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Martonchik, J. V.

Publications and source records attributed to Martonchik, J. V..

At least 19 records

Small-Scale Drop Size Variability: Impact on Estimation of Cloud Optical Properties

Most cloud radiation models and conventional data processing techniques assume that the mean number of drops of a given radius is proportional to volume. The analysis of microphysical data on liquid water drop sizes shows that, for sufficiently small volumes, this proportionality breaks down; the number of cloud drops of a given radius is instead proportional to the volume raised to a drop size-dependent nonunit power. The coefficient of proportionality, a generalized drop concentration, is a function of the drop size. For abundant small drops the power is unity as assumed in the conventional approach. However, for rarer large drops, it falls increasingly below unity. This empirical fact leads to drop clustering, with the larger drops exhibiting a greater degree of clustering. The generalized drop concentration shows the mean number of drops per cluster, while the power characterizes the occurrence frequency of clusters. With a fixed total number of drops in a cloud, a decrease in frequency of clusters is accompanied by a corresponding increase in the generalized concentration. This initiates a competing process missed in the conventional models: an increase in the number of drops per cluster enhances the impact of rarer large drops on cloud radiation while a decrease in the frequency suppresses it. Because of the nonlinear relationship between the number of clustered drops and the volume, these two opposite tendencies do not necessarily compensate each other. The data analysis suggests that clustered drops likely have a stronger radiative impact compared to their unclustered counterpart; ignoring it results in underestimation of the contribution from large drops to cloud horizontal optical path.

Knyazikhin, Y.

Radiative Closure Experiments at a Cloud-Free Desert Site, Nevada, as Part of MISR Algorithm Validation

Radiative closure experiments involving a comparison between surface-measured spectral irradiance and the surface irradiance calculated according to a radiative transfer code at a desert site in Nevada under clear skies, yield the result that agreement between the two requires presence of an absorbing aerosol component with an imaginary refractive index equal to 0.03 and a 50:50 mix by optical depth of small and large particles with log-normal size distributions.

MISR experiment field observations calibration dat

Multiangle Remote Sensing of Aerosols Over Ocean

Multiangle, multispectral remote sensing observations, such as those anticipated from the Earth Observing System (EOS) Multiangle Imaging SpectroRadiometer (MISR), can significantly improve our ability to constrain aerosol properties from space.

retrieve column optical depth calm ocean particle

Ground-based Validation of the EOS Multi-angle Imaging SpectroRadiometer (MISR) Aerosol Retrieval Algorithms and Science Data Products

A plan for the ground-based validation of MISR aerosol retrieval is outlined. Activities occur in two phases: (1) pre-launch, work is focused on technique development and MISR algorithm validation using conventional ground-base methods and a MISR simulator (AirMISR) operating from the ER-2 aircraft to simulate MISR on-orbit observations. (2) Post-launch, the validation program relies on ground campaigns, underflights wit the MISR simulator and the use of local measurements fo aerosol loading and properties and irradiance measurements derived from the AERONET and ISIS networks.

algorithm

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.

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.

Retrieval of Bidirectional Reflectance Factors and Directional-Hemispherical Reflectances Using Space-Based and Airborne Multi-Angle Observations

MISR is scheduled for launch in 1998 on the EOS AM1 platform. The algorithms needed to analyze data from this instrument are currently being tested, using both simulated data and data currently available from the airborne ASAS multi-angle instrument...Results of the retrieval algorithms are presented using simulated MISR data.

MISR EOS algorithms retrieval algorithms