Journal
JOURNAL OF ATMOSPHERIC AND SOLAR-TERRESTRIAL PHYSICS
Volume 208, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jastp.2020.105407
Keywords
Solar flares; Space weather; The Sun; Hidden Markov model; Forecasting; GOES (Geostationary Operational Environmental Satellites)
Funding
- NAWA, Poland [PPN/ULM/20 19/1/00087/DEC/1]
- Beethoven, Poland [DFG-NCN 2016/23/G/ST1/04083]
- Narodowe Centrum Nauki (NCN), Poland Sonata Grant [2019/35/D/HS4/00369]
Ask authors/readers for more resources
Solar flares are large explosions in the sun's atmosphere. They can damage satellites and overload electrical systems. To manage that risk, finding methods of efficiently predicting future events is very important. In this paper we introduce a full-Sun flare prediction method based on the Hidden Markov modelling with two hidden states. We concentrate on the soft X-ray emission data near the minimum of solar cycle and consider two different driving dynamics for both states, namely the independent identically distributed (IID) random variables and the autoregressive (AR) processes, the latter introducing a memory structure. We compare prediction performance for the IID and AR approaches and also with a naive prediction equal to the last observation. The solar X-flux dynamics is predicted by using the day-ahead forecasts. We calculate point and interval forecasts and perform relevant statistical tests to choose the best method. It appears that the AR approach is clearly superior to the IID and naive both by means of point and interval forecasts. Moreover, it can well detect the higher state which can lead to very strong energy releases. Significant development of the model would be necessary to forecast solar flares over an entire solar cycle.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available