Solar wind data assimilation in an operational context: Use of
near-real-time data and the forecast value of an L5 monitor.
Abstract
For accurate and timely space weather forecasting, advanced knowledge of
the ambient solar wind is required, both for its direct impact on the
magnetosphere and for accurately forecasting the propagation of coronal
mass ejections to Earth. Data assimilation (DA) combines model output
and observations to form an optimum estimation of reality. Initial
experiments with assimilation of in situ solar wind observations suggest
the potential for significant improvement in the forecast skill of
near-Earth solar wind conditions. However, these experiments have
assimilated science-quality observations, rather than near-real-time
(NRT) data that would be available to an operational forecast scheme.
Here, we assimilate both NRT and science observations from the Solar
Terrestrial Relations Observatory (STEREO) and near-Earth observations
from the Advanced Composition Explorer (ACE) and Deep Space Climate
Observatory (DSCOVR) spacecraft. We show that forecasts using NRT data
are comparable to those based on science-level data. This suggests that
an operational solar wind DA scheme would provide significant forecast
improvement, with reduction in the mean absolute error (MAE) of solar
wind speed around 45% over forecasts without DA. With a proposed space
weather monitor planned for the L5 Lagrange point, we also quantify the
solar wind forecast gain expected from L5 observations alongside
existing observations from L1. This is achieved using particular
configurations of the STEREO and L1 spacecraft. There is a 15.5%
improvement for forecast lead times of less than 5 days when
observations from L5 are assimilated alongside those from L1, compared
to assimilation of L1 observations alone.