Connecting solar wind turbulence to plasma parameters at L1 using multi-spacecraft coherence

Context. Solar wind propagation behavior has significant implications for solar wind forecasting and measurements. Variability in coherence and plasma turbulence under different plasma conditions is important for cross-satellite comparisons. Forecasting also depends on whether upstream measurements remain valid at the magnetosphere. Aims. We used computational methods to analyze magnetic coherence and connections to plasma parameters, utilizing multi-decade ACE and Wind measurements to capture turbulence behavior across a wide range of spatial separations and solar cycle phases. Methods. The measurements were separated into three frequency ranges within the inertial range of solar wind turbulence: in periods of 1–2.5 min, 2.5–10 min, and 10–30 min. We assessed the coherence in each frequency band using time-lagged cross-correlations and applied a clustering algorithm to identify connections between coherence and plasma parameters (velocity, proton density, flow pressure). We performed this analysis in the radial, nonradial, and total directions. Results. We used a k-means clustering algorithm to find that higher coherence in all cases is associated with smaller variance in plasma parameters. Taking this into consideration, we find a trivial association with the satellite separation or solar cycle phase. Small variations in dynamic pressure and velocity appear to be the best indicators of high coherence at these high-frequency inertial scales. Identifying connections between turbulence and plasma parameters could improve our understanding of the underlying physical processes. This information will also be vital for instrument calibration on future missions such as the Space Weather Follow On L1 (SWFO-L1).