Perturbed Input Ensemble Modeling With the Space Weather Modeling Framework

Abstract

To assess the effect of uncertainties in solar wind driving on the predictions from the operational configuration of the Space Weather Modeling Framework, we have developed a nonparametric method for generating multiple possible realizations of the solar wind just upstream of the bow shock, based on observations near the first Lagrangian point. We have applied this method to the solar wind inputs at the upstream boundary of Space Weather Modeling Framework and have simulated the geomagnetic storm of 5 April 2010. We ran a 40‐member ensemble for this event and have used this ensemble to quantify the uncertainty in the predicted Sym‐H index and ground magnetic disturbances due to the uncertainty in the upstream boundary conditions. Both the ensemble mean and the unperturbed simulation tend to underpredict the magnitude of Sym‐H in the quiet interval before the storm and overpredict in the storm itself, consistent with previous work. The ensemble mean is a more accurate predictor of Sym‐H, improving the mean absolute error by nearly 2 nT for this interval and displaying a smaller bias. We also examine the uncertainty in predicted maxima in ground magnetic disturbances. The confidence intervals are typically narrow during periods where the predicted dBH/dt is low. The confidence intervals are often much wider where the median prediction is for enhanced dBH/dt. The ensemble also allows us to identify intervals of activity that cannot be explained by uncertainty in the solar wind driver, driving further model improvements. This work demonstrates the feasibility and importance of ensemble modeling for space weather applications.Plain Language SummaryForecasts of space weather usually rely on spacecraft measurements of the solar wind from about a million miles away from Earth. Like water flowing toward a rock in a stream, measurements at a single point upstream may not reflect exactly what will hit the Earth. Forecasts that are driven by these measurements have uncertainty due to the uncertainty in the measurements driving the forecast models. We have developed a technique to estimate the uncertainty on space weather predictions using 7 years of solar wind measurements from two satellites. We have performed computer simulations of the same geomagnetic storm 41 times. In each simulation, the inputs were modified slightly each time to reflect the uncertainty in the measurements. By considering the set of simulations as a whole, we have shown that space weather forecasts can be improved by accounting for the uncertainty in the input data. We have also shown that accounting for uncertainty in the data driving, the model can highlight where incorrect forecasts are due to the uncertainty, as well as where they are due to inadequacies in the model itself. This work shows the importance of ensemble methods and accounting for uncertainties in space weather simulation and forecasting.Key PointsA new nonparametric method for drawing different realizations of solar wind data to drive magnetospheric models is derivedThe new method is used to obtain uncertainties on predicted geophysical indices from the operational Space Weather Modeling FrameworkModel skill can be improved by considering the uncertainty on model input