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Kahn, R. A.

Publications and source records attributed to Kahn, R. A..

29 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

A Modified Linear-Mixing Method for Calculating Atmospheric Path Radiances of Aerosol Mixtures

The top-of-atmosphere (TOA) path radiance generated by an aerosol mixture can be synthesized by linearly adding the contributions of the individual aerosol components, weighted by their fractional optical depths. The method, known as linear mixing, is exact in the single-scattering limit. When multiple scattering is significant, the method reproduces the atmospheric path radiance of the mixture with less than 3% errors for weakly absorbing aerosols up to optical thickness of 0.5. However, when strongly absorbing aerosols are included in the mixture, the errors are much larger. This is due to neglecting the effect of multiple interactions between the aerosol components, especially when the values of the single-scattering albedos of these components are so different that the parameter epsilon = (Sigma)f(sub i) absolute value of bar omega(sub i) - bar omega(sub mix)/bar omega(sub i), is larger than approx. 0.1, where bar omega(sub i) and f(sub i) are the single-scattering albedo and the fractional abundance of the i th component, and bar omega(sub i) is the effective single-scattering albedo of the mixture. We describe an empirical, modified linear-mixing method which effectively accounts for the multiple interactions between aerosol components. The modified and standard methods are identical when epsilon = 0.0 and give similar results when epsilon less than or equal to 0.05. For optical depths larger than approx. 0.5, or when epsilon greater than 0.05, only the modified method can reproduce the radiances within 5% error for common aerosol types up to optical thickness of 2.0. Because this method facilitates efficient and accurate atmospheric path radiance calculations for mixtures of a wide variety of aerosol types, it will be used as part of the aerosol retrieval methodology for the Earth Observing System (EOS) multiangle imaging spectroradiometer (MISR), scheduled for launch into polar orbit in 1998.

Abdou, W. A.↗
NASA NTRS1997

A Modified Linear-Mixing Method for Calculating Atmospheric Path Radiances of Aerosol Mixtures

The top-of-atmosphere (TOA) path radiance generated by an aerosol mixture can be synthesized by linearly adding the contributions of the individual aerosol components, weighted by their fractional optical depths. The method, known as linear mixing, is exact in the single-scattering limit. When multiple scattering is significant, the method reproduces the atmospheric path radiance of the mixture with less than 3% errors for weakly absorbing aerosols up to optical thickness of 0.5. However, when strongly absorbing aerosols are included in the mixture, the errors are much larger. This is due to neglecting the effect of multiple interactions between the aerosol components, especially when the values of the single-scattering albedos of these components are so different that the parameter e = the sum of f(sub i)[(bar)omega(sub i) - (bar)omega(sub mix)]/(bar)omega(sub i) is larger than approximately 0.1, where (bar)omega(sub i)and f(sub i) are the single-scattering albedo and the fractional abundance of the ith component, and (bar)omega(sub mix) is the effective single-scattering albedo of the Mixture. We describe an empirical, modified linear-mixing method which effectively accounts for the multiple interactions between aerosol components. The modified and standard methods are identical when epsilon = 0.0 and give similar results when epsilon is less than or equal to 0.05. For optical depths larger than approximately 0.5, or when epsilon is greater than 0.05, only the modified method can reproduce the radiances within 5% error for common aerosol types up to optical thickness of 2.0. Because this method facilitates efficient and accurate atmospheric path radiance calculations for mixtures of a wide variety of aerosol types, it will be used as part of the aerosol retrieval methodology for the Earth Observing System (EOS) multiangle imaging spectroradiometer (MISR), scheduled for launch into polar orbit in 1998.

Abdou, W. A.↗
NASA NTRS1992

The Martian dust cycle

Martian dust plays a key role in determining the current climate of Mars, and is suspected of having had a major influence on the evolution of the surface and the history of climatic conditions on the planet. From spacecraft and ground-based observations, some understanding has been acquired of the styles and frequency of dust motion in the atmosphere and at the surface, of atmospheric dust loading, dust particle properties, and their variability. Limited constraints have also been derived for the volumes and ages of some potential dust reservoirs on the surface. These data are adequate to frame questions about the seasonal and long-term cycles of dust of Mars. Additional data are needed to place quantitative constraints on components of these cycles, on their possible linkage to one another, and to draw conclusions from the dust budget about the climate history of Mars.

Kahn, R. A.↗
NASA NTRS1985

Participation in the Mars Data Analysis Program: Analysis of cloud forms in Viking and Mariner 9 images

The first systematic account of the climate of Mars, based upon observations was produced. Cloud data were used to determine spatially and temporally varying near-surface wind direction, relative wind speed, static stability, and humidity conditions on a global scale. Existing models of meteorological processes were critically reexamined in light of the data, and more stringent constraints were set on global processes. Several discoveries were made, including the large extent and seasonal variability of the Mars equatorial Hadley cell, the failure of high latitude winds to reverse direction in early northern spring, the change in meridional wind component in southern midautum, and the almost constant cloud cover in the northern hemisphere, during spring and summer primarily by condensate clouds and in fall and winter by condensates and dust. The implications of these observations are discussed.

Gierasch, P.↗