Most large (over a kilometre in diameter) near-Earth asteroids are now known, but recognition that airbursts (or fireballs resulting from nuclear-weapon-sized detonations of meteoroids in the atmosphere) have the potential to do greater damage 1 than previously thought has shifted an increasing portion of the residual impact risk (the risk of impact from an unknown object) to smaller objects 2 . Above the threshold size of impactor at which the atmosphere absorbs sufficient energy to prevent a ground impact, most of the damage is thought to be caused by the airburst shock wave 3 , but owing to lack of observations this is uncertain 4,5 . Here we report an analysis of the damage from the airburst of an asteroid about 19 metres (17 to 20 metres) in diameter southeast of Chelyabinsk, Russia, on 15 February 2013, estimated to have an energy equivalent of approximately 500 (6100) kilotons of trinitrotoluene (TNT, where 1 kiloton of TNT 54.185310 12 joules). We show that a widely referenced technique 4-6 of estimating airburst damage does not reproduce the observations, and that the mathematical relations 7 based on the effects of nuclear weapons-almost always used with this technique-overestimate blast damage. This suggests that earlier damage estimates 5,6 near the threshold impactor size are too high. We performed a global survey of airbursts of a kiloton or more (including Chelyabinsk), and find that the number of impactors with diameters of tens of metres may be an order of magnitude higher than estimates based on other techniques 8,9 . This suggests a non-equilibrium (if the population were in a long-term collisional steady state the size-frequency distribution would either follow a single power law or there must be a size-dependent bias in other surveys) in the near-Earth asteroid population for objects 10 to 50 metres in diameter, and shifts more of the residual impact risk to these sizes. for the Chelyabinsk airburst, based on indirect illumination measured from video records. The brightness is an average derived from indirect scattered sky brightness from six videos proximal to the airburst, corrected for the sensor gamma setting, autogain, range and airmass extinction, following the procedure used for other airburst light curves generated from video 24,25 . The light curve has been normalized using the US government sensor data peak brightness value of 2.7 3 10 13 W sr 21, corresponding to an absolute astronomical magnitude of 228 in the silicon bandpass. The individual video light curves deviate by less than one magnitude between times 22 and 11.5 with larger deviations outside this interval. Time zero corresponds to 03:20:32.2 UTC on 15 February 2013. b, The energy deposition per unit height for the Chelyabinsk airburst, based on video data. The conversion to absolute energy deposition per unit path length assumes a blackbody emission of 6,000 K and bolometric efficiency of 17%, the same as the assumptions used to convert earlier US government sensor information to energy 26 . The heights are computed us...

[1] The ability of the International Monitoring System (IMS) infrasound network to detect atmospheric nuclear explosions and other signals of interest is strongly dependent on stationspecific ambient noise. This ambient noise includes both incoherent wind noise and real coherent infrasonic waves. Previous ambient infrasound noise models have not distinguished between incoherent and coherent components. We present a first attempt at statistically and systematically characterizing coherent infrasound recorded by the IMS. We perform broadband (0.01-5 Hz) array processing with the IMS continuous waveform archive (39 stations from 1 April 2005 to 31 December 2010) using an implementation of the Progressive Multi-Channel Correlation algorithm in logfrequency space. From these results, we estimate multi-year 5th, 50th, and 95th percentiles of the RMS pressure of coherent signals in 15 frequency bands for each station. We compare the resulting coherent infrasound models with raw power spectral density noise models, which inherently include both incoherent and coherent components. Our results indicate that IMS arrays consistently record coherent ambient infrasound across the broad frequency range from 0.01 to 5 Hz when wind noise levels permit. The multi-year averaging emphasizes continuous signals such as oceanic microbaroms, as well as persistent transient signals such as repetitive volcanic, surf, thunder, or anthropogenic activity. Systematic characterization of coherent infrasound detection is important for quantifying a station's recording environment, signal-to-noise ratio as a function of frequency and direction, and overall performance, which all influence the detection probability of specific signals of interest.

[1] A global-scale analysis of detections made at all 36 currently operating International Monitoring System (IMS) infrasound arrays confirms that the primary factor controlling signal detectability is the seasonal variability of the stratospheric zonal wind. At most arrays, $80% of the detections in the 0.2-to 2-Hz bandpass are associated with propagation downwind of the dominant stratospheric wind direction. Previous IMS infrasound network performance models neglect the time-and site-dependent effects of both stratospheric meteorological variability and ambient noise models. In this study both effects are incorporated; we compare empirical and improved specifications of the stratospheric wind and include station-dependent wind noise models. Using a deterministic approach, the influence of individual model parameters on the network performance is systematically assessed. At frequencies of interest for detecting atmospheric explosions (0.2-2 Hz), the simulations predict that explosions equivalent to $500 t of TNT would be detected by at least two stations at any time of the year. The detection capability is best around January and July when stratospheric winds are strongest, compared to the equinox periods when zonal winds reduce and reverse. The model predicts that temporal fluctuations in the ground-to-stratosphere meteorological variables generate detection threshold variations on daily and seasonal timescales of $50 and $500 t, respectively. While the strong zonal winds lead to an improvement in detection capability, their highly directional nature leads to an increase in the location uncertainty owing to the decreased azimuthal separation of the detecting stations.