Statistical quantification of the variation in return period, amplitude and duration of space within and between successive solar cycles.
Abstract
Successive solar cycles differ in duration and activity level. Solar wind parameters and geomagnetic indices fluctuate as they monitor near-Earth response to solar activity. Moderate to large excursions in indices, such as AE and Dst, populate tails of distinct statistical distributions for different solar maxima. However, whilst the overall level of activity varies with that of successive solar cycles, each index has an underlying functional form for its statistical distribution [1] which does not change from one solar maximum to the next. This was found for distribution functions of the observed data records. Quantifying space weather risk requires understanding how the occurrence frequency of events of a given size varies with the strength of each solar cycle. One definition of an event seen in a time series is a burst, that is, an excursion above/below a fixed threshold. Bursts in space weather relevant parameters have been the subject of extensive study. Relationships between duration, burst size and return time have been identified [2] and found to be insensitive to the level of solar activity [3]. Crossing theory constrains average burst properties of a stationary time series via distribution functions of the raw observations [4]. We have combined these statistical properties of space weather relevant parameters to determine how they place constraints on the way in which the size, duration, and return time of space weather events can change across and between different solar cycles, thus constraining space weather risk.
[1] Chapman, S. C., Watkins, N. W., & Tindale, E. (2018). Space Weather. doi:10.1029/2018SW001884 [2] Uritsky, V.M., Klimas, A.J. and Vassiliadis, D. (2001). GRL. doi:10.1029/2001GL013026 [3] Tindale, E., Chapman, S. C., Moloney, N. R., & Watkins, N. (2018). JGR. doi:10.1029/2018JA025740 [4] Chapman, S. C., Watkins, N., & Stainforth, D. A. (2019). GRL. doi:10.1029/2018GL081004- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMSM0040010B
- Keywords:
-
- 0530 Data presentation and visualization;
- COMPUTATIONAL GEOPHYSICS;
- 1914 Data mining;
- INFORMATICS;
- 1942 Machine learning;
- INFORMATICS;
- 2722 Forecasting;
- MAGNETOSPHERIC PHYSICS