Combining STEREO SECCHI COR2 and HI1 images for automatic CME front edge tracking

Abstract

COR2 coronagraph images are the most commonly used data for coronal mass ejection (CME) analysis among the various types of data provided by the STEREO (Solar Terrestrial Relations Observatory) SECCHI (Sun-Earth Connection Coronal and Heliospheric Investigation) suite of instruments. The field of view (FOV) in COR2 images covers 2-15 solar radii (Rs) that allow for tracking the front edge of a CME in its initial stage to forecast the lead-time of a CME and its chances of reaching the Earth. However, estimating the lead-time of a CME using COR2 images gives a larger lead-time, which may be associated with greater uncertainty. To reduce this uncertainty, CME front edge tracking should be continued beyond the FOV of COR2 images. Therefore, heliospheric imager (HI1) data that covers 15-90 Rs FOV must be included. In this paper, we propose a novel automatic method that takes both COR2 and HI1 images into account and combine the results to track the front edges of a CME continuously. The method consists of two modules: pre-processing and tracking. The pre-processing module produces a set of segmented images, which contain the signature of a CME, for both COR2 and HI1 separately. In addition, the HI1 images are resized and padded, so that the center of the Sun is the central coordinate of the resized HI1 images. The resulting COR2 and HI1 image set is then fed into the tracking module to estimate the position angle (PA) and track the front edge of a CME. The detected front edge is then used to produce a height-time profile that is used to estimate the speed of a CME. The method was validated using 15 CME events observed in the period from January 1, 2008 to August 31, 2009. The results demonstrate that the proposed method is effective for CME front edge tracking in both COR2 and HI1 images. Using this method, the CME front edge can now be tracked automatically and continuously in a much larger range, i.e., from 2 to 90 Rs, for the first time. These improvements can greatly help in making the quantitative CME analysis more accurate and have the potential to assist in space weather forecasting.

CMEs in the Heliosphere: II. A Statistical Analysis of the Kinematic Properties Derived from Single-Spacecraft Geometrical Modelling Techniques Applied to CMEs Detected in the Heliosphere from 2007 to 2017 by STEREO/HI-1

“…Further difficulties of CME detection in HI are caused by the jitter experienced by STEREO-B, which is the result of vibrations caused by the attitude-control reaction wheels. Kirnosov, Chang, and Pulkkinen (2016), however, combine COR-2 and HI-1 observations to automatically detect CME leading edges in the imagery, and CACTusCAT, which automatically detects CMEs in HI, was produced as part of HELCATS (Pant et al, 2016). The merits and difficulties of the manual versus automaticdetection methods are tackled directly by Yashiro, Michalek, and Gopalswamy (2008), who compare the CDAW and CACTus data sets.…”

Comparing the Heliospheric Cataloging, Analysis, and Techniques Service (HELCATS) Manual and Automatic Catalogues of Coronal Mass Ejections Using Solar Terrestrial Relations Observatory/Heliospheric Imager (STEREO/HI) Data

“…Barnard et al (2015) reported on a semi-automatic method (J-tracker) using STEREO HI data. Kirnosov, Chang, and Pulkkinen (2016) presented results in automatic tracking of CMEs using COR2 and HI-1 images. All these methods were applied to a reduced set of events.…”