HELCATS

The primary goal of WP4 is to provide researchers with the ability to view and obtain the principal CME parameters (e.g. direction, speed) at a glance, following the complete chain of imaging and in-situ observations from the Sun out to 1 AU. We will bring together totally independent data sets, bridging the gap between remote and in-situ observations. The CME database will be optimized to aid the space physics community’s search for clues on the origin, propagation, morphology, and planetary effects of CMEs. We will not only furnish the event catalogue with relevant parameters, but will also provide the linkage (including physical interpretations) between different CME-related structures in different datasets. This resource will be useful for future missions (e.g. Solar Orbiter).

The work package consists of three tasks.

• Comparing to coronal sources
• Comparing to in-situ measurements
• Assessing the validity of the HI modelling

The work package will construct a community-oriented online database of in-situ CMEs, and their main parameters, from 2007 to 2015 (minimum through maximum and early-declining phase of solar cycle 24) that fully exploits suitable currently-operating heliospheric space missions. The aim is to establish definitive links to coronal sources (Task 1) and in-situ signatures (Task 2) for the CMEs in the STEREO/HI catalogue (from WP2), based on the modelling results from WP3 that form the backbone that connects the HI data in space and time to the coronal data (by backward projection) and the in-situ data (by forward projection). This activity will also benchmark using HI modelling to better predict CME arrival at various heliospheric locations using in-situ data from multiple sources, with the aim of maximizing the prediction lead time and minimizing the prediction error.

Task 1: Comparing to coronal sources

Well-established signatures of the CMEs in the STEREO/HI catalogue (WP2 and 3) will be identified in the low corona and photospheric magnetograms (flares, filaments, EUV post-eruption arcades, coronal dimmings, EUV waves, bipolar regions). The modelling methods used on HI data (in WP3) will produce windows for CME launch time and position on the solar disk, acting as proxies for identification of the sources in the low corona and photosphere.

Task 2: Comparing to in-situ measurements

This task will combine in-situ observations from many spacecraft into a single comprehensive CME database by extensive analysis (of magnetic field, thermal plasma, suprathermal electrons and compositional data) during the estimated CME arrival times (from WP3). Use of a physics-based phenomenological characterization of CMEs and their surrounding solar wind, which we have carefully evaluated to optimize comparison with remote observations, will maximize the benefit to us and other researchers in understanding CME effects. Task 4.2 will consist of the following: 1. Categorizing CMEs based on their physical structure observed in-situ (e.g. flux rope/non-flux rope CMEs, complex CMEs, compound streams) and calculating relevant parameters (e.g. shock stand-off distance, expansion speed). 2. Modelling flux-rope CMEs using Grad-Shafranov (GS) reconstruction. 3. Categorizing CMEs based on ambient solar wind speed/interplanetary magnetic field structure. 4. Analysis of sheath/CME density substructures.

Task 3: Assessing the validity of the HI modelling

In Task 3 we statistically analyse results from Tasks 1 and 2, with STEREO/HI CME parameters from WP3 forming the backbone. The WP3 HI modelling results (over a large portion of solar cycle 24) will be assessed in terms of their reliability, in terms of connecting the different data sets, and their potential for space weather prediction. Direct comparisons between HI and in-situ data sets are possible: (1) comparing HI-derived CME direction with spacecraft position (hit or miss predictions), (2) comparing HI-derived CME arrival times/speeds with in-situ CME arrival times/speeds, and (3) comparing white-light HI morphology with in-situ flux rope orientation. Questions that can be addressed are: How can different CME substructures (sheaths, flux ropes) be identified from HI data? How well can CME arrival times/speeds be forecast using HI data, and how can this be optimized? What is the outcome of binary classifications of CME hits and misses? Are there CME, sheath or substructure properties that optimize predictive capability, and why? Moreover, comparing HI modelling and source region properties will address questions on source position versus CME propagation direction. To test relations for forecasting magnetic clouds, in-situ magnetic structures

Description of work package.

Description of work package.

Description of work package.