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Probabilistic solar wind forecasting using large ensembles of near-Sun conditions with a simple one-dimensional “upwind” scheme

Owens, M. J. ORCID: and Riley, P. (2017) Probabilistic solar wind forecasting using large ensembles of near-Sun conditions with a simple one-dimensional “upwind” scheme. Space Weather, 15 (11). pp. 1461-1474. ISSN 1542-7390

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To link to this item DOI: 10.1002/2017SW001679


Long lead-time space-weather forecasting requires accurate prediction of the near-Earth solar wind. The current state-of-the-art uses a coronal model to extrapolate the observed photospheric magnetic field to the upper corona, where it is related to solar wind speed through empirical relations. These near-Sun solar wind and magnetic field conditions provide the inner boundary condition to three-dimensional numerical magnetohydrodynamic (MHD) models of the heliosphere out to 1 AU. This physics-based approach can capture dynamic processes within the solar wind, which affect the resulting conditions in near-Earth space. However, this deterministic approach lacks a quantification of forecast uncertainty. Here, we describe a complementary method to exploit the near-Sun solar-wind information produced by coronal models and provide a quantitative estimate of forecast uncertainty. By sampling the near-Sun solar wind speed at a range of latitudes about the sub-Earth point, we produce a large ensemble (N = 576) of time series at the base of the Sun-Earth line. Propagating these conditions to Earth by a three-dimensional MHD model would be computationally prohibitive, thus a computationally-efficient one-dimensional “upwind” scheme is used. The variance in the resulting near-Earth solar wind speed ensemble is shown to provide an accurate measure of the forecast uncertainty. Applying this technique over 1996-2016, the upwind ensemble is found to provide a more “actionable” forecast than a single deterministic forecast; potential economic value is increased for all operational scenarios, but particularly when false alarms are important (i.e., where the cost of taking mitigating action is relatively large).

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:73315
Publisher:American Geophysical Union


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