Connecting speeds, directions and arrival times of 22 coronal mass ejections from the Sun to 1 AU

Forecasting the in situ properties of coronal mass ejections (CMEs) from remote images is expected to strongly enhance predictions of space weather, and is of general interest for studying the interaction of CMEs with planetary environments. We study the feasibility of using a single heliospheric imager (HI) instrument, imaging the solar wind density from the Sun to 1 AU, for connecting remote images to in situ observations of CMEs. We compare the predictions of speed and arrival time for 22 CMEs (in 2008-2012) to the corresponding interplanetary coronal mass ejection (ICME) parameters at in situ observatories (STEREO PLASTIC/IMPACT, Wind SWE/MFI). The list consists of front- and backsided, slow and fast CMEs (up to $2700 \: km \: s^{-1}$). We track the CMEs to $34.9 \pm 7.1$ degrees elongation from the Sun with J-maps constructed using the SATPLOT tool, resulting in prediction lead times of $-26.4 \pm 15.3$ hours. The geometrical models we use assume different CME front shapes (Fixed-$\Phi$, Harmonic Mean, Self-Similar Expansion), and constant CME speed and direction. We find no significant superiority in the predictive capability of any of the three methods. The absolute difference between predicted and observed ICME arrival times is $8.1 \pm 6.3$ hours ($rms$ value of 10.9h). Speeds are consistent to within $284 \pm 288 \: km \: s^{-1}$. Empirical corrections to the predictions enhance their performance for the arrival times to $6.1 \pm 5.0$ hours ($rms$ value of 7.9h), and for the speeds to $53 \pm 50 \: km \: s^{-1}$. These results are important for Solar Orbiter and a space weather mission positioned away from the Sun-Earth line.Comment: 19 pages, 13 figures, accepted for publication in the Astrophysical Journa

Wind Power Forecasting Methods Based on Deep Learning: A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics

Eruptive Event Generator Based on the Gibson-Low Magnetic Configuration

Coronal Mass Ejections (CMEs), a kind of energetic solar eruptions, are an integral subject of space weather research. Numerical magnetohydrodynamic (MHD) modeling, which requires powerful computational resources, is one of the primary means of studying the phenomenon. With increasing accessibility of such resources, grows the demand for user-friendly tools that would facilitate the process of simulating CMEs for scientific and operational purposes. The Eruptive Event Generator based on Gibson-Low flux rope (EEGGL), a new publicly available computational model presented in this paper, is an effort to meet this demand. EEGGL allows one to compute the parameters of a model flux rope driving a CME via an intuitive graphical user interface (GUI). We provide a brief overview of the physical principles behind EEGGL and its functionality. Ways towards future improvements of the tool are outlined

Modeling the Young Sun's Solar Wind and its Interaction with Earth's Paleomagnetosphere

We present a focused parameter study of solar wind - magnetosphere interaction for the young Sun and Earth, $~3.5$ Ga ago, that relies on magnetohydrodynamic (MHD) simulations for both the solar wind and the magnetosphere. By simulating the quiescent young Sun and its wind we are able to propagate the MHD simulations up to Earth's magnetosphere and obtain a physically realistic solar forcing of it. We assess how sensitive the young solar wind is to changes in the coronal base density, sunspot placement and magnetic field strength, dipole magnetic field strength and the Sun's rotation period. From this analysis we obtain a range of plausible solar wind conditions the paleomagnetosphere may have been subject to. Scaling relationships from the literature suggest that a young Sun would have had a mass flux different from the present Sun. We evaluate how the mass flux changes with the aforementioned factors and determine the importance of this and several other key solar and magnetospheric variables with respect to their impact on the paleomagnetosphere. We vary the solar wind speed, density, interplanetary magnetic field strength and orientation as well as Earth's dipole magnetic field strength and tilt in a number of steady-state scenarios that are representative of young Sun-Earth interaction. This study is done as a first step of a more comprehensive effort towards understanding the implications of Sun-Earth interaction for planetary atmospheric evolution.Comment: 16 pages, 7 figure