Extracting Inner‐Heliosphere Solar Wind Speed Information From Heliospheric Imager Observations

Abstract

We present evidence that variability in the STEREO‐A Heliospheric Imager (HI) data is correlated with in situ solar wind speed estimates from WIND, STEREO‐A, and STEREO‐B. For 2008–2012, we compute the variability in HI differenced images in a plane‐of‐sky shell between 20 to 22.5 solar radii and, for a range of position angles, compare daily means of HI variability and in situ solar wind speed estimates. We show that the HI variability data and in situ solar wind speeds have similar temporal autocorrelation functions. Carrington rotation periodicities are well documented for in situ solar wind speeds, but, to our knowledge, this is the first time they have been presented in statistics computed from HI images. In situ solar wind speeds from STEREO‐A, STEREO‐B, and WIND are all are correlated with the HI variability, with a lag that varies in a manner consistent with the longitudinal separation of the in situ monitor and the HI instrument. Unlike many approaches to processing HI observations, our method requires no manual feature tracking; it is automated, is quick to compute, and does not suffer the subjective biases associated with manual classifications. These results suggest we could possibly estimate solar wind speeds in the low heliosphere directly from HI observations. This motivates further investigation, as this could be a significant asset to the space weather forecasting community; it might provide an independent observational constraint on heliospheric solar wind forecasts, through, for example, data assimilation. Finally, these results are another argument for the potential utility of including a HI on an operational space weather mission.

Why are ELEvoHI CME arrival predictions different if based on STEREO-A or STEREO-B heliospheric imager observations?

“…However, this model does not consider preceding CMEs and is likely not valid in such cases. An additional approach to infer the ambient solar wind conditions in the low heliosphere is shown in Barnard, Owens, Scott, and Jones (2019). In this study the authors established a statistical relationship between the solar wind speed in the low heliosphere and the variability in HI images.…”

Why are ELEvoHI CME Arrival Predictions Different if Based on STEREO‐A or STEREO‐B Heliospheric Imager Observations?

“…An additional approach to infer the ambient solar wind conditions in the low heliosphere is shown in Barnard et al. (2019). In this study, the authors established a statistical relationship between the solar wind speed in the low heliosphere and the variability in HI images.…”

Quantifying the latitudinal representivity of in situ solar wind observations

“…This methodology has long been used in atmospheric and oceanic sciences (Sasaki, 1970;Ghil & Malanotte-Rizzoli, 1991;Rabier, 2005) and has more recently been adopted in relatively data-rich areas of space physics (Bust & Mitchell, 2008;Hickmann et al, 2015;Glauert et al, 2018;Temmer et al, 2018;Aseev & Shprits, 2019;Elvidge & Angling, 2019). In the heliosphere, we have recently demonstrated assimilation of in situ solar wind observations (Lang et al, 2017;Lang & Owens, 2019), though the same methods should be broadly applicable to other remote observing techniques in the future (e.g., Manoharan, 2012;Barnard et al, 2019). In order to accommodate in situ measurements into the model state, it is necessary to know the spatial domain over which measurements are representative and the error introduced by any spatial mismatch between model and measurement point (Waller et al, 2014;Janji et al, 2018).…”