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.

中文翻译：

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使用粒子滤波的WSA源表面和界面半径的数据同化优化

Wang-Sheeley-Arge（WSA）模型使用全局光球磁场图估算了太阳内部层中太阳风的速度和行星际磁场的极性。WSA使用势场源表面（PFSS）和Schatten Current Sheet（SCS）模型来确定太阳的整体日冕磁场。PFSS和SCS模型通过两个径向参数连接，即源表面和界面半径，这些参数指定内部SCS和外部PFSS模型之间的重叠区域。尽管两个半径值都是可调的，但通常将其固定为2.5太阳半径。我们的工作重点介绍了在允许半径随时间变化时，太阳风预测将如何改善。使用粒子滤波（顺序蒙特卡洛）的数据同化用于推断固定时间窗口内的最佳值。将太阳风模型的预测和卫星观测与最新开发的协议质量预测指标进行了比较。假设模型和观测值之间的一致性度量对应于两个关键的WSA模型参数的概率，即源表面和界面半径，其中最高度量值表示最佳半径。我们发现，在1901年的卡灵顿旋转期间，太阳半径的最佳粒子滤波值可以达到标准值的两倍，并且这些值还减少了太阳磁场线中的非物理扭结效应。输入实现和时间范围的数据同化选择会影响太阳风随时间的变化。我们介绍了这项工作的理论背景和预测精度的实际应用。