Abstract/Summary

Studying solar-wind conditions is central to forecasting the impact of space weather on Earth. Under the assumption that the structure of this wind is constant in time and co-rotates with the Sun, solar-wind and thereby space-weather forecasts have been made quite effectively. Such co-rotation forecasts are well studied with decades of observations from STEREO and near-Earth spacecraft. Forecast accuracy is primarily determined by three factors: i) the longitudinal separation of spacecraft from Earth determines the corotation time (and hence forecast lead time) [δt] over which the solar wind must be assumed to be constant, ii) the latitudinal separation (or offset) between Earth and spacecraft [δθ]] determines the degree to which the same solar wind is being encountered at both locations, and iii) the solar cycle, via the sunspot number (SSN), acts as a proxy for both how fast the solar-wind structure is evolving and how much it varies in latitude. However, the precise dependencies factoring in uncertainties are a mixture of influences from each of these factors. Furthermore, for high-precision forecasts, it is important to understand what drives the forecast accuracy and its uncertainty. Here we present a causal inference approach based on information-theoretic measures to do this. Our framework can compute not only the direct (linear and nonlinear) dependencies of the forecast mean absolute error (MAE) on SSN, Δθ, and Δt, but also how these individual variables combine to enhance or diminish the MAE. We provide an initial assessment of this with the potential of aiding data assimilation in the future.