Analog ensemble forecasts of solar wind parameters: quantification of the predictability and time‐domain spectral performance

Simon, P. A. ORCID: https://orcid.org/0000-0003-2091-2093, Chen, C. H. K., Owens, M. J. ORCID: https://orcid.org/0000-0003-2061-2453 and Sishtla, C. ORCID: https://orcid.org/0000-0003-4236-768X (2025) Analog ensemble forecasts of solar wind parameters: quantification of the predictability and time‐domain spectral performance. Space Weather, 23 (7). e2025SW004473. ISSN 1542-7390 doi: 10.1029/2025SW004473

Abstract/Summary

Forecasting multiscale properties of the solar wind is one of the important aspects of space weather prediction as mesoscales, larger than 1 min, can affect the magnetosphere. Amongst forecasting techniques, the analog ensemble (AnEn) method allows the forecast of a quantity from its past behavior, is easy and quick to implement, and results in an ensemble of time series. A comparison of optimal AnEn forecasts of Wind spacecraft observations of near-Earth solar wind properties with the persistence and climatology baselines allows a quantification of the predictability of the magnetic and velocity components and magnitude. The AnEn predictions were found to be as accurate as persistence for short-term forecasts and climatology for long-term ones, and performed better than both baselines for more than 60% of the samples for a particular lead time. Furthermore, using an AnEn instead of the baselines enables prediction of the full spectrum of solar wind fluctuations. However, using the standard averaging method to generate a unique forecast from the AnEn ensemble results in a loss of power in the small-scale fluctuations. To prevent this loss, a new spectral reduction method is proposed and compared to the standard averaging method as well as the synodic recurrence baseline. The AnEn spectral-reduced forecast is shown to be more time-accurate than the synodic baseline and more frequency-accurate than the mean-reduced forecasts. Such a reduced forecast is then confirmed to be useful as a comparative baseline in performance diagnostics of space weather models.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/123504
Identification Number/DOI	10.1029/2025SW004473
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	American Geophysical Union
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