Three-dimensional MHD Simulation of Solar Wind Using a New Boundary Treatment: Comparison with In Situ Data at Earth

Abstract

Three-dimensional magnetohydrodynamics (MHD) numerical simulation is an important tool in the prediction of solar wind parameters. In this study, we improve our corona interplanetary total variation diminishing MHD model by using a new boundary applicable to all phases of solar cycles. This model uses synoptic magnetogram maps from the Global Oscillation Network Group as the input data. The empirical Wang–Sheeley–Arge relation is used to assign solar wind speed at the lower boundary, while temperature is specified accordingly based on its empirical relation with the solar wind speed. Magnetic field intensity and solar wind density at the boundary are obtained from observational data in the immediate past Carrington rotations, permitting the persistence of these two parameters in a short time period. The boundary conditions depend on only five tunable parameters when simulating the solar wind for different phases of the solar cycle. We apply this model to simulate the background solar wind from 2007 to 2017 and compare the modeled results with the observational data in the OMNI database. Visual inspection shows that our model can capture the time patterns of solar wind parameters well at most times. Statistical analysis shows that the simulated solar wind parameters are all in good agreement with the observations. This study demonstrates that the improved interplanetary total variation diminishing model can be used for predicting all solar wind parameters near the Earth.

Solar wind energy flux observations in the inner heliosphere: first results from Parker Solar Probe

“…We find that the average value of W R , W R , is about 69.4 W m −2 with a total uncertainty of at most 20%, which is similar to previous results based on long-term observations at greater distances and various latitudes (e.g., Schwenn & Marsch 1990;Meyer-Vernet 2006;Le Chat et al 2009McComas et al 2014). This result confirms that this quantity appears as a global solar constant, which is of importance since it is often used to deduce the solar wind density from the speed (or the reverse) in global heliospheric studies and modeling (e.g., Shen et al 2018;McComas et al 2014;McComas et al 2017McComas et al , 2020Krimigis et al 2019;Wang et al 2020).…”

A Computationally Efficient, Time-Dependent Model of the Solar Wind for Use as a Surrogate to Three-Dimensional Numerical Magnetohydrodynamic Simulations

“…But it is nevertheless important to remember that the MHD model should not be considered "perfect", and one of the issues of numerical MHD is the diffusive nature, which means that it does not readily capture velocity sharp gradients present in observations (e.g. Jian et al, 2015;Shen et al, 2018).…”

Using Gradient Boosting Regression to Improve Ambient Solar Wind Model Predictions

“…Well‐known empirical relationships in this context are the Wang‐Sheeley (WS) model (Wang & Sheeley, 1990), Distance from the Coronal Hole Boundary (DCHB) model (Riley et al., 2001) and the Wang‐Sheeley‐Arge (WSA) model (Arge et al., 2003). More sophisticated three‐dimensional magnetohydrodynamic (MHD) codes such as CORHEL (Linker et al., 2016), LFM‐Helio (Merkin et al., 2016), SIP‐CESE (Feng et al., 2015) and COIN‐TVD MHD (Shen et al., 2018) are also used, with further examples being the Magnetohydrodynamics Algorithm outside a Sphere (Linker et al., 1999), Enlil (Odstrcil, 2003), the Space Weather Modeling Framework (Tóth et al., 2005), and the recently developed European Heliospheric Forecasting Information Asset (Pomoell & Poedts, 2018). Besides these MHD models, other modeling approaches based on empirical relationships and statistics (e.g., M. Owens, Lang, et al., 2020) have also been developed.…”