MHD heliosphere with boundary conditions from a tomographic reconstruction using interplanetary scintillation data

Abstract

Observations of interplanetary scintillation (IPS) provide a set of data that is used in estimating the solar wind parameters with reasonably good accuracy. Various tomography techniques have been developed to deconvolve the line-of-sight integration effects ingrained in observations of IPS to improve the accuracy of solar wind reconstructions. Among those, the time-dependent tomography developed at the University of California, San Diego (UCSD) is well known for its remarkable accuracy in reproducing the solar wind speed and density at Earth by iteratively fitting a kinematic solar wind model to observations of IPS and near-Earth spacecraft measurements. However, the kinematic model gradually breaks down as the distance from the Sun increases beyond the orbit of Earth. Therefore, it would be appropriate to use a more sophisticated model, such as a magnetohydrodynamics (MHD) model, to extend the kinematic solar wind reconstruction beyond the Earth's orbit and to the outer heliosphere. To test the suitability of this approach, we use boundary conditions provided by the UCSD time-dependent tomography to propagate the solar wind outward in a MHD model and compare the simulation results with in situ measurements and also with the corresponding kinematic solution. Interestingly, we find notable differences in proton radial velocity and number density at Earth and various locations in the inner heliosphere between the MHD results and both the in situ data and the kinematic solution. For example, at 1 AU, the MHD velocities are generally larger than the spacecraft data by up to 150 km s −1 , and the amplitude of density fluctuations is also markedly larger in the MHD solution. We show that the MHD model can deliver more reasonable results at Earth with an ad hoc adjustment of the inner boundary values. However, we conclude that the MHD model using the inner boundary conditions derived from kinematic simulations has little chance to match IPS and in situ data as well as the kinematic model does unless it too is iteratively fit to the observational data and measurements.

Predicting the Solar Wind at the Parker Solar Probe Using an Empirically Driven MHD Model

“…Before interpolating the original 2°× 2°(φ, θ) WSA maps onto the MS-FLUKSS inner boundary, we scale the WSA magnetic field uniformly by a factor of 2 to compensate for the systematic underestimation of the magnetic field strengths at 1 au (e.g., Linker et al 2016Linker et al , 2017Wallace et al 2019). The WSA solar wind speeds are also reduced by 75 km s −1 to account for the differences in solar wind acceleration between the simple kinematic model of WSA and the more sophisticated MS-FLUKSS MHD model (e.g., MacNeice et al 2011;Kim et al 2014). We further estimate the solar wind density and temperature at the MS-FLUKSS inner boundary based on the assumptions of constant momentum flux and thermal pressure balance, respectively (see Linker et al 2016, and references therein).…”

CESE‐HLL Magnetic Field‐Driven Modeling of the Background Solar Wind During Year 2008

“…The PNC method was also employed by Hayashi () in his data‐driven coronal simulation. Other heliospheric solar wind simulations are driven above the Alfvénic surface by either empirical coronal models (Linker et al, ; Merkin, Lyon, et al, ) or interplanetary scintillation data (IPS) (Hayashi, ; Jackson et al, ; Kim et al, ; Odstrcil et al, ; Yu et al, ). For these models, the boundary treatment is more straightforward because the solar wind at their inner boundaries is already super‐Alfvénic and all variables may be prescribed directly.…”

Analysis of an Interplanetary Coronal Mass Ejection by a Spacecraft Radio Signal: A Case Study

“…The amplitude and phase of any natural or man-made signal are disturbed when crossing an ICME. Natural radio sources have, for several decades, provided a tool to investigate heliospheric plasma by measuring interplanetary scintillation (see, e.g., Hewish et al, 1964;Jackson et al, 1998;Kojima et al, 1998), by comparing radio observations with solar and heliospheric images (see, e.g., Dorrian et al, 2008;Hardwick et al, 2013;Jones et al, 2007), by in situ solar wind observations (see, e.g., Coles et al, 1978), and by simulating the plasma of the heliosphere (see, e.g., Kim et al, 2014;Sokól et al, 2013).…”