Real‐Time SWMF at CCMC: Assessing the Dst Output From Continuous Operational Simulations

Abstract

The ground-based magnetometer index of Dst is a commonly used measure of near-Earth current systems, in particular the storm time inner magnetospheric current systems. The ability of a large-scale, physics-based model to reproduce, or even predict, this index is therefore a tangible measure of the overall validity of the code for space weather research and space weather operational usage. Experimental real-time simulations of the Space Weather Modeling Framework (SWMF) are conducted at the Community Coordinated Modeling Center (CCMC). Presently, two configurations of the SWMF are running in real time at CCMC, both focusing on the geospace modules, using the Block Adaptive Tree Solar wind-type Roe Upwind Solver magnetohydrodynamic model, the Ridley Ionosphere Model, and with and without the Rice Convection Model. While both have been running for several years, nearly continuous results are available since April 2015. A 27-month interval through July 2017 is used for a quantitative assessment of Dst from the model output compared against the Kyoto real-time Dst. Quantitative measures are presented to assess the goodness of fit including contingency tables and a receiver operating characteristic curve. It is shown that the SWMF run with the inner magnetosphere model is much better at reproducing storm time values, with a correlation coefficient of 0.69, a prediction efficiency of 0.41, and Heidke skill score of 0.57 (for a À50-nT threshold). A comparison of real-time runs with and without the inner magnetospheric drift physics model reveals that nearly all of the storm time Dst signature is from current systems related to kinetic processes on closed magnetic field lines.Plain Language Summary As society becomes more dependent on technologies susceptible to adverse space weather, it is becoming increasingly critical to have numerical models capable of running in real time to nowcast/forecast the conditions in the near-Earth space environment. One such model is available at the Community Coordinated Modeling Center and has been running for several years, allowing for an assessment of the quality of the result. Comparisons are made against globally compiled index of near-Earth space storm activity, including numerous statistical quantities and tests. The skill of the model is remarkable, especially when a few hours after each of the cold restarts of the model are removed from the comparison. It is also shown that a global model alone is not that good at reproducing this storm index; a regional model for the inner part of geospace is necessary for good data-model agreement.

Model Evaluation Guidelines for Geomagnetic Index Predictions

“…Haiducek et al (2017) SYM-H prediction from SWMF for all of January 2005 R = 0.84, RMSE = 17 nT, ME = 4 nTLiemohn et al (2018) Dst prediction from SWMF in real-time mode R = 0.69, PE = 0.41, HSS = 0.57, RMSE = 13 nTMorley, Welling, and Woodroffe (2018) SYM-H prediction for the 5 April 2010 storm from an ensemble run varying solar wind input Probability distributions of MAE, ME, and RMSE 10.1029/2018SW002067…”

Space Weather Quarterly Volume 16, Issue 4, 2019

“…The potential for improved forecasts has already been demonstrated for parts of the Sun-to-Earth system. For example, coupling a global magnetosphere model with an inner magnetosphere drift physics model considerably improves forecasts of geomagnetic storms (Liemohn et al, 2018) and improved representation of the thermosphere leads to improved ionospheric evolution (e.g., Chartier et al, 2013). In addition, there is a strong connection between the lower atmosphere state and the ionosphere that was highlighted initially by Immel et al (2006) and demonstrated in later modeling studies (e.g., Pedatella et al, 2016).…”

Simulations of the inner magnetospheric energetic electrons using the IMPTAM-VERB coupled model

“…from the NOAA solar wind predictions of the Wang-Sheeley-Arge(WSA)-ENLIL model (Parsons et al, 2011), from the Space Weather Modeling Framework (SWMF) (e.g. Haiducek et al, 2017;Liemohn et al, 2018;Wintoft and Wik, 2018) or from the SWIFT-code (Arber et al, 2001).…”