Retrieval of Cloud Cover Parameters from Multispectral Satellite Images

Arking, Albert; Childs, Jeffrey D.

doi:10.1175/1520-0450(1985)024<0322:roccpf>2.0.co;2

Cited by 205 publications

(89 citation statements)

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“…Rozenberg et al (1974) were the first to report satellite results with near infrared channels aboard the U.S.S.R. Cosmos satellite Landsat is placed into an orbit which only allows repeat coverage every 16 days. Arking and Childs (1985) used the AVHRR for remote sensing cloud drop radii and optical depths though no in situ cloud measurements were used for validation. Grainger (1990) used the AVHRR for studying orographic effects on drop sizes.…”

Section: Jl-t11/mentioning

confidence: 99%

Remote sensing the susceptibility of cloud albedo to changes in drop concentration

Platnick

Twomey²

1994

Atmospheric Research

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Section: Jl-t11/mentioning

confidence: 99%

Remote sensing the susceptibility of cloud albedo to changes in drop concentration

Platnick

Twomey²

1994

Atmospheric Research

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“…The main objective in originally flying the channel was reported to be for cloud screening of sea surface temperature observations (Schwalb 1978). Measurements in this window are now routinely used for cloud detection and screening (Saunders and Kriebel 1988;Ackerman et al 1998;Heidinger et al 2002), fire detection (Kaufman et al 1990;Prins and Menzel 1992;Justice et al 2002), cloud phase and surface snow/ice discrimination (Pavolonis et al 2005), and quantitative cloud microphysical retrievals (Arking and Childs 1985;Platnick and Twomey 1994;Han et al 1994;Minnis et al 1995;Platnick et al 2003). The usefulness of the band for cloud observations essentially derives from the significant dependence of single scattering albedo on cloud thermodynamic phase and particle size (absorption increases for the ice phase and with particle size) and differences in single scattering albedo (and thereby cloud emissivity) when compared with window IR channels.…”

Section: Introductionmentioning

confidence: 99%

Model Calculations of Solar Spectral Irradiance in the 3.7-μm Band for Earth Remote Sensing Applications

Platnick

Fontenla

2008

Journal of Applied Meteorology and Climatology

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Since the launch of the first Advanced Very High Resolution Radiometer (AVHRR) instrument aboard the Television and Infrared Observational Satellite (TIROS-N), measurements in the 3.7-m atmospheric window have been exploited for use in cloud detection and screening, cloud thermodynamic phase and surface snow/ice discrimination, and quantitative cloud particle size retrievals. The utility of the band has led to the incorporation of similar channels on a number of existing satellite imagers and future operational imagers. Daytime observations in the band include both reflected solar and thermal emission energy. Since 3.7-m channels are calibrated to a radiance scale (via onboard blackbodies), knowledge of the top-ofatmosphere solar irradiance in the spectral region is required to infer reflectance. Despite the ubiquity of 3.7-m channels, absolute solar spectral irradiance data come from either a single measurement campaign (Thekaekara et al.) or synthetic spectra. In the current study, the historical 3.7-m band spectral irradiance datasets are compared with the recent semiempirical solar model of the quiet sun by Fontenla et al. The model has expected uncertainties of about 2% in the 3.7-m spectral region. The channel-averaged spectral irradiances using the observations reported by Thekaekara et al. are found to be 3.2%-4.1% greater than those derived from the Fontenla et al. model for Moderate Resolution Imaging Spectroradiometer (MO-DIS) and AVHRR instrument bandpasses; the Kurucz spectrum, as included in the Moderate Spectral Resolution Atmospheric Transmittance (MODTRAN4) distribution, gives channel-averaged irradiances 1.2%-1.5% smaller than the Fontenla model. For the MODIS instrument, these solar irradiance uncertainties result in cloud microphysical retrieval uncertainties that are comparable to other fundamental reflectance error sources.

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“…For instance, it was shown [Feigelson, 1981] that clouds having t < 5 are characterized by a geometrical thickness less than 200 m. It is difficult to expect that such thin clouds are horizontally homogeneous media. This requires taking the horizontal photon transport into account [Cahalan et al, 1994;Platnick, 2001], which is not considered in standard retrieval procedures [Arking and Childs, 1985;Nakajima and King, 1990]. Due to this inhomogeneity, satellite cloud retrievals for optically thin clouds are troublesome even if the lookup table approach is used.…”

Section: Introductionmentioning

confidence: 99%

A semianalytical cloud retrieval algorithm using backscattered radiation in 0.4–2.4 μm spectral region

et al. 2003

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[1] This study is devoted to the development of a semianalytical algorithm for the determination of the optical thickness, the liquid water path, and the effective size of droplets from spectral measurements of the intensity of light reflected from water clouds with large optical thickness. The probability of photon absorption by droplets in the visible and near-infrared spectral regions is low. This allows us to simplify and modify wellknown asymptotic equations of the radiative transfer theory, taking into account the fact that the single scattering albedo is close to one. Modified asymptotic equations are used to develop the inverse algorithm. We also avoid the use of the Mie theory, applying parametrizations and geometrical optics results with account for wave corrections. The main advantage of the method proposed lays in the fact that the equations derived not only provide a valuable alternative to the numerical radiative transfer solution but also are much more simple than equations of a conventional asymptotic theory. This simplicity allows both the simplification of the cloud retrieval algorithm and, even more important, the insight into various factors involved in a retrieval scheme.

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Retrieval of Cloud Cover Parameters from Multispectral Satellite Images

Cited by 205 publications

References 0 publications

Remote sensing the susceptibility of cloud albedo to changes in drop concentration

Remote sensing the susceptibility of cloud albedo to changes in drop concentration

Model Calculations of Solar Spectral Irradiance in the 3.7-μm Band for Earth Remote Sensing Applications

A semianalytical cloud retrieval algorithm using backscattered radiation in 0.4–2.4 μm spectral region

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