Altitude parameter

Thirdly, the altitude parameter, x, is calculated using equation (4.11),... 

Table 4.2 The ratio of altitude muzzle velocity squared for the altitude parameter, X.

The blast scaling law which is almost universally used to predict characteristics of blast waves from explosions at high altitude is that of Sachs (Reference 10). Sachs law states that dimensionless overpressure and dimensionless impulse can be expressed as unique functions of a dimensionless scaled distance, where the dimensionless parameters include quantities which define the ambient atmospheric conditions prior to the explosion. 

Equations (4.8)-(4.15) show how the altitude attained and time of flight can be estimated from basic parameters and Newtonian Laws of Motion. 

Specifically, the calculations had as their goal the computation of (dZi/dz) as a function of particle size for clouds of different heights at various altitudes and sampling times, including the parameters applicable to the samples analyzed. A detailed exposition of the theory and its limitations is presented in the Appendix. The values of (dzjbz) are divided point by point into the measured size distribution—i.e., f(a,z,t)— to arrive at the size distribution at stabilization time—i.e.y f[a,z(a,z,t),0], according to Equation 2. An additional output of the calculations are the cutoff diameters (smallest and largest diameters) in the samples. 

Figure 8 shows values of (dZi/dz )a,t plotted vs. particle size for times ranging from one to 10 hours and an observation altitude of 15,000 meters. The calculations were done with the atmospheric parameters mentioned previously, and with a particle density Pp = 2.6 g./cm.3. Superimposed on the graph are lines corresponding to various initial cloud tops. The intersection of these lines with the dzi/dz lines gives the cutoff size values. As expected, the correction factor increases with time and particle size. For an initial cloud top at 35,000 meters, observations at t = 3 hours give a maximum particle size of 138/a, with the correction factor varying from zero for small sizes to 1.02, 1.15, and 1.62 at 50, 100, and 138/a, respectively. If the observation time is increased to five hours, the maximum particle size decreases to 113/a, and the correction factor values increase to 1.03,1.33, and 1.78 at 50, 100, and 113/a. 

A large number of observations, both remote and in situ, confirm this qualitative picture of the loss of ozone over Antarctica. The in situ data have come from instruments carried on small balloons and the NASA ER-2 high-altitude aircraft. Small-balloon measurements are of particle distributions and sizes, ozone, and water vapor (23, 33). ER-2 measurements, listed in Table I, are of particle size and composition atmospheric parameters such as temperature, pressure, lapse rate, and winds and trace gas abundances of 03, N20, NOy or NO, CIO and BrO, and stable gases, including CH4, chlorofluorocarbons, halons, and others (34-45). 

The pressures at the tangent altitudes (representing the observation geometries) and the temperature profile (p,T), as well as die volume mixing ratio (VMR) profiles of five high priority species (O3, H20, HNO3, CH4 and N20), will be routinely retrieved in near real time (NRT). The retrieval of these parameters from calibrated spectra (Level lb data) is indicated as NRT Level 2 processor. 

For the retrieval of each single vertical profile, the global fit approach [5] is used, i.e. the whole altitude profile is retrieved analysing simultaneously all the limb-views of a scan. This approach provides a more comprehensive exploitation of the information and a rigorous determination of the correlations between atmospheric parameters at the different altitudes. Besides, global fit approach avoids repeating calculation of quantities which are common to all the limb-views of the scan. 

The main goal of this research is to construct a global, vertically-resolved data set of several stratospheric aerosol parameters retrieved by the LUT algorithm. This section presents examples of LUT retrievals of Rtff, S, V and [Pg.356]

Fossil floras provide one of the most useful sources for obtaining data that can be used to estimate paleoelevation. The distribution of modern forests is clearly delineated largely in accordance with climate, which varies with both altitude and latitude. The correlation of modern vegetation with mean temperature parameters provides the basis for comparing Cenozoic fossil floras with the thermal distribution of these modern forest types to infer paleotemperatures. Such information is a valuable source for inferring climate fluctuations through time, and in combination with thermodynamic properties of the atmosphere, it also can be used to estimate paleoelevation. 

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