Accurate forecasting of the properties of coronal mass ejections (CMEs) as they approach Earth is now recognized as an important strategic objective for both NOAA and NASA. The time of arrival of such events is a key parameter, one that had been anticipated to be relatively straightforward to constrain. In this study, we analyze forecasts submitted to the Community Coordinated Modeling Center at NASA's Goddard Space Flight Center over the last 6 years to answer the following questions: (1) How well do these models forecast the arrival time of CME‐driven shocks? (2) What are the uncertainties associated with these forecasts? (3) Which model(s) perform best? (4) Have the models become more accurate during the past 6 years? We analyze all forecasts made by 32 models from 2013 through mid‐2018, and additionally focus on 28 events, all of which were forecasted by six models. We find that the models are generally able to predict CME‐shock arrival times—in an average sense—to within ±10 hr, but with standard deviations often exceeding 20 hr. The best performers, on the other hand, maintained a mean error (bias) of −1 hr, a mean absolute error of 13 hr, and a precision (standard deviation) of 15 hr. Finally, there is no evidence that the forecasts have become more accurate during this interval. We discuss the intrinsic simplifications of the various models analyzed, the limitations of this investigation, and suggest possible paths to improve these forecasts in the future.
Three-dimensional magnetohydrodynamics (MHD) numerical simulation is an important tool in the prediction of solar wind parameters. In this study, we improve our corona interplanetary total variation diminishing MHD model by using a new boundary applicable to all phases of solar cycles. This model uses synoptic magnetogram maps from the Global Oscillation Network Group as the input data. The empirical Wang–Sheeley–Arge relation is used to assign solar wind speed at the lower boundary, while temperature is specified accordingly based on its empirical relation with the solar wind speed. Magnetic field intensity and solar wind density at the boundary are obtained from observational data in the immediate past Carrington rotations, permitting the persistence of these two parameters in a short time period. The boundary conditions depend on only five tunable parameters when simulating the solar wind for different phases of the solar cycle. We apply this model to simulate the background solar wind from 2007 to 2017 and compare the modeled results with the observational data in the OMNI database. Visual inspection shows that our model can capture the time patterns of solar wind parameters well at most times. Statistical analysis shows that the simulated solar wind parameters are all in good agreement with the observations. This study demonstrates that the improved interplanetary total variation diminishing model can be used for predicting all solar wind parameters near the Earth.
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