Scattering and Polarization Properties of Water Clouds and Hazes in the Visible and Infrared

Deirmendjian, D.

doi:10.1364/ao.3.000187

Cited by 319 publications

(117 citation statements)

References 11 publications

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“…In satellite remote-sensing DSDs are often described using theoretical distributions that fit well with in situ observations, in addition to being mathematically convenient (Deirmendjian, 1964;Tampieri and Tomasi, 1976;Martin et al, 1994;Miles et al, 2000). A popular theoretical DSD is the gamma distribution proposed by Hansen and Travis (1974):…”

Section: Cloud Microphysical and Optical Propertiesmentioning

confidence: 99%

Comparisons of bispectral and polarimetric retrievals of marine boundary layer cloud microphysics: case studies using a LES–satellite retrieval simulator

et al. 2018

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Abstract. Many passive remote-sensing techniques have been developed to retrieve cloud microphysical properties from satellite-based sensors, with the most common approaches being the bispectral and polarimetric techniques. These two vastly different retrieval techniques have been implemented for a variety of polar-orbiting and geostationary satellite platforms, providing global climatological data sets. Prior instrument comparison studies have shown that there are systematic differences between the droplet size retrieval products (effective radius) of bispectral (e.g., MODIS, Moderate Resolution Imaging Spectroradiometer) and polarimetric (e.g., POLDER, Polarization and Directionality of Earth's Reflectances) instruments. However, intercomparisons of airborne bispectral and polarimetric instruments have yielded results that do not appear to be systematically biased relative to one another. Diagnosing this discrepancy is complicated, because it is often difficult for instrument intercomparison studies to isolate differences between retrieval technique sensitivities and specific instrumental differences such as calibration and atmospheric correction. In addition to these technical differences the polarimetric retrieval is also sensitive to the dispersion of the droplet size distribution (effective variance), which could influence the interpretation of the droplet size retrieval. To avoid these instrument-dependent complications, this study makes use of a cloud remote-sensing retrieval simulator. Created by coupling a large-eddy simulation (LES) cloud model with a 1-D radiative transfer model, the simulator serves as a test bed for understanding differences between bispectral and polarimetric retrievals. With the help of this simulator we can not only compare the two techniques to one another (retrieval intercomparison) but also validate retrievals directly against the LES cloud properties. Using the satellite retrieval simulator, we are able to verify that at high spatial resolution (50 m) the bispectral and polarimetric retrievals are highly correlated with one another within expected observational uncertainties. The relatively small systematic biases at high spatial resolution can be attributed to different sensitivity limitations of the two retrievals. In contrast, a systematic difference between the two retrievals emerges at coarser resolution. This bias largely stems from differences related to sensitivity of the two retrievals to unresolved inhomogeneities in effective variance and optical thickness. The influence of coarse angular resolution is found to increase uncertainty in the polarimetric retrieval but generally maintains a constant mean value.

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Section: Cloud Microphysical and Optical Propertiesmentioning

confidence: 99%

Comparisons of bispectral and polarimetric retrievals of marine boundary layer cloud microphysics: case studies using a LES–satellite retrieval simulator

et al. 2018

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“…Here must be considered the work of Waldram (1945), Reeger and Siendentopf (1946), Bullrich andMoeller (1947 and, Foitzik and Zschaeck (1952), Deirmendjian (1964), Fraser (1960), Barteneva (1960) and otheis who made important contributions to this area of research. The criteria finally conside-.ed for the choice of the phase function were (a) its derivation should be from field measurements with a meteorological range of at least 30 kin, and (b) the phase function should permit Angstroms.…”

mentioning

confidence: 99%

An Atlas of Aerosol Attenuation and Extinction Profiles for the Troposphere and Stratosphere

Elterman¹

1966

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Light scattering measurements were carried out to determine the aerosol properties of the atmosphere. First the expression for the aerosol attenuation coefficient is derived, based on the field geometry in conjunction with Rayleigh and aerosol scattering considerations. Then the results derived from the measurements are discussed. The paper concludes with an atlas of 105 profiles for altitudes to about 35 km (the data does not exclude the presence of aerosols with low number density between 35 to 70 kin). These profiles consist of aerosol attenuation and extinction coefficients as a function of altitude. Since the coefficients are proportional to aerosol number density, the profiles yield information concerning aerosol stratification. A plot for the computed mean of the 105 vertical profiles is included.

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“…In satellite remote sensing DSDs are often described using theoretical distributions that fit well with in situ observations, in addition to being mathematically convenient (Deirmendjian, 1964;Martin et al, 1994;Miles et al, 2000;Tampieri and Tomasi, 1976). A popular theoretical DSD is the gamma distribution proposed by Hansen and Travis (1974):…”

Section: Cloud Microphysical and Optical Propertiesmentioning

confidence: 99%

Comparisons of bispectral and polarimetric cloud microphysical retrievals using LES-Satellite retrieval simulator

Miller

Zhang

Platnick

et al. 2017

Preprint

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Abstract. Many passive remote sensing techniques have been developed to retrieve cloud microphysical properties from satellite-based sensors, with the most common approaches being the bispectral and polarimetric techniques. These two vastly different retrieval techniques have been implemented for a variety of polar-orbiting and geostationary satellite platforms, providing global climatological datasets. Prior instrument comparison studies have shown that there are systematic 15 differences between the droplet size retrieval products (effective radius) of bispectral (e.g. MODIS, Moderate Resolution Imaging Spectroradiometer) and polarimetric (e.g. POLDER, Polarization and Directionality of Earth's Reflectances) instruments. However, intercomparisons of airborne bispectral and polarimetric instruments have yielded results that do not appear to be systematically biased relative to one another. Diagnosing this discrepancy is complicated, because it is often difficult for instrument intercomparison studies to isolate differences between retrieval technique sensitivities and specific 20 instrumental differences such as calibration, atmospheric correction, etc. In addition to these technical differences the polarimetric retrieval is also sensitive to the dispersion of the droplet size distribution (effective variance), which could influence the interpretation of the droplet size retrieval. To avoid these instrument-dependent complications, this study makes use of a cloud remote sensing retrieval simulator. Created by coupling a large eddy simulation (LES) cloud model with radiative transfer models, the simulator serves as a test bed for understanding differences between bispectral and 25 polarimetric retrievals. With the help of this simulator we can not only compare the two techniques to one another (retrieval intercomparison), but also validate retrievals directly against the LES cloud properties. Using the satellite retrieval simulator we are able to verify that at high spatial resolution (50 m) the bispectral and polarimetric retrievals are indeed highly correlated with one another. The small differences at high spatial resolution can be attributed to different sensitivity limitations of the two retrievals. In contrast, a systematic difference between the two retrievals emerges at coarser resolution. 30This bias largely stems from differences related to sensitivity of the two retrievals to unresolved inhomogeneities in effective variance and optical thickness. The influence of coarse angular resolution is found to increase uncertainty in the polarimetric retrieval, but generally maintains a constant mean value.Atmos. Meas. Tech. Discuss., https://doi

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Scattering and Polarization Properties of Water Clouds and Hazes in the Visible and Infrared

Cited by 319 publications

References 11 publications

Comparisons of bispectral and polarimetric retrievals of marine boundary layer cloud microphysics: case studies using a LES–satellite retrieval simulator

Comparisons of bispectral and polarimetric retrievals of marine boundary layer cloud microphysics: case studies using a LES–satellite retrieval simulator

An Atlas of Aerosol Attenuation and Extinction Profiles for the Troposphere and Stratosphere

Comparisons of bispectral and polarimetric cloud microphysical retrievals using LES-Satellite retrieval simulator

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