A Variational Approach to Data Assimilation in the Solar Wind. Issue 1 (21st January 2019)

• Record Type: Journal Article
• Authors: Lang, Matthew; Owens, Mathew J.
• Is Part Of: Space weather. Volume 17:Issue 1(2019)
• Page Start: 59
• Page End: 83
• Publication Date: 2019-01-21
• Subjects: data assimilation -- solar wind -- variational -- 4DVar -- ensemble Kalman smoother; Space environment -- Periodicals; 551.509992
• DOI: 10.1029/2018SW001857
• Languages: English
• ISSNs: 1542-7390

Abstract

Abstract: Variational data assimilation (DA) has enabled huge improvements in the skill of operational weather forecasting. In this study, we use a simple solar‐wind propagation model to develop the first solar‐wind variational DA scheme. This scheme enables solar‐wind observations far from the Sun, such as at 1 AU, to update and improve the inner‐boundary conditions of the solar wind model (at 30 solar radii). In this way, observational information can be used to improve estimates of the near‐Earth solar wind, even when the observations are not directly downstream of the Earth. Using controlled experiments with synthetic observations, we demonstrate this method's potential to improve solar wind forecasts, though the best results are achieved in conjunction with accurate initial estimates of the solar wind. The variational DA scheme is also applied to Solar‐Terrestrial Relations Observatory (STEREO) in situ observations using initial solar wind conditions supplied by a coronal model of the observed photospheric magnetic field. We consider the period October 2010 to October 2011, when the STEREO spacecraft were approximately 80 ∘ ahead/behind Earth in its orbit. For 12 of 13 Carrington Rotations, assimilation of STEREO data improves the near‐Earth solar wind estimate over the nonassimilated state, with a 18.4% reduction in the root mean square error. The largest gains are made by the DA during times when the steady‐state assumption of the coronal models breaks down. While applying this pure variational approach to complex solar‐wind models is technically challenging, we discuss hybrid DA approaches which are simpler to implement and may retain many of the advantages demonstrated here. Plain Language Summary: In order to forecast space weather, it is necessary to accurately model the solar wind, the continually expanding solar atmosphere which fills the solar system. At present, telescopic observations of the Sun's surface are used to provide the starting conditions for computer simulations of the solar wind, which then propagate conditions all the way from the Sun to Earth. But spacecraft also make direct measurements of the solar wind, which provide useful additional information that is not presently used. In this study we use a simple solar wind model to develop a method to routinely "assimilate" spacecraft observations into the model and thus improve space weather forecasts. This data assimilation (DA) approach closely follows that of terrestrial weather prediction, where DA has led to increasingly accurate forecasts. We use artificial and real spacecraft observations to test the new solar wind DA method and show that the error in predicting the near‐Earth solar wind can be reduced by around a fifth using available observations. Key Points: This paper develops a variational data assimilation (DA) method and applies it to a simple solar wind propagation model Such a DA scheme enables the inner boundary of the solar wind model to be updated using observations in near‐Earth space Experiments performed with both synthetic and STEREO observations show that the DA method is able to reduce errors in the solar wind speeds