Probabilistic Solar Wind Forecasting Using Large Ensembles of Near‐Sun Conditions With a Simple One‐Dimensional "Upwind" Scheme. Issue 11 (6th November 2017)

• Record Type: Journal Article
• Authors: Owens, Mathew J.; Riley, Pete
• Is Part Of: Space weather. Volume 15:Issue 11(2017:Nov.)
• Page Start: 1461
• Page End: 1474
• Publication Date: 2017-11-06
• Subjects: solar wind -- forecasting -- ensembles -- MHD; Space environment -- Periodicals; 551.509992
• DOI: 10.1002/2017SW001679
• Languages: English
• ISSNs: 1542-7390

Abstract

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). Key Points: An ensemble sampling of near‐Sun conditions provides a better solar wind forecast than a single deterministic forecast The ensemble approach both improves the "best" estimate and provides a reliable measure of solar wind forecast uncertainty The ensemble approach is particularly useful in operational settings where false alarms are important (i.e., high cost of mitigating action)