| Name: | Shanmugha Balan |
| Affiliation: | Udaipur Solar Observatory |
| Conference ID : | ASI2024_315 |
| Title : | A Space Weather Modelling Framework to estimate CME parameters and Dst index at 1 AU using INFROS and DBM |
| Authors : | Shanmugha Balan (1, 2), Ranadeep Sarkar (3), Sandeep Kumar (1), Nandita Srivastava (1) |
| Authors Affiliation: | 1. Udaipur Solar Observatory, Physical Research Laboratory, Badi Road, Udaipur - 313001, India
2. Department of Physics, Birla Institute of Technology and Science, Pilani - 333031, India
3. Department of Physics, University of Helsinki, P.O. Box 64, Helsinki FI-00014, Finland |
| Mode of Presentation: | Poster |
| Abstract Category : | Sun, Solar System, Exoplanets, and Astrobiology |
| Abstract : | The geoeffectiveness of a space weather event at the magnetosphere is reflected in the Disturbance Storm Time (Dst) index. Most Dst prediction schemes require solar wind parameters at 1 AU such as the magnetic field (specifically the southward component of the IMF), plasma velocity and proton density. Since the magnetic cloud in a CME is the primary driver of CME-based space weather events, we develop a framework to estimate their impact. The geometry of a CME is first determined using a Graduated Cylindrical Shell (GCS) reconstruction scheme on near Sun observations of the CME. The magnetic parameters to constrain the CME flux-rope properties are derived using multi-wavelength remote-sensing observations of the source region of the CME on the Sun. This information is used as an input to the INFROS model (Sarkar et al. 2020) which estimates the magnetic field vectors of the IMF at 1 AU. The measured speed of the CME in the coronagraphic field of view would also enable calculation of the CME speed at 1 AU using the DBM model. Using these solar wind parameters as inputs, we utilize a set of semi-empirical Dst prediction models to estimate the space weather impact of CMEs. As a proof of concept, this framework is tested on CME events from solar cycle 24. Since all of the individual models in the framework are computationally inexpensive, this forecasting framework provides good predictions while remaining exceptionally simple. |
