Data Assimilative Optimization of WSA Source Surface and Interface Radii using Particle Filtering

The Wang‐Sheeley‐Arge (WSA) model estimates solar wind speed and interplanetary magnetic field polarity in the inner heliosphere using global photospheric magnetic field maps. WSA employs the Potential Field Source Surface (PFSS) and Schatten Current Sheet (SCS) models to determine the Sun's global coronal magnetic field. The PFSS and SCS models are connected through two radial parameters, the source surface and interface radii, which specify the overlap region between the inner SCS and outer PFSS models. Though both radii values are adjustable, they have typically been fixed to 2.5 solar radii. Our work highlights how solar wind predictions improve when the radii are allowed to vary over time. Data assimilation using particle filtering (sequential Monte Carlo) is used to infer optimal values over a fixed time window. Solar wind model predictions and satellite observations are compared with a newly developed quality‐of‐agreement prediction metric. The agreement metric between the model and observations is assumed to correspond to the probability of the two key WSA model parameters, the source surface and interface radii, where the highest metric value implies the optimal radii. We find that the optimal particle filter values of solar radii can perform twice as well as standard values for an exploratory period during Carrington Rotation 1901, with these values also reducing nonphysical kinking effects seen in solar magnetic field lines. Data assimilation choices of input realization and time frame have implications for variation in the solar wind over time. We present this work's theoretical context and practical applications for prediction accuracy. Plain Language Summary The solar wind drives electromagnetic disturbances on the ground, which can cause satellite disruptions and many other adverse affects on human technology, so it is important to predict the solar wind variability accurately. For several decades, the space weather community has developed physical and empirical models to estimate the state of the solar wind. Some of the models make assumptions about the magnetic field topology of the Sun. We analyze satellite solar wind observations with different model parameter assumptions to help make more accurate predictions. Key Points Key Wang‐Sheeley‐Arge (WSA) model parameters were optimized to better predict solar wind speed and IMF polarity in the heliosphere New observation and model difference metric provides quantitative comparison between pre