Pognoz-1, Prognoz-2 and HEOS-2 data, making this event particularly suitable for the study of the evolution of the solar ejections in the Heliosphere. This work starts reviewing the papers on the interplanetary scenario during this event. Then, we revisit their conclusions considering the scientific advances since that date and finally go ahead in the analysis of the event.

Geomagnetic indices quantify the disturbance caused by the solar activity in particular regions of the Earth. Among them, the SYM-H and ASY-H indices represent the (longitudinally) symmetric and asymmetric geomagnetic disturbance of the horizontal component of the magnetic field at mid-latitude with a 1-minute resolution. Their resolution, along with their relation to the solar wind parameters, makes the forecasting of the geomagnetic indices a problem that can be addressed through the use of Deep Learning, particularly using Deep Neural Networks (DNN). In this work, we present two DNNs developed to forecast the SYM-H and ASY-H indices. Both networks have been trained using solar wind data from the last two solar cycles and they are able to accurately forecast the indices two hours in advance, considering the solar wind and indices values for the previous 16 hours. The evaluation of both networks reveals a great precision for the forecasting, including good predictions for large storms that occurred during the solar cycle 23.

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.

Mid latitudes around 40 degree are influenced by effects typically found at both high and low latitudes. Moreover, the focus of the Solar Quiet ionospheric current system, drifts around these mid-latitudes. Consequently they have been considered as a complicated place to infer the geospace state from the ground and also complicated for practical procedures to generate geomagnetic indices. The procedure designed at the University of Alcala specially focused on removing solar regular variations at mid-latitudes is delivering a geomagnetic Local Disturbance index (LDi) in realtime. The same procedure can be used to produce global geomagnetic indices when applied to several geomagnetic stations at these latitudes. We present in this work the high-resolution (one minute) realtime production of ring current and auroral indices (MID-R, MID-E, MID-U and MID-L) similar to the well known Dst and AE indices for mid-latitudes which will help in the understanding of the complex physical processes that emerge at these latitudes. At the same time they fill a gap in the current operational space weather products available for these latitudes.

The boundaries of interplanetary coronal mass ejections (ICMEs) are commonly established based on the magnetic field smoothness and/or the low temperature, when compared to normal solar wind. Based on the analysis of the ICME on 2015 January 6-7, observed by Wind and ACE spacecraft, Cid et al. (2016) proposed compositional signatures as the most precise diagnostic tool for the boundaries of ICMEs. Having as a starting point the ICMEs catalogues from Jian et al. (2006) and Richardson and Cane (2010), and the Wind spacecraft ICME catalogue on the NASA web site, we have compared the boundaries of all ICMEs observed by the ACE spacecraft attending to different signatures. This contribution shows the results of the study.

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.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.

SMOS is an Earth observing satellite that is been adapted to provide full polarization observations of the Sun at 1.4 GHz 24 hours a day. Its solar radio observations from the last decade will be released to the community by the middle of this year. In this presentation we show the capabilities of SMOS as a solar radio observatory and compare some of the most relevant radio bursts with data from GOES, LASCO, SDO and RSTN. We show how SMOS responds to different kinds of solar flares depending on their x-ray flux, and the kind of mass ejection or solar dimming that they have produced, if any. In addition to this we also show the potential of SMOS as a space weather tool to monitor GNSS satellites signal fades and to provide an early warning of Earth-directed coronal mass ejections.

Geomagnetic storms, as part of the Sun-Earth relations, are continuously monitored with different indices and scales. These indices usually have some associated scale thresholds to quantify the severity or risk of geomagnetic disturbances. However, the most usual scale thresholds are arbitrarily chosen. In this work we aim to quantify the range of the thresholds through a new method. These new thresholds are based on statistical distribution fitting. We used different geomagnetic indices, as Dst, SYM-H, and Kp, since they are relevant for space weather purposes. The first two indices have been discriminated between their negative values and the whole dataset. We considered two periods: a short-term one, comprising data from 1997 to 2012; and long-term ones, which are from 1957–2012 for Dst and 1932–2012 for Kp. We look for the best fit for different statistical continuous distributions applied to these indices. The best fits and the data distribution functions yield to intersects that can be used to define thresholds. The best fit distribution functions are more coincidental between them when considering determined similar datasets, as non-central f-distribution for negative values, meaningful for geomagnetic disturbances; or non-central Student's-t, when the whole dataset is taken. The method yields different values for thresholds depending on the index. Thresholds for geomagnetic storms can be chosen by common values of SYM-H and Dst, as −75 nT for moderate storms; −150 nT for intense storms, and −330 nT for extreme storms. For the case of Kp, the value equal to 5 may mark the departure from quiet time to stormy time. The obtained values are close to those usually considered as thresholds for, typically, Dst and Kp; therefore the thresholds defined here may provide criteria to assess the vulnerability to geomagnetic activity on design or mitigation purposes.

We analyze two magnetic clouds (MCs) observed in different points of the heliosphere. The main aim of the present study is to provide a link between the different aspects of this phenomenon, starting with information on the origins of the MCs at the Sun and following by the analysis of in-situ observations at 1AU and at Ulysses. The candidate source regions were identified in SOHO/EIT and SOHO/MDI observations. They were correlated with H-alfa images that were obtained from ground-based observatories. Hints on the internal magnetic field configuration of the associated coronal mass ejections are obtained from LASCO C2 images. In interplanetary space, magnetic and plasma moments of the distribution function of plasma species (ACE/Ulysses) were analyzed together with information on the plasma composition, and the results were compared between both spacecraft in order to understand how these structures interact and evolve in their cruise from the Sun to 5 AU. Additionally, estimates of global magnitudes of magnetic fluxes and helicity were obtained from magnetic field models applied to the data in interplanetary space. We have found that these magnetic characteristics were well kept from their solar source, up to 5 AU where Ulysses provided valuable information which, together with that obtained from ACE, can help to reinforce the correct matching of solar events and their interplanetary counterparts., Ministerio de Educación y Cultura