arXiv subjects

Ward B. Manchester

Publications and source records attributed to Ward B. Manchester.

9 recordsLinked to original sources

arXiv2026

Evolution of Coronal Mass Ejections in Different Data-Driven Solar Wind Conditions

Numerical models of the solar wind and coronal mass ejections (CMEs) utilize photospheric magnetic field observations to prescribe the inner boundary conditions for the plasma solutions. These magnetic field data are available to the community through various observational instruments, prepared via different methodologies and/or flux-transport models. The solar wind solution driven by these maps provides the ambient plasma environment into which CMEs travel, coupling, and interacting with the surrounding plasma and governing the CME evolution and propagation in the solar corona and inner heliosphere. In this work, we use different input magnetic field maps for the same time period to drive the global Alfven Wave Solar atmosphere Model (AWSoM). We obtain the ambient solar wind conditions and compare the plasma properties and magnetic morphology in the coronal domain to study the influence of the input maps. To understand how the resulting coronal solutions impact CMEs, we launch eruptions described by analytical flux ropes into these data-driven solutions and compare their evolution in the coronal domain (up to 24 solar radii radially). The CMEs achieve varying speeds, deceleration rates, propagation directions, mass and energies while coupling with the background solar wind. We quantify these differences to show that the different input driving maps can significantly impact the simulated CME propagation in the solar wind plasma. This also highlights the importance of understanding the uncertainties associated with data-driven modeling that become increasingly important in operational models and space weather prediction.

astro-ph.SR↗

arXiv2024

Adjusting the Potential Field Source Surface Height Based on MHD Simulations

A potential field solution is widely used to extrapolate the coronal magnetic field above the Sun's surface to a certain height. This model applies the current-free approximation and assumes that the magnetic field is entirely radial beyond the source surface height, which is defined as the radial distance from the center of the Sun. Even though the source surface is commonly specified at 2.5 Rs (solar radii), previous studies have suggested that this value is not optimal in all cases. In this study, we propose a novel approach to specify the source surface height, by comparing the areas of the open magnetic field regions from the potential field solution with predictions made by a magnetohydrodynamics model, in our case the Alfven Wave Solar atmosphere Model. We find that the adjusted source surface height is significantly less than 2.5 Rs near solar minimum, and slightly larger than 2.5 Rs near solar maximum. We also report that the adjusted source surface height can provide a better open flux agreement with the observations near the solar minimum, while the comparison near the solar maximum is slightly worse.

astro-ph.SR↗

arXiv2022

Incorporating Polar Field Data for Improved Solar Flare Prediction

In this paper, we consider incorporating data associated with the sun's north and south polar field strengths to improve solar flare prediction performance using machine learning models. When used to supplement local data from active regions on the photospheric magnetic field of the sun, the polar field data provides global information to the predictor. While such global features have been previously proposed for predicting the next solar cycle's intensity, in this paper we propose using them to help classify individual solar flares. We conduct experiments using HMI data employing four different machine learning algorithms that can exploit polar field information. Additionally, we propose a novel probabilistic mixture of experts model that can simply and effectively incorporate polar field data and provide on-par prediction performance with state-of-the-art solar flare prediction algorithms such as the Recurrent Neural Network (RNN). Our experimental results indicate the usefulness of the polar field data for solar flare prediction, which can improve Heidke Skill Score (HSS2) by as much as 10.1%.

astro-ph.SR↗

arXiv2021

What Sustained Multi-Disciplinary Research Can Achieve: The Space Weather Modeling Framework

MHD-based global space weather models have mostly been developed and maintained at academic institutions. While the "free spirit" approach of academia enables the rapid emergence and testing of new ideas and methods, the lack of long-term stability and support makes this arrangement very challenging. This paper describes a successful example of a university-based group, the Center of Space Environment Modeling (CSEM) at the University of Michigan, that developed and maintained the Space Weather Modeling Framework (SWMF) and its core element, the BATS-R-US extended MHD code. It took a quarter of a century to develop this capability and reach its present level of maturity that makes it suitable for research use by the space physics community through the Community Coordinated Modeling Center (CCMC) as well as operational use by the NOAA Space Weather Prediction Center (SWPC).

physics.space-ph↗

arXiv2020

Threaded-Field-Lines Model for the Low Solar Corona Powered by the Alfven Wave Turbulence

We present an updated global model of the solar corona, including the transition region. We simulate the realistic tree-dimensional (3D) magnetic field using the data from the photospheric magnetic field measurements and assume the magnetohydrodynamic (MHD) Alfvén wave turbulence and its non-linear dissipation to be the only source for heating the coronal plasma and driving the solar wind. In closed field regions the dissipation efficiency in a balanced turbulence is enhanced. In the coronal holes we account for a reflection of the outward propagating waves, which is accompanied by generation of weaker counter-propagating waves. The non-linear cascade rate degrades in strongly imbalanced turbulence, thus resulting in colder coronal holes. The distinctive feature of the presented model is the description of the low corona as almost-steady-state low-beta plasma motion and heat flux transfer along the magnetic field lines. We trace the magnetic field lines through each grid point of the lower boundary of the global corona model, chosen at some heliocentric distance, $R=R_{b}\sim1.1\ R_\odot$ well above the transition region. One can readily solve the plasma parameters along the magnetic field line from 1D equations for the plasma motion and heat transport together with the Alfvén wave propagation, which adequately describe physics within the heliocentric distances range, $R_{\odot}<R<R_{b}$, in the low solar corona. By interfacing this threaded-field-lines model with the full MHD global corona model at $r=R_{b}$, we find the global solution and achieve a faster-than-real-time performance of the model on $\sim200$ cores.

astro-ph.SR↗

arXiv2020

Predicting solar flares with machine learning: investigating solar cycle dependence

A deep learning network, Long-Short Term Memory (LSTM) network, is used in this work to predict whether the maximum flare class an active region (AR) will produce in the next 24 hours is class $Γ$. We considered $Γ$ are $\ge M$, $\ge C$ and any flare class. The essence of using LSTM, which is a recurrent neural network, is its capability to capture temporal information of the data samples. The input features are time sequences of 20 magnetic parameters from SHARPs - Space-weather HMI Active Region Patches. We analyzed active regions from June 2010 to Dec 2018, using the Geostationary Operational Environmental Satellite (GOES) X-ray flare catalogs and label the data samples with identified ARs in the GOES X-ray flare catalogs. Our results (i) shows consistent skill scores with recently published results using LSTMs and better than the previous work using single time input (eg. DeFN) (ii) The skill scores from the model show essential differences when different years of data was chosen for training and testing.

astro-ph.SR↗

arXiv2019

Identifying Solar Flare Precursors Using Time Series of SDO/HMI Images and SHARP Parameters

We present several methods towards construction of precursors, which show great promise towards early predictions, of solar flare events in this paper. A data pre-processing pipeline is built to extract useful data from multiple sources, Geostationary Operational Environmental Satellites (GOES) and Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI), to prepare inputs for machine learning algorithms. Two classification models are presented: classification of flares from quiet times for active regions and classification of strong versus weak flare events. We adopt deep learning algorithms to capture both the spatial and temporal information from HMI magnetogram data. Effective feature extraction and feature selection with raw magnetogram data using deep learning and statistical algorithms enable us to train classification models to achieve almost as good performance as using active region parameters provided in HMI/Space-Weather HMI-Active Region Patch (SHARP) data files. Case studies show a significant increase in the prediction score around 20 hours before strong solar flare events.

astro-ph.SR↗

arXiv2018

Extended MHD modeling of the steady solar corona and the solar wind

The history and present state of large-scale magnetohydrodynamic (MHD) modeling of the solar corona and the solar wind with steady or quasi-steady coronal physics is reviewed. We put the evolution of ideas leading to the recognition of the existence of an expanding solar atmosphere into historical context. The development and main features of the first generation of global corona and solar wind models are described in detail. This historical perspective is also applied to the present suite of global corona and solar wind models. We discuss the evolution of new ideas and their implementation into numerical simulation codes. We point out the scientific and computational challenges facing these models and discuss the ways various groups tried to overcome these challenges. Next, we discuss the latest, state-of-the art models and point to the expected next steps in modeling the corona and the interplanetary medium.

astro-ph.SR↗

arXiv2017

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.

physics.space-ph↗