The risks and impacts of space weather: Policy recommendations and initiatives
Abstract Space weather affects global technological systems and societies. Space weather, or the dynamic conditions on the Sun and in the space environment and their impacts on technological systems, can produce coronal mass ejections, solar energetic particles, and geomagnetic disturbances. These space weather events can cause extreme currents in the electric grid, widespread blackouts, and phone and internet communication failures both in space and within the Earth's atmosphere. Severe space weather can damage satellites used for global positioning, communications, and weather forecasting. It also creates a risk of radiation exposure to astronauts and commercial airline crews and passengers. Accurate forecast knowledge of the space weather threat and timing of events is critical to planning and preparation to minimize socioeconomic impacts. This paper reviews the scope of the space weather threat on the global community today. It describes current international and US public policy initiatives to mitigate risk to infrastructure, public safety, and human life. It examines recent international reports and US federal disaster reduction plans to meet these challenges and provides recommendations to increase public awareness and implement public policies to prepare, prevent, and recover from possible catastrophic failures of commercial and government infrastructures caused by a major space weather event.
The Economic Impact of Space Weather: Where Do We Stand?
Space weather describes the way in which the Sun, and conditions in space more generally, impact human activity and technology both in space and on the ground. It is now well understood that space weather represents a significant threat to infrastructure resilience, and is a source of risk that is wide‐ranging in its impact and the pathways by which this impact may occur. Although space weather is growing rapidly as a field, work rigorously assessing the overall economic cost of space weather appears to be in its infancy. Here, we provide an initial literature review to gather and assess the quality of any published assessments of space weather impacts and socioeconomic studies. Generally speaking, there is a good volume of scientific peer‐reviewed literature detailing the likelihood and statistics of different types of space weather phenomena. These phenomena all typically exhibit “power‐law” behavior in their severity. The literature on documented impacts is not as extensive, with many case studies, but few statistical studies. The literature on the economic impacts of space weather is rather sparse and not as well developed when compared to the other sections, most probably due to the somewhat limited data that are available from end‐users. The major risk is attached to power distribution systems and there is disagreement as to the severity of the technological footprint. This strongly controls the economic impact. Consequently, urgent work is required to better quantify the risk of future space weather events.
Autocorrelation method for temporal interpolation and short‐term prediction of ionospheric data
An autocorrelation method is developed for temporal interpolation and short‐term prediction of ionospheric characteristics. The ionospheric data are considered as a realization of a periodic process with randomly dispersed measured values superimposed on it. The autocorrelation function or its nonnalized autocorrelation coefficients are determined from the measured data over a period of 20–30 days, and on that basis an autocorrelation model is obtained. This model is then used to interpolate the missing values in the monthly tables of ionospheric characteristics, here called “gaps.” The interpolation at a given hour is performed by calculating weighting coefficients for the neighboring measured values. The procedure selects those measurement values around the gap which have the highest autocorrelation coefficients. The model can be used to extrapolate (predict) the data, treating the prediction period (usually 24 hours) as a gap placed at the end of the available data. The method also calculates the so‐called prediction error, which is found to be close to the standard deviation of the measured data. The interpolation and prediction error are estimated to be less than 12% in the case of ƒoF2.