Inclusion of Real-Time In-Situ Measurements into the UCSD Time-Dependent Tomography and Its Use as a Forecast Algorithm

The University of California, San Diego (UCSD) three-dimensional (3D) time-dependent tomography program, used for over a decade to reconstruct and forecast coronal mass ejections (CMEs), does so from observations of interplanetary scintillation (IPS) taken using the Solar-Terrestrial Environment Laboratory (STELab) radio arrays in Japan. An earlier article (Jackson et al. in Solar Phys. 265, 245, 2010) demonstrated how in-situ velocity measurements from the Advanced Composition Explorer (ACE) space-borne instrumentation can be used in addition to remote-sensing data to constrain a time-dependent tomographic velocity solution. Here we extend this in-situ inclusion to density measurements, and show how this constrains the tomographic density solution. Supplementing remote-sensing observations with in-situ measurements provides additional information to construct an iterated solar-wind parameter that is propagated outward from near the solar surface past the measurement location, and throughout the volume. As in the case of velocity when this is done, the largest changes within the volume are close to the radial directions around Earth that incorporate the in-situ measurements; the inclusion significantly reduces the uncertainty in extending these measurements to global 3D reconstructions that are distant in time and space from the spacecraft. At Earth, this analysis provides a finely tuned real-time result up to the latest time for which in-situ measurements are available, and enables more-accurate extension of these results near Earth to those remotely sensed. We show examples of this new algorithm using real-time STELab IPS data that were used in our forecasts throughout Carrington rotations 2010 through 2016, and we provide one metric prescription that we have used to determine the forecasting accuracy one, two, and three days in advance of the time data become available to analyze from STELab. We show that the accuracy is considerably better than assuming persistence of the same signal over one to two days in advance of when the data are available.