Abstract
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).
Highlights
Variability in near-Earth solar wind conditions can lead to the energization of the terrestrial magnetosphere, resulting in disruption to power grids, communications, and satellite operations, as well as threat to health of humans in space and on high-altitude aircraft (Cannon et al, 2013; Hapgood, 2011)
We find the root-mean-square error (RMSE) for the single MHD realization to be 123 km sÀ1, very similar to what has been reported by previous studies (Owens et al, 2008), while the upwind ensemble median has an RMSE of 107 (101) km sÀ1
As the upwind technique tends to smooth the 1 AU solar wind speed and does not capture the full solar wind speed extremes produced by the MHD solar wind model, it may produce a lower RMSE without providing a more actionable forecast; taken to an extreme, a purely climatological forecast of no solar wind variation can sometimes have a lower RMSE than an accurate solar wind forecast with small timing errors in a forecast
Summary
Variability in near-Earth solar wind conditions can lead to the energization of the terrestrial magnetosphere, resulting in disruption to power grids, communications, and satellite operations, as well as threat to health of humans in space and on high-altitude aircraft (Cannon et al, 2013; Hapgood, 2011). At solar minimum, the position and width of the modeled slow wind band can be quite sensitive to the strength of the poorly observed polar photospheric fields, leading to large solar wind speed uncertainties at the sub-Earth point (Bertello et al, 2014; Sun et al, 2011) Other observations, such as interplanetary scintillation (e.g., Breen et al, 2006) or heliospheric imagers (Eyles et al, 2009), can in principle provide a more direct measure of near-Sun solar wind conditions. The forecast value of this approach is compared with that from a single deterministic MHD forecast and climatology
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