Adapting ensemble‐calibration techniques to probabilistic solar‐wind forecasting

Edward‐Inatimi, N. O. ORCID: https://orcid.org/0009-0001-6211-5781, Owens, M. J. ORCID: https://orcid.org/0000-0003-2061-2453, Barnard, L. ORCID: https://orcid.org/0000-0001-9876-4612, Turner, H. ORCID: https://orcid.org/0000-0002-4012-8004, Marsh, M. ORCID: https://orcid.org/0000-0003-2765-0874, Gonzi, S. ORCID: https://orcid.org/0000-0002-0974-7392, Lang, M. and Riley, P. ORCID: https://orcid.org/0000-0002-1859-456X (2024) Adapting ensemble‐calibration techniques to probabilistic solar‐wind forecasting. Space Weather, 22 (12). e2024SW004164. ISSN 1542-7390 doi: 10.1029/2024SW004164

Abstract/Summary

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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/120136
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	AGU
Download/View statistics	View download statistics for this item