On the Sources and Sizes of Uncertainty in Predicting the Arrival Time of Interplanetary Coronal Mass Ejections Using Global MHD Models

Abstract

Accurate predictions of the properties of interplanetary coronal mass ejection (ICME)‐driven disturbances are a key objective for space weather forecasts. The ICME's time of arrival (ToA) at Earth is an important parameter, and one that is amenable to a variety of modeling approaches. Previous studies suggest that the best models can predict the arrival time to within an absolute uncertainty of 10–15 h. Here, we investigate the main sources of uncertainty in predicting a CME's ToA at Earth. These can be broken into two main categories: (a) the initial properties of the ejecta, including its speed, mass, and direction of propagation and (b) the properties of the ambient solar wind into which it propagates. To estimate the relative contribution to ToA uncertainties, we construct a set of numerical experiments of cone‐model CMEs, where we vary the initial speed, mass, and direction at the inner radial boundary. Additionally, we build an ensemble of 12 ambient solar wind solutions using realizations from the ADAPT model. We find that each component in the chain contributes between ±2.5 and ±7 h of uncertainty to the estimate of the CME's ToA. Importantly, different realizations of the synoptic produce the largest uncertainties. This suggests that estimates of ToA will continue to be plagued with intrinsic uncertainties of ±10 h until tighter constraints can be found for these boundary conditions. Our results suggest that there are clear benefits to focused investigations aimed at reducing the uncertainties in CME speed, mass, direction, and input boundary magnetic fields.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jun 01, 2021
Source ID
10.1029/2021sw002775

Entities

People

  • Michal Ben-Nun
  • Pete Riley

Organizations

  • NOAA Research
  • National Aeronautics and Space Administration
  • United States Air Force

Tags

Readers

  • Computational Modeling and Simulation
  • Public Financial Management and Budgeting
  • Solar Physics

Technology Areas

  • Space