Bimodal distribution of the solar wind at 1 AU

Larrodera, Carlos; Cid, C.

doi:10.1051/0004-6361/201937307

Cited by 22 publications

(19 citation statements)

References 23 publications

(35 reference statements)

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“…Larrodera and Cid (2020) find using Advanced Composition Explorer (ACE) data that the main solar wind parameters are reproduced by a bi‐Gaussian function showing that the bulk solar wind at 1 AU is bimodal. This disagrees with the notion that the solar wind has a statistical structure resulting from the dynamic evolution and interaction of flows near 1 AU.…”

Section: Resultsmentioning

confidence: 99%

A K‐Means Clustering Analysis of the Jovian and Terrestrial Magnetopauses: A Technique to Classify Global Magnetospheric Behavior

et al. 2020

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Previous studies suggested that the Jovian magnetopause position is best characterized as a bimodal distribution indicative of a two‐state system. We applied a modified k‐means clustering analysis to observations of the Jovian and terrestrial magnetopause crossings. Clustering analysis using pre‐Juno data shows that the Jovian magnetopause is best described as a two‐state system, whereas the terrestrial magnetopause is best described by a single state, indicating that the behavior of the Jovian magnetopause is fundamentally different from that of the terrestrial magnetopause. We also analyzed magnetopause locations detected by the Juno spacecraft and conclude that their distribution is consistent with a two‐state system. The origin of the different number of Jovian and terrestrial magnetopause states may be due to the disparate source strengths supplying the two magnetospheres. The Jovian magnetosphere is supplied with about a metric ton of Iogenic heavy ions per second, whereas the terrestrial source, Earth's ionosphere, is two orders of magnitude weaker. A suggestive correlation between Io phase and magnetopause state may be consistent with this proposed link between an internal source and a two‐state magnetopause. The possible role of energetic magnetospheric events and Jovian current systems is discussed.

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Section: Resultsmentioning

confidence: 99%

A K‐Means Clustering Analysis of the Jovian and Terrestrial Magnetopauses: A Technique to Classify Global Magnetospheric Behavior

et al. 2020

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“…These scales include variations in parameters at different phases of the solar cycle and variations at different cycles. Several similar studies have been carried out (see, e.g., Bruno et al, 1994;Dmitriev et al, 2009;Gopalswamy, Tsurutani, & Yan, 2015;Larrodera & Cid, 2020;Li et al, 2016;Nakagawa et al, 2019 and references therein). However, such studies have several disadvantages: (a) The study of the solar wind lasts for only a short period (usually 2 neighboring cycles are compared), although there are measurements for more than 4 solar cycles that are available for analysis (e.g., OMNI base of solar wind parameters https://spdf.gsfc.nasa.gov/pub/data/omni), (b) There is a lack of selection of the solar wind for its individual types.…”

mentioning

confidence: 90%

“…However, such studies have several disadvantages: (a) The study of the solar wind lasts for only a short period (usually 2 neighboring cycles are compared), although there are measurements for more than 4 solar cycles that are available for analysis (e.g., OMNI base of solar wind parameters https://spdf.gsfc.nasa.gov/pub/data/omni), (b) There is a lack of selection of the solar wind for its individual types. In most works, if a selection is made according to the types of solar wind streams, the selection is only according to the magnitude of the bulk speed (without additional analysis of the connection of these streams with solar structures and/or phenomena), for the so‐called fast and slow streams (see e.g., Bruno et al., 1994; Kasper et al., 2007; Larrodera & Cid, 2020 and references therein). This does not allow the change in the number of different types of SW and the change in parameters in these different types of SW to be separated on these scales.…”

Section: Introductionmentioning

confidence: 99%

Drop of Solar Wind at the End of the 20th Century

et al. 2021

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Variations in the solar wind (SW) parameters with scales of several years are an important characteristic of solar activity and the basis for a long‐term space weather forecast. We examine the behavior of interplanetary parameters over 21–24 solar cycles (SCs) on the basis of the OMNI database (https://spdf.gsfc.nasa.gov/pub/data/omni). Since changes in the parameters can be associated with both changes in the number of different large‐scale types of SW and with variations in the values of these parameters at different phases of the solar cycle and during the transition from one cycle to another, we select the entire study period in accordance with the Catalog of large‐scale SW types for 1976–2019 (see the site http://www.iki.rssi.ru/pub/omni, [Yermolaev, Nikolaeva, et al., 2009, https://doi.org/10.1134/s0010952509020014], which covers the period from 21 to 24 SCs) and in accordance with the phases of the cycles, and average the parameters at selected intervals. In addition to a sharp drop in the number of interplanetary coronal mass ejections and associated sheath types, there is a noticeable drop in the value (by 20%–40%) of plasma parameters and magnetic field in different types of solar wind at the end of the 20th century and a continuation of the fall or persistence at a low level in the 23–24 cycles. Such a drop in the solar wind is apparently associated with a decrease in solar activity and manifests itself in a noticeable decrease in space weather factors.

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“…The choice of limiting the input variables to the IMF data is motivated by the limited data availability of the solar wind plasma parameters for effective implementation of any ML algorithm. Figure 1 in Larrodera and Cid (2020) shows data coverage of the main solar wind parameters measured by the ACE spacecraft from the time it is operational until the end of the year 2017. Data availability for IMF and solar wind velocity is over 98% for any year in the sample (even if some data gaps appear in solar wind velocity due to saturation of SWEPAM instrument during important storm events), but the coverage drops substantially for solar wind temperature or density.…”

Section: Databasementioning

confidence: 99%

Deep Neural Networks With Convolutional and LSTM Layers for SYM‐H and ASY‐H Forecasting

2021

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Geomagnetic indices quantify the disturbance caused by the solar activity on a planetary scale or 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 midlatitude with a 1‐min 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 (DNNs). In this work, we present two DNNs developed to forecast respectively the SYM‐H and ASY‐H indices. Both networks have been trained using the Interplanetary Magnetic Field (IMF) and the related index for the solar storms occurred in the last two solar cycles. As a result, the networks are able to accurately forecast the indices 2 h in advance, considering the IMF and indices values for the previous 200 min. The evaluation of both networks reveals a great forecasting precision, including good predictions for large storms that occurred during the solar cycle 23 and comparing with the persistence model for the period 2013–2020.

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Bimodal distribution of the solar wind at 1 AU

Cited by 22 publications

References 23 publications

A K‐Means Clustering Analysis of the Jovian and Terrestrial Magnetopauses: A Technique to Classify Global Magnetospheric Behavior

A K‐Means Clustering Analysis of the Jovian and Terrestrial Magnetopauses: A Technique to Classify Global Magnetospheric Behavior

Drop of Solar Wind at the End of the 20th Century

Deep Neural Networks With Convolutional and LSTM Layers for SYM‐H and ASY‐H Forecasting

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