Data Assimilation in the Solar Wind: Challenges and First Results.

Area of Science:

• Space Physics
• Computational Plasma Physics
• Geophysics

Background:

• Numerical Weather Prediction (NWP) heavily relies on Data Assimilation (DA) for improved forecast skill.
• Current space weather prediction, especially for solar wind, underutilizes DA due to data and technical limitations.
• Advances in NWP DA offer potential for enhancing solar wind forecasting.

Purpose of the Study:

• Investigate the application of advanced DA methods from NWP to solar wind prediction.
• Assess the performance of the Local Ensemble Transform Kalman Filter (LETKF) within the ENLIL solar wind model.
• Quantify the potential benefits of DA for improving solar wind forecasts.

Main Methods:

• Conducted twin experiments using the ENLIL solar wind model.
• Employed the Local Ensemble Transform Kalman Filter (LETKF) for data assimilation.
• Generated synthetic observations (density, temperature, momentum) at 0.6 AU with specified boundary conditions.

Main Results:

• The LETKF improved the model state at observation points and propagated these improvements towards Earth.
• Demonstrated enhanced forecast skill for both observed and unobserved variables in near-Earth space.
• Identified artificial wavelike structures caused by sharp gradients from single observations, which were advected towards Earth.

Conclusions:

• This study is the first to apply DA to solar wind prediction, offering an in-depth analysis.
• LETKF shows potential for improving solar wind prediction, but challenges related to observation analysis need mitigation.
• Future work can explore remote sensing data (e.g., Heliospheric Imagers) for DA in solar wind models.