The Retrieval of Effective Particle Radius and Liquid Water Path of Low-Level Marine Clouds from NOAA AVHRR Data

Kuji, Makoto; Hayasaka, Tadahiro; Kikuchi, Nobuyuki; Nakajima, Teruyuki; Tanaka, Masayuki

doi:10.1175/1520-0450(2000)039<0999:troepr>2.0.co;2

Cited by 11 publications

(7 citation statements)

References 31 publications

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“…Nakajima et al report reasonable agreement between observed and retrieved estimates of visible optical depths, but the droplet effective radii retrieved using reflectances at 2.1-µm were 1-2 µm larger than the in situ measurements. Retrievals using AVHRR radiances, like those used here, produced somewhat better agreement Nakajima, 1995 andKuji et al 2000). The retrieved properties obtained with the current scheme are likely to agree with their corresponding physical counterparts with about the same level of accuracy, 1-2 units in the 0.64-µm optical depth for clouds with optical depths ~10, and 1-2 µm in droplet effective radius.…”

Section: Cloud Property Retrievalssupporting

confidence: 53%

Limits to the Aerosol Indirect Radiative Effect Derived from Observations of Ship Tracks

2002

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Section: Cloud Property Retrievalssupporting

confidence: 53%

Limits to the Aerosol Indirect Radiative Effect Derived from Observations of Ship Tracks

2002

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“…Nakajima and King (1990) were the first to develop an algorithm to derive COT and effective particle size from dual satellite radiance observations (at 0.75 and 2.16 mm). This technique was later applied to Advanced Very High Resolution Radiometer (AVHRR) observations (Nakajima and Nakajima 1995;Kuji et al 2000). The dual-view capacity of ATSR-2 was employed by Evans and Haigh (1995) to retrieve COT and effective particle size.…”

Section: Introductionmentioning

confidence: 99%

“…Validation of satellite observations with ground observations has been performed by various authors (see, e.g., Nakajima and Nakajima 1995;Kuji et al 2000;Jolivet and Feijt 2005). But the effect of inhomogeneity on such a validation effort has received little attention.…”

Section: Introductionmentioning

confidence: 99%

Validating the Validation: The Influence of Liquid Water Distribution in Clouds on the Intercomparison of Satellite and Surface Observations

Schutgens

Roebeling

2009

Journal of Atmospheric and Oceanic Technology

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The intercomparison of LWP retrievals from observations by a geostationary satellite imager [Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation (MSG)] and a ground-based microwave (MW) radiometer is examined in the context of the inhomogeneity of overcast cloudy skies. Although the influence of cloud inhomogeneity on satellite observations has received much attention, relatively little is known about its impact on validation studies. Given SEVIRI's large field of view (3 km 3 6 km for northern Europe), especially when compared to the narrow width of the radiometer tracks (100-200 m), cloud inhomogeneity may be expected to significantly affect the satellite retrieval validation.This paper quantifies the various validation uncertainties resulting from cloud inhomogeneities and proposes an approach to minimize these uncertainties. The study is performed by simulating both satellite and ground-based observations through resampling a set of high-resolution (100 m) cloud fields that are derived from 1 km 3 1 km Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The authors' technique for generating realistic high-resolution LWP fields preserves the information present in the original observations and creates extra LWP variation at smaller-length scales by considering clouds as simple fractals. The authors believe that this is a new technique for creating high-resolution LWP fields.Validation errors resulting from cloud inhomogeneity can be classified in two groups. The first group relates entirely to the retrieval process for satellite observations and includes the well-known plane-parallel bias as well as field-of-view mismatches between different channels used in the retrieval. The second group relates to differences in the scene observed by satellite and ground-based sensors. This includes systematic shifts in the observed scene resulting from viewing conditions (parallax effect), offsets between satellite images and ground sites, and different fields of view.Results indicate that the plane-parallel bias for the authors' sample of 604 clouds has a median value of 23.3 g m 22 . All other error contributions appear to be random and have no biases. For individual observations, the parallax effect easily dominates the total error budget for sites that are observed under large viewing angles (e.g., northern Europe). The authors show that this error may be partly compensated by using information about cloud-top heights and by spatially interpolating among an array of SEVIRI pixels to obtain the best estimate of the satellite-retrieved LWP value over the ground site. Optimal intercomparison of satellite and ground-based observations is also possible by matching the track length of the ground observations to the imager's pixel size in the wind direction.Thus, one surprising conclusion is that the LWP errors resulting from the second group (scene differences) are significantly larger than those resulting from the first group (satellite retrieval), even after correction...

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“…3.2 Partial cloudiness within the infrared channel Kuji et al (2000) discussed the partial cloud coverage within a pixel as a cause of retrieval error in the solar reflection method. The partial cloudiness was examined using SEVIRI high-resolution visible (HRV) data (1 km).…”

Section: Retrieval Of Optical Thickness and Effective Particle Radiusmentioning

confidence: 99%

Retrieval of Optical Thickness and Effective Particle Radius of Thin Low-Level Water Clouds using the Split Window of Meteosat-8

Kawamoto

Inoue²,

Lutz

et al. 2006

SOLA

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We developed a method to retrieve the optical thickness ( ) and effective particle radius (re) of water clouds using the split-window channels and the 8.7-µm channel of Meteosat-8. Valid ranges are approximately from 1 to 9 for and smaller than 18 µm for re. Water clouds were first identified using 8.7-µm and 11-µm data. The retrieval used the brightness temperature (TBB) and brightness temperature difference (BTD) between the split windows, as computed with the radiation code RSTAR5b for various properties of water clouds and vertical profiles of temperature and water vapor. Archshaped curves in the TBB and BTD domains resembled those for ice clouds, depending on and re. The retrieved cloud parameters were then compared to those retrieved by the solar reflection method, which uses the 0.6-, 3.9-, and 11-µm channels of Meteosat-8. Comparison of the two methods revealed that the split-window technique could capture spatial features for both and re, showing some agreement with re for the solar reflection but needing more improvement for in the current case study.

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The Retrieval of Effective Particle Radius and Liquid Water Path of Low-Level Marine Clouds from NOAA AVHRR Data

Cited by 11 publications

References 31 publications

Limits to the Aerosol Indirect Radiative Effect Derived from Observations of Ship Tracks

Limits to the Aerosol Indirect Radiative Effect Derived from Observations of Ship Tracks

Validating the Validation: The Influence of Liquid Water Distribution in Clouds on the Intercomparison of Satellite and Surface Observations

Retrieval of Optical Thickness and Effective Particle Radius of Thin Low-Level Water Clouds using the Split Window of Meteosat-8

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