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An asteroid impact is a low probability event with potentially devastating consequences. The Asteroid Risk Mitigation Optimization and Research (ARMOR) software tool calculates whether a colliding asteroid experiences an airburst or surface impact and calculates effect severity as well as reach on the global map. To calculate the consequences of an impact in terms of loss of human life, new vulnerability models are derived that connect the severity of seven impact effects (strong winds, overpressure shockwave, thermal radiation, seismic shaking, ejecta deposition, cratering and tsunamis) with lethality to human populations. With the new vulnerability models ARMOR estimates casualties of an impact under consideration of the local population and geography. The presented algorithms and models are employed in two case studies to estimate total casualties as well as the damage contribution of each impact effect. The case studies highlight that aerothermal effects are most harmful except for deep water impacts, where tsunamis are the dominant hazard. Continental shelves serve a protective function against the tsunami hazard caused by impactors on the shelf. Furthermore, the calculation of impact consequences facilitates asteroid risk estimation to better characterize a given threat and the concept of risk as well as its applicability to the asteroid impact scenario are presented.
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A set of 50,000 artificial Earth impacting asteroids was used to obtain, for the first time, information about the dominance of individual impact effects such as wind blast, overpressure shock, thermal radiation, cratering, seismic shaking, ejecta deposition and tsunami for the loss of human life during an impact event for impactor sizes between 15 to 400 m and how the dominance of impact effects changes over size. Information about the dominance of each impact effect can enable disaster managers to plan for the most relevant effects in the event of an asteroid impact. Furthermore, the analysis of average casualty numbers per impactor shows that there is a significant difference in expected loss for airburst and surface impacts and that the average impact over land is an order of magnitude more dangerous than one over water.
We assess the risk of an Earth impact for asteroid (99942) Apophis by means of a statistical analysis accounting for the uncertainty of both the orbital solution and the Yarkovsky effect. We select those observations with either rigorous uncertainty information provided by the observer or a high established accuracy. For the Yarkovsky effect we perform a Monte Carlo simulation that fully accounts for the uncertainty in the physical characterization, especially for the unknown spin orientation. By mapping the uncertainty information onto the 2029 b-plane and identifying the keyholes corresponding to subsequent impacts we assess the impact risk for future encounters. In particular, we find an impact probability greater than 10^-6 for an impact in 2068. We analyze the stability of the impact probability with respect to the assumptions on Apophis physical characterization and consider the possible effect of the early 2013 radar apparition.
ESA and NASA maintain asteroid hazard lists that contain all known asteroids with a non zero chance of colliding with the Earth in the future. Some software tools exist that are, either, capable of calculating the impact points of those asteroids, or that can estimate the impact effects of a given impact incident. However, no single tool is available that combines both aspects and enables a comprehensive risk analysis. The question is, thus, whether tools that can calculate impact location may be used to obtain a qualitative understanding of the asteroid impact risk distribution. To answer this question, two impact risk distributions that control for impact effect modelling were generated and compared. The Asteroid Risk Mitigation Optimization and Research (ARMOR) tool, in conjunction with the freely available software OrbFit, was used to project the impact probabilities of listed asteroids with a minimum diameter of 30 m onto the surface of the Earth representing a random sample (15% of all objects) of the hazard list. The resulting 261 impact corridors were visualized on a global map. Furthermore, the impact corridors were combined with Earth population data to estimate the simplified risk (without impact effects) and advanced risk (with impact effects) associated with the direct asteroid impacts that each nation faces from present to 2100 based on this sample. The relationship between risk and population size was examined for the 40 most populous countries and it was apparent that population size is a good proxy for relative risk. The advanced and simplified risk distributions were compared and the alteration of the results based on the introduction of physical impact effects was discussed. Population remained a valid proxy for relative impact risk, but the inclusion of impact effects resulted in significantly different risks, especially when considered at the national level.
Orbit-determination programs find the orbit solution that best fits a set of observations by minimizing the RMS of the residuals of the fit. For near-Earth asteroids, the uncertainty of the orbit solution may be compatible with trajectories that impact Earth. This paper shows how incorporating the impact condition as an observation in the orbit-determination process results in a robust technique for finding the regions in parameter space leading to impacts. The impact pseudo-observation residuals are the b-plane coordinates at the time of close approach and the uncertainty is set to a fraction of the Earth radius. The extended orbit-determination filter converges naturally to an impacting solution if allowed by the observations. The uncertainty of the resulting orbit provides an excellent geometric representation of the virtual impactor. As a result, the impact probability can be efficiently estimated by exploring this region in parameter space using importance sampling. The proposed technique can systematically handle a large number of estimated parameters, account for nongravitational forces, deal with nonlinearities, and correct for non-Gaussian initial uncertainty distributions. The algorithm has been implemented into a new impact monitoring system at JPL called Sentry-II, which is undergoing extensive testing. The main advantages of Sentry-II over JPLs currently operating impact monitoring system Sentry are that Sentry-II can systematically process orbits perturbed by nongravitational forces and that it is generally more robust when dealing with pathological cases. The runtimes and completeness of both systems are comparable, with the impact probability of Sentry-II for 99% completeness being $3times10^{-7}$.
According to different typologies of activity and priority, risks can assume diverse meanings and it can be assessed in different ways. In general risk is measured in terms of a probability combination of an event (frequency) and its consequence (impact). To estimate the frequency and the impact (severity) historical data or expert opinions (either qualitative or quantitative data) are used. Moreover qualitative data must be converted in numerical values to be used in the model. In the case of enterprise risk assessment the considered risks are, for instance, strategic, operational, legal and of image, which many times are difficult to be quantified. So in most cases only expert data, gathered by scorecard approaches, are available for risk analysis. The Bayesian Network is a useful tool to integrate different information and in particular to study the risks joint distribution by using data collected from experts. In this paper we want to show a possible approach for building a Bayesian networks in the particular case in which only prior probabilities of node states and marginal correlations between nodes are available, and when the variables have only two states.
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