Adapting Ensemble‐Calibration Techniques to Probabilistic Solar‐Wind Forecasting.

Solar‐wind forecasting is critical for predicting events which can affect Earth's technological systems. Typically, forecasts combine coronal model outputs with heliospheric models to predict near‐Earth conditions. Ensemble forecasting generates sets of outputs to create probabilistic forecasts which quantify forecast uncertainty, vital for reliable/actionable forecasts. We adapt meteorological methods to create a calibrated solar‐wind ensemble and probabilistic forecast for ambient solar wind, a prerequisite for accurate coronal mass ejection (CME) forecasting. Calibration is achieved by adjusting ensemble inputs/outputs to align the ensemble spread with observed event frequencies. We produce hindcasts in near‐Earth space using coronal‐model output over Solar Cycle 24, as input to Heliospheric Upwind eXtrapolation with time dependence (HUXt) solar‐wind model. Making spatial perturbations to the coronal model output at 0.1 AU, we produce ensembles of inner‐boundary conditions for HUXt, evaluating how forecast accuracy was impacted by the scales of perturbations applied. We found optimal spatial perturbations described by Gaussian distributions with variances of 20° latitude and 10° longitude; these might represent spatial uncertainty within the coronal model. This produced probabilistic forecasts better matching observed frequencies. Calibration improved forecast reliability, reducing the Brier score by 9% and forecast decisiveness increasing AUC ROC score by 2.5%. Improvements were subtle but systematic. Additionally, we explored statistical post‐processing to correct over‐confidence bias, improving forecast actionability. However, this method, applied post‐run, does not affect the solar‐wind state used to propagate CMEs. This work represents the first formal calibration of solar‐wind ensembles, laying groundwork for comprehensive forecasting systems like a calibrated multi‐model ensemble. Plain Language Summary: Current solar‐wind forecasting methods combine coronal and heliospheric models to predict physical properties of the solar wind near the Earth. Ensemble forecasting combines many individual forecasts to provide probabilistic predictions and estimate forecast uncertainty. However, the uncertainty is poorly constrained for a naively generated ensemble forecast and needs to be calibrated to be reliable. We adapt established meteorological methods to create a calibrated solar‐wind speed ensemble forecast, improving forecast accuracy by adjusting model inputs based on past solar activity. By applying spatial perturbations to model conditions, we find the optimal extent of spatial perturbations that improve forecast reliability, better matching observed event frequencies. This approach is a step toward more comprehensive and reliable solar‐wind forecasting systems. Key Points: Ensemble methods capture model uncertainty. Solar‐wind ensembles are generated through spatial perturbations to inner‐boundary conditionsCalibration aligns forecast probabilities with observed frequencies, to do so we find ideal perturbation scales for a solar‐wind ensembleCalibrating the ensemble improved forecast performance; optimal perturbations might link to spatial uncertainty within the inner‐boundary [ABSTRACT FROM AUTHOR]