High-speed Streams Research Articles

HelioCast: heliospheric forecasting based on white-light observations of the solar corona

We present a new 3D magnetohydrodynamic (MHD) heliospheric model for space-weather forecasting driven by boundary conditions defined from white-light observations of the solar corona. The model is based on the MHD code PLUTO, constrained by an empirical derivation of the solar wind background properties at 0.1 au. This empirical method uses white-light observations to estimate the position of the heliospheric current sheet (HCS). The boundary conditions necessary to run HelioCast are then defined from pre-defined relations between the necessary MHD properties (speed, density, and temperature) and the distance to the current sheet. We assess the accuracy of the model over six Carrington rotations during the first semester of 2018. Using point-by-point metrics and event-based analyses, we evaluate the performances of our model varying the angular width of the slow solar wind layer surrounding the HCS. We also compare our empirical technique with two well-tested models of the corona: Multi-VP and WindPredict-AW. We find that our method is well-suited to reproduce high-speed streams, and does – for well-chosen parameters – better than full MHD models. The model shows, nonetheless, limitations that could worsen for rising and maximum solar activity.

Geomagnetic storm forecasting from solar coronal holes

ABSTRACT Coronal holes (CHs) are the source of high-speed streams (HSSs) in the solar wind, whose interaction with the slow solar wind creates corotating interaction regions (CIRs) in the heliosphere. Whenever the CIRs hit the Earth, they can cause geomagnetic storms. We develop a method to predict the strength of CIR/HSS-driven geomagnetic storms directly from solar observations using the CH areas and associated magnetic field polarity. First, we build a data set comprising the properties of CHs on the Sun, the associated HSSs, CIRs, and orientation of the interplanetary magnetic field (IMF) at L1, and the strength of the associated geomagnetic storms by the geomagnetic indices Dst and Kp. Then, we predict the Dst and Kp indices using a Gaussian process model, which accounts for the annual variation of the orientation of Earth’s magnetic field axis. We demonstrate that the polarity of the IMF at L1 associated with CIRs is preserved in around 83 per cent of cases when compared to the polarity of their CH sources. Testing our model over the period 2010–2020, we obtained a correlation coefficient between the predicted and observed Dst index of R = 0.63/0.73, and Kp index of R = 0.65/0.67, for HSSs having a polarity towards/away from the Sun. These findings demonstrate the possibility of predicting CIR/HSS-driven geomagnetic storms directly from solar observations and extending the forecasting lead time up to several days, which is relevant for enhancing space weather predictions.

Virtual self-adaptive bitmap for online cardinality estimation

Cardinality estimation is the task of obtaining the number of distinct items in a data stream, which plays an important role in many application domains. However, when dealing with high-speed data streams, it remains a significant challenge to estimate cardinality considering record/query overhead and memory efficiency. This paper proposes a virtual self-adaptive bitmap estimator to support online cardinality estimation, which reduces the record overhead to one hash per item for the first time. By logically adding virtual bits, our estimator automatically adapts its sampling probability to different stream sizes. We evaluate the virtual self-adaptive bitmap theoretically and experimentally. The experimental results show that our estimator significantly improves over the existing work in terms of record throughput, query throughput and estimation accuracy.

Direct numerical simulation of turbulent structures and asymmetric properties in the supersonic non-isothermal mixing layer

By direct numerical simulations, the non-isothermal effects on turbulent structures and asymmetric properties are investigated in the spatially developing supersonic mixing layers with high convective Mach numbers (Mc > 0.6). Hot air is blown in the high-speed stream, and cold air is added on the low-speed side. Two non-isothermal simulations with different temperature gradients are conducted and compared with the isothermal mixing layer. The self-similar model of the spatially developing supersonic turbulent mixing layer is analyzed to reveal the physical mechanisms for the asymmetry of non-isothermal mixing layers. The supersonic mixing layer is characterized by diverse vortices and unsteady shocklets, which increase in the initial shear layer and then decrease in the self-similar turbulent region. Also, the mixing layer is asymmetric between the high- and low-speed streams, and the shear layer center skews toward the low-speed side with more vortices and less shocklets, which is attributed to the streamwise momentum gradient. The effects of temperature gradients enhance the flow instability and accelerate the growth of vortices and shocklets in the initial mixing layer. Nevertheless, the turbulent structures are attenuated in the fully developed region, as the viscous dissipation is augmented and turbulence decays more strongly. In addition, the streamwise momentum gradient is reduced in the non-isothermal mixing layers. The vortical structures suffer from stronger attenuation on the cold side, while the shocklets are more significantly reduced on the hot side. Thus, the skewness of the shear layer center toward the low-speed side is reduced, and the mixing layer asymmetry is attenuated.

Multiclass imbalanced and concept drift network traffic classification framework based on online active learning

The complex problems of multiclass imbalance, virtual or real concept drift, concept evolution, high-speed traffic streams and limited label cost budgets pose severe challenges in network traffic classification tasks. In this paper, we propose a multiclass imbalanced and concept drift network traffic classification framework based on online active learning (MicFoal), which includes a configurable supervised learner for the initialization of a network traffic classification model, an active learning method with a hybrid label request strategy, a label sliding window group, a sample training weight formula and an adaptive adjustment mechanism for the label cost budget based on a periodic performance evaluation. In addition, a novel uncertain label request strategy based on a variable least confidence threshold vector is designed to address the problems of a variable multiclass imbalance ratio or even the number of classes changing over time. Experiments performed based on eight well-known real-world network traffic datasets demonstrate that MicFoal is more effective and efficient than several state-of-the-art learning algorithms.

A Study of the Relationship Between Southward Bz > -10 nT and Storm Time Disturbance Index During Solar Cycle 23

Magnetic reconnection can be used for studying the geoeffective processes in the coupled Sun–Solar wind – Magnetosphere dynamics leading to geomagnetic disturbance. In this study, 1-hour resolution solar wind plasma parameters from OMNIweb were used to investigate the relationship between moderate southward interplanetary magnetic field, IMF-Bz (i.e., Bz > -10 nT) and geomagnetic storm time disturbance, Dst , during the ascending, maximum and descending phases of solar cycle 23. Occurrences of different classes of geomagnetic storms during moderate southward Bz are reported. The occurrence of weak and moderate geomagnetic storms is more predominant during maximum solar activity than intense and super intense storms. It was found that 10.11 % (181) of all the classes of the storm were intense, and 0.17 % (3) were super intense storms. Furthermore, it was found that 4 (2.2 %) out of the 181 intense storms were caused by southward Bz > -10 nT which were associated with the complex structure due to the high-speed solar wind stream and corotating interacting region. In such a complex structure and Bz > -10 nT, we observed that an intense geomagnetic storm rarely occurs and if it does, would be predominant around solar maximum. It was found that long-duration (\Delta t > 6 hrs) of southward Bz (i.e., -10 nT < Bz <= -3.6 nT ) can also lead to an intense geomagnetic storm during the solar maximum and descending phase (moderate solar activity) of a solar cycle. The complex structure of intense geomagnetic storms associated with the Bz > -10 nT is rare and possesses a special configuration of magnetic field and solar wind parameters structures which are CIR manifestations.

Reduced-order modeling of supersonic fuel–air mixing in a multi-strut injection scramjet engine using machine learning techniques

Dual-mode ramjet/scramjet engines promise extended flight speed range and are the commonly preferred air-breathing propulsion system from within the family of hypersonic aircraft concepts. One of the main challenges that should be hurdled in their design is modeling the fuel–air mixing process to provide optimal fuel distribution and yield the best engine performance. Injecting fuel into high-speed air stream along multiple parallel struts can augment the fuel penetration and improve mixing efficiency. Mixing intensity is increased with turbulence by the shock-expansion waves in post-strut regions. However, this enhancement in mixing can bring about detrimental effects on the aerodynamic performance by increasing losses on total pressure. Designing the optimal working configuration requires testing the interaction between many design variables. This can be a tedious and computationally costly task. Machine learning models thus appear well-suited for multi-objective optimization of design variables that can be elusive to designers. In particular, non-linear regression models can be built over the available sparse simulation data to predict unseen mixing conditions. In the present work, we carry out a detailed investigation of the effect of multi-strut configuration parameters on three objective functions: mixing efficiency, mixing length, and the total pressure recovery (TPR) factor. These objective functions are linked with the most relevant physical phenomena in the supersonic fuel–air mixing flow field. We first generate a CFD database by solving compressible, non-reactive, Reynolds-averaged Navier–Stokes (RANS) filtered flow equations in a 2D scramjet engine domain with three varying design variables: struts location, strut wedge angle and strut V-settlement angle. We then apply various regression models – artificial neural network (ANN), Gaussian process regression (GPR), and kernel regression – to this database and formulate a surrogate model relevant for fuel injection that can be utilized in reduced-order modeling studies that estimate the hypersonic engine performance. We find that regression is generally more difficult in the vicinity of fuel struts (due to turbulence/shock effects) and easier further downstream from the struts, but ANN performs generally better than other regression models. Thus, our reduced-order tool incorporates a mixing efficiency model predicted by the ANN. It computes the thrust of a hypersonic engine with less than 10% error. We also present a detailed discussion of the physical insights gained from our CFD database; we link this discussion with the earlier findings from the machine learning tools. In our sensitivity study, we find the strut wedge angle to be the most influencing parameter on the mixing properties and aerodynamic losses.

Multi-instrument observations of polar cap patches and traveling ionospheric disturbances generated by solar wind Alfvén waves coupling to the dayside magnetosphere

Abstract. During minor to moderate geomagnetic storms, caused by corotating interaction regions (CIRs) at the leading edge of high-speed streams (HSSs), solar wind Alfvén waves modulated the magnetic reconnection at the dayside magnetopause. The Resolute Bay Incoherent Scatter Radars (RISR-C and RISR-N), measuring plasma parameters in the cusp and polar cap, observed ionospheric signatures of flux transfer events (FTEs) that resulted in the formation of polar cap patches. The patches were observed as they moved over the RISR, and the Canadian High-Arctic Ionospheric Network (CHAIN) ionosondes and GPS receivers. The coupling process modulated the ionospheric convection and the intensity of ionospheric currents, including the auroral electrojets. The horizontal equivalent ionospheric currents (EICs) are estimated from ground-based magnetometer data using an inversion technique. Pulses of ionospheric currents that are a source of Joule heating in the lower thermosphere launched atmospheric gravity waves, causing traveling ionospheric disturbances (TIDs) that propagated equatorward. The TIDs were observed in the SuperDual Auroral Radar Network (SuperDARN) high-frequency (HF) radar ground scatter and the detrended total electron content (TEC) measured by globally distributed Global Navigation Satellite System (GNSS) receivers.

Low Frequency ULF Waves in the Earth's Inner Magnetosphere: Power Spectra During High Speed Streams and Quiet Solar Wind and Seeding of EMIC Waves.

Here, we extend the scope of the Gamayunov and Engebretson (2021, hereinafter Paper 1), https://doi.org/10.1029/2021JA029247 work by analyzing the low frequency ultra-low-frequency (ULF) wave power spectra in the Earth's inner magnetosphere during high speed stream (HSS) and quiet solar wind (QSW) driving conditions in the upstream solar wind (SW) and comparing our results to the results of Paper 1, where the statistics of ULF wave power spectra during coronal mass ejections (CMEs) are presented. The most important results of our statistical and comparative analyses are as follows. (a) During CMEs, HSSs, and QSW, the magnetic field power spectra of the transverse and compressional fluctuations are well approximated by power laws in the ∼mHz-Hz frequency range, where on average the parameters of power law fits during CMEs and HSSs are close, and those during QSW differ considerably from the respective parameters during CMEs and HSSs. (b) The dominance of the average compressional power over the average transverse power for the low frequency ULF waves during the 0<SYM/H≲25nT geomagnetic conditions may serve as a proxy of HSSs in the upstream SW, whereas the opposite relation between the average powers is an indication of CMEs. (c) Independently of the SW driving conditions, a turbulent energy cascade from low frequencies in the ULF wave frequency range into the higher frequency range exists in the Earth's inner magnetosphere, supplying the nonthermal electromagnetic seed fluctuations needed for the growth of electromagnetic ion cyclotron waves (∼Hz) due to relaxation of unstable distributions of energetic magnetospheric ions.

Solar-Wind High-Speed Stream (HSS) Alfvén Wave Fluctuations at High Heliospheric Latitudes: Ulysses Observations During Two Solar-Cycle Minima

Solar-Wind High-Speed Stream (HSS) Alfvén Wave Fluctuations at High Heliospheric Latitudes: Ulysses Observations During Two Solar-Cycle Minima

Multi-Spacecraft Observations of an Interplanetary Coronal Mass Ejection Interacting with Two Solar-Wind Regimes Observed by the Ulysses and Twin-STEREO Spacecraft

We present a combined study of a coronal mass ejection (CME), revealed in a unique orbital configuration that permits the analysis of remote-sensing observations on 27 June 2007 from the twin Solar Terrestrial Relations Observatory (STEREO)-A and -B spacecraft and of its subsequent in situ counterpart outside the ecliptic plane, the interplanetary coronal mass ejection (ICME) observed on 04 July 2007 by Ulysses at 1.5 AU and heliographic-Earth-ecliptic coordinates system (HEE) 33° latitude and 49° longitude. We apply a triangulation method to the STEREO Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI) COR2 coronagraph images of the CME, and a self-similar expansion fitting method to STEREO/SECCHI Heliospheric Imager (HI)-B. At Ulysses we observe: a preceding forward shock, followed by a sheath region, a magnetic cloud, a rear forward shock, followed by a compression region due to a succeeding high-speed stream (HSS) interacting with the ICME. From a minimum variance analysis (MVA) and a length-scale analysis we infer that the magnetic cloud at Ulysses, with a duration of 24 h, has a west-north-east configuration, length scale of ≈0.2 AU, and mean expansion speed of 14.2 km s−1. The relatively small size of this ICME is likely to be a result of its interaction with the succeeding HSS. This ICME differs from the previously known over-expanding types observed by Ulysses, in that it straddles a region between the slow and fast solar wind that in itself drives the rear shock. We describe the agreements and limitations of these observations in comparison with 3D magneto-hydrodynamic (MHD) heliospheric simulations of the ICME in the context of a complex solar-wind environment.

Scaling patch analysis of planar turbulent mixing layers

Proper scales for the mean flow and Reynolds shear stress in planar turbulent mixing layers are determined from a scaling patch analysis of the mean continuity and momentum equations. By seeking an admissible scaling of the mean continuity equation, a proper scale for the mean transverse flow is determined as Vref=(dδ/dx)Uref, where dδ/dx is the growth rate of the mixing layer width and Uref=Uh−Ul is the difference between the velocity of the high speed stream Uh and the velocity of the low speed stream Ul. By seeking an admissible scaling for the mean momentum equation, a proper scale for the kinematic Reynolds shear stress is determined as Ruv,ref=UavgVref=[12Audδdx]Uref2, where Au=def(Uh−Ul)/(Uh+Ul) is the normalized velocity difference that emerges naturally in the admissible scaling of the mean momentum equation. Self-similar equations for the scaled mean transverse flow V* and Reynolds shear stress Ruv*=Ruv/Ruv,ref are derived from the mean continuity and mean momentum equations. Approximate equations for V* and Ruv* are developed and found to agree well with experimental data.

Системный анализ организации маркетинговой деятельности в конкурентной среде

The Industrial Internet of Things is irreversibly transforming the economy. New strategic opportunities derived from the high-speed streams of Big Data of the Industrial Internet of Things are changing the boundaries of industries, creating opportunities for new strategic decisions, including in marketing. The use of Industrial Internet of Things technologies in marketing is of interest. The continuous interaction of devices and programs connected to the Internet and controlled by their own intelligent systems gives the marketing system the ability to automatically manage the effectiveness of marketing processes, increases the stability of the system, warns of possible threats from the Internet or new opportunities for the system. The purpose of the article is to improve the IIoT system of enterprise marketing through a systematic analysis of the positive synergistic interactions of its subsystems, determine the feedback of subsystems, and increase the functionality of the system. In the system of organizing marketing activities, a synergistic effect arises from the interaction of subsystems associated with intensive information exchange between distributed devices and business objects of the Industrial Internet of Things, each of which independently manages its own area, determined by its competencies. In the process of work, the tasks of studying the contribution to the effectiveness of marketing of functional subsystems were solved: information technology, intellectual, Internet marketing and information security subsystem. Classical system analysis was used as a research method. The scientific novelty of the work lies in a systematic analysis of the effectiveness of marketing based on the Industrial Internet of Things. The conditions under which marketing is most effective are determined. The practical significance of the work lies in the fact that the basis for the development of an effective marketing system based on the Industrial Internet of Things is proposed.

The Sun and Space Weather

The explosion of space weather research since the early 1990s has been partly fueled by the unprecedented, uniform, and extended observations of solar disturbances from space- and ground-based instruments. Coronal mass ejections (CMEs) from closed magnetic field regions and high-speed streams (HSS) from open-field regions on the Sun account for most of the disturbances relevant to space weather. The main consequences of CMEs and HSS are their ability to cause geomagnetic storms and accelerate particles. Particles accelerated by CME-driven shocks can pose danger to humans and their technological structures in space. Geomagnetic storms produced by CMEs and HSS-related stream interaction regions also result in particle energization inside the magnetosphere that can have severe impact on satellites operating in the magnetosphere. Solar flares are another aspect of solar magnetic energy release, mostly characterized by the sudden enhancement in electromagnetic emission at various wavelengths—from radio waves to gamma-rays. Flares are responsible for the sudden ionospheric disturbances and prompt perturbation of Earth’s magnetic field known as magnetic crochet. Nonthermal electrons accelerated during flares can emit intense microwave radiation that can drown spacecraft and radar signals. This review article summarizes major milestones in understanding the connection between solar variability and space weather.

Worldwide study of the Sporadic E (Es) layer development during a space weather event

This work analyzes the worldwide development of Sporadic E (Es) layers in different regions during a High-Speed Solar Wind Stream (HSS) event on March 18, 2018. We used Digisonde data from auroral, equatorial, and low-middle latitude stations as the primary source for the analysis. We also examined a set of data from selected geomagnetic quiet days to compare the Es layer behavior in such regions. The preliminary results agreed with the understanding that the wind shear mechanism is the principal agent causing the Es layer behavior around the globe, except for the auroral region. Magnetometer and Total Electron Content (TEC) data showed that an undershielding electric field could also influence the Es layer formation over equatorial regions. Further, we observed auroral-type Es layer (Esa) occurrence in the South American Magnetic Anomaly (SAMA). The particle precipitation dynamics between the auroral site and SAMA region have different ways of acting in the Esa layer formation. Finally, our analysis provided a deep understanding of the Es layer behavior in different latitudes during a specific space weather event.

Using mutual information to investigate non-linear correlation between AE index, ULF Pc5 wave activity and electron precipitation

In this study, we use mutual information from information theory to investigate non-linear correlation between geomagnetic activity indicated by auroral electrojet (AE) index with both the global ultra low frequency (ULF) Pc5 wave power and medium energy (≥30 keV) electron precipitation at the central outer radiation belt. To investigate the energy and magnetic local time (MLT) dependence of the non-linearity, we calculate the mutual information and Pearson correlation coefficient separately for three different energy ranges (30–100 keV, 100–300 keV and ≥300 keV) and four different MLT sectors (0–6, 6–12, 12–18, 18–24). We compare results from 2 years 2004 and 2007 representing geomagnetically more active and less active years, respectively. The correlation analysis between the AE index and electron precipitation shows a clear MLT and energy dependence in both active and quiet conditions. In the two lowest energy ranges of the medium energy electrons (30–100 keV and 100–300 keV) both non-linear correlation and Pearson correlation indicate strong dependence with the AE index in the dawn sector. The linear dependence indicated by the Pearson correlation coefficient decreases from dawn to dusk while the change in the non-linear correlation is smaller indicating an increase in the non-linearity from dawn to dusk. The non-linearity between the AE index and electron precipitation is larger at all MLT sectors except MLTs 6–12 during geomagnetically more active year when larger amount of the activity is driven by interplanetary coronal mass ejections (ICMEs) compared to lower activity year with high speed stream (HSS) and stream interaction region (SIR) driven activity. These results indicate that the processes leading to electron precipitation become more non-linear in the dusk and during geomagnetically more active times when the activity is driven by ICMEs. The non-linearity between the AE index and global ULF Pc5 activity is relatively low and seems not to be affected by the difference in the geomagnetic activity during the 2 years studied.

Solar Cycle Variability in Coronal Holes and Their Effects on Solar Wind Sources

AbstractVarious upstream spacecraft have now observed the solar wind conditions affecting the Earth since the 1970s, covering over four solar activity cycles. These measurements provide a long term picture of the related patterns in large scale incident plasma and magnetic field parameters of interest for both interpreting cycles in geospace effects, and understanding how the Sun controls our space environment. This paper focuses on the latter, in part to provide context at the start of the new solar cycle 25, and toward establishing connections between the 1 AU ecliptic solar wind behavior and the unprecedented near‐Sun measurements of heliospheric features on Parker Solar Probe and Solar Orbiter. Magnetograph data‐based potential field source surface models provide a basic picture of how the solar wind sources, including those that give rise to corotating high speed streams in the ecliptic, have changed since the beginning of the record of regular solar wind measurements. In particular, they suggest the contributions from low to mid latitude coronal holes dominate the observed cycles (21–24), especially the weaker cycles (23 and 24), impacting upstream measurement interpretations, modeling, and forecasting considerations. For example, recurring features are affected by differential rotation of the Sun's surface field, which through its effects on the corona, can produce solar wind streams reappearing at ∼25–30 days intervals instead of at the canonical 27.3 days Carrington rotation rate. In addition, the conditions that lead to the corotating stream structure that can dominate periods of low solar activity are seen to be more complicated than suggested by the simple concepts of early studies. The overall results illustrate where in the cycles well‐defined, long‐lived large scale structures can be expected, and the advantages of synoptic displays of 1 AU solar wind parameters for anticipating timings of recurring features.

Beyond Basic Drag in Interplanetary CME Modeling: Effects of Solar Wind Pileup and High‐Speed Streams

AbstractCoronal mass ejections (CMEs) cause severe space weather effects throughout our solar system. As a fast CME propagates through interplanetary space, it accumulates solar wind materials at its front. This pileup of materials, or CME‐driven sheath, can be important in determining the geoeffectiveness of a CME. We take an existing arrival time model that includes expansion and deformation of the CME flux rope (ANTEATR; Kay &amp; Gopalswamy, 2018, https://doi.org/10.1029/2018JA025780; Kay &amp; Nieves‐Chinchilla, 2021a, https://doi.org/10.1029/2020JA028911) and add a pileup procedure (PUP) as a physics‐based approach to modeling the CME‐driven sheath. ANTEATR‐PUP solves the Rankine‐Hugoniot equations for an oblique shock to determine the shock speed and sheath density, magnetic field, and temperature. The extra sheath mass affects the background drag calculation. Additionally, ANTEATR can now use any 1D profile for the background solar wind as opposed to the simple empirical models it previously relied upon. We present initial results from ANTEATR‐PUP and compare with previous ANTEATR findings. Using results from an MHD simulation, we explore the effects of interactions with a static high‐speed stream (HSS) on the CME's and sheath's interplanetary evolution. The drag forces essentially disappear while a CME remains within the HSS, but reappear stronger once the CME exits. The HSS‐CME interaction produces the largest changes in the CME and sheath properties at 1 au when it occurs either close to the Sun near the inner simulation boundary at 0.1 au or right before the CME reaches 1 au. We estimate that these changes could significantly affect the geoeffectiveness.

CME Evolution in the Structured Heliosphere and Effects at Earth and Mars During Solar Minimum

AbstractThe activity of the Sun alternates between a solar minimum and a solar maximum, the former corresponding to a period of “quieter” status of the heliosphere. During solar minimum, it is in principle more straightforward to follow eruptive events and solar wind structures from their birth at the Sun throughout their interplanetary journey. In this paper, we report analysis of the origin, evolution, and heliospheric impact of a series of solar transient events that took place during the second half of August 2018, that is, in the midst of the late declining phase of Solar Cycle 24. In particular, we focus on two successive coronal mass ejections (CMEs) and a following high‐speed stream (HSS) on their way toward Earth and Mars. We find that the first CME impacted both planets, whilst the second caused a strong magnetic storm at Earth and went on to miss Mars, which nevertheless experienced space weather effects from the stream interacting region preceding the HSS. Analysis of remote‐sensing and in‐situ data supported by heliospheric modeling suggests that CME–HSS interaction resulted in the second CME rotating and deflecting in interplanetary space, highlighting that accurately reproducing the ambient solar wind is crucial even during “simpler” solar minimum periods. Lastly, we discuss the upstream solar wind conditions and transient structures responsible for driving space weather effects at Earth and Mars.

Medium-term prediction of the fluence of relativistic electrons in geostationary orbit using solar wind streams forecast based on solar observations

The new approaches to the modelling of the Sun-solar wind-magnetosphere-radiation belts chain and their implementation at MSU's Space weather center (Space Monitoring Data Center, SMDC) are presented. The main idea is to use data from medium-term (3–5 days) forecasts of the speed of quasi-stationary solar wind streams based on data obtained from solar images in the UV wavelength range to improve the quality of forecasts of the magnetospheric factors of the space environment. An operational model for predicting the daily fluence of relativistic electrons (>2 MeV) in the Earth's outer radiation belt has been developed, with the extended set of input parameters that include the forecast of the solar wind velocity. To validate the model, the period from October 11, 2016 to February 18, 2017 was considered, during which the Sun made several revolutions and it was possible to observe the evolution of coronal holes and the development of solar sporadic phenomena. The arrival of high-speed solar wind streams from coronal holes to the Earth's magnetosphere, as well as, as impact of coronal mass ejections, provoked geomagnetic disturbances of various intensity, and caused significant variations of the electron fluxes of the Earth's outer radiation belt. SMDC website implements solar wind model based on the data of solar observations by the AIA instrument of the SDO space observatory at wavelengths of 19.3 and 21.1 nm that forecasts the daily fluence of relativistic electrons (E > 2 MeV) in geostationary orbit. It is shown that the use of the results of medium-term forecasting of the solar wind speed as an input parameter in the daily electron fluence forecast model can significantly improve its quality with a forecast horizon of up to 4 days.