Automated detection of coronaL MAss ejecta origiNs for space weather AppliCations (ALMANAC)

Abstract

Alerts of potentially hazardous coronal mass ejections (CME) are based on the detection of rapid changes in remote observations of the solar atmosphere. This paper presents a method that detects and estimates the central coordinates of CME eruptions in Extreme Ultraviolet (EUV) data, with the dual aim of providing an early alert, and giving an initial estimate of the CME direction of propagation to a CME geometrical model. In particular, we plan to link the ALMANAC method to the CME detection and characterisation module of the Space Weather Empirical Ensemble Package (SWEEP), which is a fully automated modular software package for operational space weather capability currently being developed for the UK Meteorological Office. In this work, ALMANAC is applied to observations by the Atmospheric Imaging Assembly (AIA) aboard the Solar Dynamics Observatory (SDO). As well as presenting the method, a proof of concept test is made on a limited set of data associated with twenty halo CMEs recorded by the Coordinated Data Analysis Workshop (CDAW) catalogue near the activity maximum of solar cycle 24. SDO/AIA data for each event is processed at 6 minute cadence to identify the on-disk location and time of each CME. The absolute mean deviance between the ALMANAC and CDAW source event coordinates are within 37.05 +- 29.71 minutes and 11.01 +- 10.39 degrees. These promising results give a solid foundation for future work, and will provide initial constraints to an automated CME alert and forecasting system.

References in corpus (24)

• Forecasting the Arrival Time of Coronal Mass Ejections: Analysis of the CCMC CME Scoreboard
• Characteristics of kinematics of a coronal mass ejection during the 2010 August 1 CME-CME interaction event
• Global Trends of CME Deflections Based on CME and Solar Parameters
• Solar Magnetic Feature Detection and Tracking for Space Weather Monitoring
• Forward modeling of coronal mass ejection flux ropes in the inner heliosphere with 3DCORE
• Automatic Detection and Tracking of CMEs II: Multiscale Filtering of Coronagraph Data
• Predicting Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and Recurrent Neural Networks
• Forecasting the Ambient Solar Wind with Numerical Models. II. An Adaptive Prediction System for Specifying Solar Wind Speed Near the Sun
• Connecting Coronal Mass Ejections to their Solar Active Region Sources: Combining Results from the HELCATS and FLARECAST Projects
• CorPITA: An Automated Algorithm for the Identification and Analysis of Coronal "EIT Waves"
• Improving solar wind forecasting using Data Assimilation
• Solar origins of a strong stealth CME detected by Solar Orbiter
• Connecting 3D evolution of Coronal Mass Ejections to their Source Regions
• Asymmetric expansion of coronal mass ejections in the low corona
• An Observationally Constrained Analytical Model for Predicting the Magnetic Field Vectors of ICMEs at 1 AU
• A new technique to provide realistic input to CME forecasting models
• Automated detection of coronal mass ejections in three-dimensions using multi-viewpoint observations
• Onboard Automated CME Detection Algorithm for Visible Emission Line Coronagraph on ADITYA-L1
• Erupting filaments with large enclosing flux tubes as sources of high-mass 3-part CMEs, and erupting filaments in the absence of enclosing flux tubes as sources of low-mass unstructured CMEs
• On the Variation of Volumetric Evolution of CMEs from Inner to Outer Corona
• Automated Detection of Accelerating Solar Eruptions using Parabolic Hough Transform
• Quantifying the uncertainty in CME kinematics derived from geometric modelling of Heliospheric Imager data
• AWARE: An algorithm for the automated characterization of EUV waves in the solar atmosphere
• Multi-Stranded Coronal Loops: Quantifying Strand Number and Heating Frequency from Simulated Solar Dynamics Observatory (SDO) Atmospheric Imaging Assembly (AIA) Observations