Predicting the Interplanetary Magnetic Field using Approaches Based on Data Mining and Physical Models

Monday, 14 December 2015: 17:15
2011 (Moscone West)
Pete Riley1, Christopher T Russell2, Curt A de Koning3, Douglas Alan Biesecker4, Jon Linker1, Mathew J Owens5, NoƩ Lugaz6, Piet Martens7, Rafal Angryk8, Alysha Reinard4, Roger K Ulrich9, Timothy Simon Horbury5, Victor J Pizzo10, Yang Liu11 and Todd Hoeksema12, (1)Predictive Science Inc., San Diego, CA, United States, (2)University of California Los Angeles, IGPP/EPSS, Los Angeles, CA, United States, (3)University of Colorado at Boulder, Boulder, CO, United States, (4)NOAA Boulder, SWPC, Boulder, CO, United States, (5)Imperial College London, London, United Kingdom, (6)University of New Hampshire Main Campus, Durham, NH, United States, (7)Georgia Southern University, Statesboro, GA, United States, (8)Georgia State University, Atlanta, GA, United States, (9)American Astronomical Society, Chevy Chase, MD, United States, (10)NOAA Boulder, Boulder, CO, United States, (11)Stanford University, HEPL, Stanford, CA, United States, (12)Stanford University, Stanford, CA, United States
An accurate prediction of the interplanetary magnetic field, and, in particular, its z-component (Bz) is a crucial capability for any space weather forecasting system, and yet, thus far, it has remained largely elusive (a point exemplified by the fact that no prediction center currently provides a forecast for Bz). In this presentation, we discuss the various physical processes that can produce non-zero values of Bz and summarize a selection of promising approaches that may ultimately lead to reliable forecasts of Bz. We describe the first steps we have taken to develop a framework for assessing these techniques, and show preliminary results of their efficacy.