Data assimilation in the solar wind: challenges and first results

Data Assimilation (DA) is used extensively in numerical weather prediction (NWP) to improve forecast skill. Indeed, improvements in forecast skill in NWP models over the past 30 years have directly coincided with improvements in DA schemes. At present, due to data availability and technical challenges, DA is underused in space weather applications, particularly for solar wind prediction. This paper investigates the potential of advanced DA methods currently used in operational NWP centres to improve solar wind prediction. To develop the technical capability, as well as quantify the potential benefit, twin experiments are conducted to assess the performance of the Local Ensemble Transform Kalman Filter (LETKF) in the solar wind model ENLIL. Boundary conditions are provided by the Wang-Sheeley-Arge coronal model and synthetic observations of density, temperature and momentum generated every 4.5hr at 0.6AU. While in-situ spacecraft observations are unlikely to be routinely available at 0.6AU, these techniques can be applied to remote sensing of the solar wind, such as with Heliospheric Imagers or Interplanetary Scintillation. The LETKF can be seen to improve the state at the observation location and advect that improvement towards the Earth, leading to an improvement in forecast skill in near Earth space for both the observed and unobserved variables. However, sharp gradients caused by the analysis of a single observation in space resulted in artificial wave-like structures being advected towards Earth. This paper is the first attempt to apply DA to solar wind prediction, and provides the first in-depth analysis of the challenges and potential solutions