Abstract
Probabilistic Hazard Assessment (PHA) provides an appropriate methodology for assessing space weather hazards and their impact on technology. PHA is widely used in geosciences to determine the probability of exceedance of critical thresholds, caused by one or more hazard sources. PHA has proved useful with limited historical data to estimate the likelihood of specific impacts. PHA has also driven the development of empirical and physical models, or ensembles of models, to replace measured data. Here we aim to highlight the PHA method to the space weather community and provide an example of how it could be used. In terms of space weather impact, the critical hazard thresholds might include the Geomagnetically Induced Current in a specific high voltage power transformer neutral, or the local pipe-to-soil potential in a particular metal pipe. We illustrate PHA in the space weather context by applying it to a twelve-year dataset of Earth-directed solar Coronal Mass Ejections (CME), which we relate to the probability that the global three-hourly geomagnetic activity index Kp exceeds specific thresholds. We call this a “Probabilistic Geomagnetic Hazard Assessment”, or PGHA. This provides a simple but concrete example of the method. We find that the cumulative probability of Kp > 6−, > 7−, > 8− and Kp = 9o is 0.359, 0.227, 0.090, 0.011, respectively, following observation of an Earth-directed CME, summed over all CME launch speeds and solar source locations. According to the historical Kp distribution, this represents an order of magnitude increase in the a priori probability of exceeding these thresholds. For the lower Kp thresholds, the results are somewhat distorted by our exclusion of coronal hole high-speed stream effects. The PHGA also reveals useful probabilistic associations between solar source location and subsequent maximum Kp for operational forecasters.
Highlights
The impact of severe space weather on technological systems can be estimated by deterministic or probabilistic means
We provide a simple example of Coronal Mass Ejections (CME) observations in relation to the Kp index (Matzka et al, 2021a: the Kp index is expressed on a scale of thirds from 0o, 0+, 1À, 1o, 1+, 2À, 2o, and so on, up to 9o), to illustrate the general method
If we identify the source magnitude, “m”, in equation (1), with the Earth-directed Coronal Mass Ejections (EDCME) speed, V, and the EDCME source location bin with the parameter “r”, the “Intensity Measure” (IM) is Kp, and the thresholds for Kp are from 6À, up to 9o
Summary
The impact of severe space weather on technological systems can be estimated by deterministic or probabilistic means. A series of coupled physical or empirical models of processes between the Sun and Earth may provide a deterministic estimate of ground-level magnetic field variations (dB/dt). These variations, in turn, may drive geomagnetically induced currents (GIC) in power grids and other grounded networks. One way of quantifying the precision and uncertainty in deterministic model outputs is ensemble modelling. This can be useful in determining a mean and a range of behaviours commensurate with varied input conditions and uncertain knowledge of variables (or physical models) at key steps in the modelling chain. Probabilistic techniques are an attractive alternative, or at least complementary method, to the deterministic, physics-driven approach
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