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.; Marsh, M. ORCID: https://orcid.org/0000-0003-2765-0874; Gonzi, S. ORCID: https://orcid.org/0000-0002-0974-7392; Lang, M.; Riley, P. ORCID: https://orcid.org/0000-0002-1859-456X. 2024 Adapting Ensemble‐Calibration Techniques to Probabilistic Solar‐Wind Forecasting. Space Weather, 22 (12). 20, pp. 10.1029/2024SW004164

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Official URL: https://agupubs.onlinelibrary.wiley.com/doi/10.102...

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:	Publication - Article
Digital Object Identifier (DOI):	10.1029/2024SW004164
ISSN:	1542-7390
Additional Keywords:	solar wind, calibration, ensemble method, forecasting, forecast verification
Date made live:	27 Dec 2024 15:55 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/538603

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1029/2024SW004164

ISSN:

1542-7390

Additional Keywords:

solar wind, calibration, ensemble method, forecasting, forecast verification

Date made live:

27 Dec 2024 15:55 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/538603