期刊
SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS
卷 15, 期 11, 页码 1461-1474出版社
AMER GEOPHYSICAL UNION
DOI: 10.1002/2017SW001679
关键词
solar wind; forecasting; ensembles; MHD
资金
- Science and Technology Facilities Council (STFC) [ST/M000885/1]
- National Aeronautics and Space Administration Living With a Star TRT program [NNX15AF39G]
- NASA [804336, NNX15AF39G] Funding Source: Federal RePORTER
- Science and Technology Facilities Council [ST/M000885/1] Funding Source: researchfish
- NERC [NE/P016928/1] Funding Source: UKRI
- STFC [ST/M000885/1] Funding Source: UKRI
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 1AU. 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).
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