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.
A method for the automated detection of solar radio bursts in dynamic spectra
The variability of the solar corona, including flares and coronal mass ejections, affects the space environment of the Earth (heating and ionization of the atmosphere, magnetic field disturbances, and bombardment by high-energy particles). Electromagnetic emissions are the first signatures of a solar eruptive event which by modifying the electron density in the ionosphere may affect airborne technology and radio communications systems. In this paper, we present a new method to detect automatically radio bursts using data from the Nancay Decametre Array (NDA) in the band 10 MHz–80 MHz. This method starts with eliminating unwanted signals (Radio-Frequency Interference, RFI and Calibration signals) by analyzing the dynamic spectrum of the signal recorded in time. Then, a gradient median filter is applied to smooth and to reduce the variability of the signal. After denoising the signal, an automated solar radio burst detection system is applied. This system is based on a sequential procedure with adaptive constant-false-alarm rate (CFAR like detector) aimed to extract the spectra of major solar bursts. To this end, a semi-automatic software package is also developed to create a data base of all possible events (type II, III, IV or other) that could be detected and used for our performance assessment.
Near real‐time predictions of the arrival at Earth of flare‐related shocks during Solar Cycle 23
[1] The arrival times at Earth of 166 flare-related shocks identified exiting the Sun (using metric radio drift data) during the maximum phase of Solar Cycle 23, were forecast in near-real time using the Shock Time of Arrival Model (STOA), the Interplanetary Shock Propagation Model (ISPM) and the Hakamada-Akasofu-Fry Model (version 2, HAFv.2). These predictions are compared with the arrival at L1 of shocks recorded in plasma and magnetic data aboard the ACE spacecraft. The resulting correspondences are graded following standard statistical methods. Among other parameters, a representative reference metric defined by {(“hits” + “correct nulls”) × 100}/(total number of predictions) is used to describe the success rates of the predictions relative to the measurements. Resulting values for STOA, ISPM, and HAFv.2 were 50%, 57%, and 51%, respectively, for a hit window of ±24 hours. On increasing the statistical sample by 173 events recorded during the rise phase of the same cycle, corresponding success rates of 54%, 60%, and 52%, respectively, were obtained. A χ2 test shows these results to be statistically significant at better than the 0.05 level. The effect of decreasing/increasing the size of the hit window is explored and the practical utility of shock predictions considered. Circumstances under which the models perform well/poorly are investigated through the formation, and statistical analysis, of various event subsets, within which the constituent shocks display common characteristics. The results thereby obtained are discussed in detail in the context of the limitations that affect some aspects of the data utilized in the models.