tions in calculated casualties are therefore smaller for urban than rural targets, though the absolute numbers of predicted deaths can differ by large amounts (hundreds of thousands) for urban areas depending on factors that are not readily controlled (e.g., degree of sheltering).

For urban and rural targets, respectively, the use of a nuclear EPW is calculated to reduce casualties by a factor of ~2 to 7 and 10 to 60 relative to an aboveground nuclear burst with a yield increased by a factor of 25. This calculated 101 to 102 reduction in fatalities and serious injuries is comparable to the effect of the aggregate uncertainties that the committee has derived for the modeling tools.

NOTES

1.  

The use of exponential notation (100 = 1, 101 = 10, 102 = 100, 103 = 1,000, and so on) implies that the values indicate only orders of magnitude (powers of ten) and are typically uncertain by factors of at least 2 to 5.

2.  

Details are provided in the following classified reports: W86 Warhead Status Report (U), SAND 83-1642, RS 3151/83/ 033, Sandia National Laboratories, Albuquerque, N.Mex. (11/1/1983); Strategic Earth Penetrator Joint DOD/DOE Phase 2 Study (U), Robert Blankert, Air Force Material Command, RS 2907/01/00268, NWIC-TR-94-2 (9/1/1998); and W61 Weapon Development Report (U), SAND 91-2243, RS 3151/91/00024, Sandia National Laboratories, Albuquerque, N.Mex. (3/1/1992).

3.  

Ted F. Harvey, Lawrence Livermore National Laboratory, March 23, 2004, personal communication.

4.  

K.A. Hart, W.J. Steenburgh, D.J. Onton, and A.J. Siffert. 2004. “An Evaluation of Mesoscale-Model-Based Model Output Statistics (MOS) During the 2002 Olympic and Paralympic Winter Games,” Weather and Forecasting, Vol. 19, pp. 200-218.

5.  

National Research Council, 2003a, To Live on an Active Earth: Perspectives on Earthquake Science, National Academies Press, Washington, D.C.; National Research Council, 2003b, Tracking and Predicting the Atmospheric Dispersion of Hazardous Material Releases, National Academies Press, Washington, D.C.

6.  

Fred A. Mettler, Jr., New Mexico Federal Regional Medical Center, March 23, 2004, personal communication.

7.  

To avoid ambiguities in comparing directions greater or less than 360 degrees, the errors are typically reported out of a total possible range of 180 degrees and are therefore listed here as ± values out of 360 degrees.

8.  

E.P. Grimit and C.F. Mass, 2002, “Initial Results of a Mesoscale Short-Range Ensemble Forecasting System over the Pacific Northwest,” Weather and Forecasting, Vol. 17, pp. 192-205; C.F. Mass, D. Ovens, K. Westrick, and B.A. Cole, 2002, “Does Increasing Horizontal Resolution Produce More Skillful Forecasts? The Results of Two Years of Real-Time Numerical Weather Prediction over the Pacific Northwest,” Bull. Am. Meteorol. Soc., Vol. 83, pp. 407-430.

9.  

R.L. Buckley, A.H. Weber, and J.H. Weber. 2004. “Statistical Comparison of Regional Atmospheric Modelling System Forecasts with Observations,” Meteorol. Appl., Vol. 11, pp. 67-82.

10.  

Given the level of interest associated with the Olympic Games, from media, commercial enterprises, and participants, among others, Hart et al.’s 2004 study could be considered to describe a best-case scenario for current modeling capabilities applied to a complex terrain.

11.  

K.A. Hart, W.J. Steenburgh, D.J. Onton, and A.J. Siffert. 2004. “An Evaluation of Mesoscale-Model-Based Model Output Statistics (MOS) During the 2002 Olympic and Paralympic Winter Games,” Weather and Forecasting, Vol. 19, pp. 200-218.

12.  

A. Russell and R. Dennis, 2000, “NARSTO Critical Review of Photochemical Models and Modeling,” Atmos. Environ., Vol. 34, pp. 2283-2324; S. Zhong and J. Fast, 2003, “An Evaluation of the MM5, RAMS, and Meso-Eta Models at Sub-Kilometer Resolution Using VTMX Field Campaign Data in the Salt Lake Valley,” Monthly Weather Review, Vol. 131, pp. 1301-1322; D.P. Bacon, N.N. Ahmad, Z. Boybei, T.J. Dunn, M.S. Hall, P.C.S. Lee, R.A. Sarma, M.D. Turner, K.T. Waight III, S.H. Young, and J.W. Zack, 2000, “A Dynamically Adapting Weather and Dispersion Model: The Operational Multiscale Environment Model with Grid Adaptivity (OMEGA),” Monthly Weather Review, Vol. 128, pp. 2044-2076.

13.  

Z. Boybei, N.N. Ahmad, D.P. Bacon, T.J. Dunn, M.S. Hall, P.C.S. Lee, R.A. Sarma, and T.R. Wait. 2001. “Evaluation of the Operational Multiscale Environment Model with Grid Adaptivity Against the European Tracer Experiment,” J. Appl. Meteorol., Vol. 40, pp. 1541-1558.

14.  

A. Straume. 2004. “A More Extensive Investigation of the Use of Ensemble Forecasts for Dispersion Model Evaluation,” J. Appl. Meteorol., Vol. 40, pp. 425-445.

15.  

M.H. Freilich and R.H. Dunbar, 1999, “The Accuracy of the NSCAT 1 Vector Winds: Comparisons with National Data Buoy Center Buoys,” J. Geophys. Res., Vol. C5, pp. 11231-11246; D. Orell, L. Smith, J. Barkmeijer, and T.N. Palmer, 2001, “Model Error in Weather Forecasting,” Nonlinear Processes in Geophysics, Vol. 8, pp. 357-371.

16.  

In contrast, casualties from earthquakes are often observed to be greatly magnified due to the effects of building collapses when a large fraction of a population is indoors—for example at night.



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