Clouds-Aerosols-Precipitation Satellite Analysis Tool (CAPSAT)

Lensky, Itamar M.; Rosenfeld, Daniel

doi:10.5194/acp-8-6739-2008

Cited by 220 publications

(208 citation statements)

References 27 publications

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“…Panel (a) displays this scene in grey scales as observed by the HRV channel. Panel (b) shows the same scene using the day natural color false-color composite described in detail by Lensky and Rosenfeld (2008) at LRES resolution. This scheme uses the 1.6, 0.8 and 0.6 µm spectral channels as red, green, and blue signals, respectively, and facilitates the physical interpretation of the image, as it allows to easily distinguish different surface types, as well as ice and water clouds.…”

Section: Resultsmentioning

confidence: 99%

Downscaling of METEOSAT SEVIRI 0.6 and 0.8 μm channel radiances utilizing the high-resolution visible channel

Deneke

Roebeling

2010

Atmos. Chem. Phys.

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Abstract. An algorithm is introduced to downscale the 0.6 and 0.8 µm spectral channels of the METEOSTAT SEVIRI satellite imager from 3×3 km 2 (LRES) to 1×1 km 2 (HRES) resolution utilizing SEVIRI's high-resolution visible channel (HRV). Intermediate steps include the coregistration of lowand high-resolution images, lowpass filtering of the HRV channel with the spatial response function of the narrowband channels, and the estimation of a least-squares linear regression model for linking high-frequency variations in the HRV and narrowband images. The importance of accounting for the sensor spatial response function for matching reflectances at different spatial resolutions is demonstrated, and an estimate of the accuracy of the downscaled reflectances is provided. Based on a 1-year dataset of Meteosat SEVIRI images, it is estimated that on average, the reflectance of a HRES pixel differs from that of an enclosing LRES pixel by standard deviations of 0.049 and 0.052 in the 0.6 and 0.8 µm channels, respectively. By applying our downscaling algorithm, explained variance of 98.2 and 95.3 percent are achieved for estimating these deviations, corresponding to residual standard deviations of only 0.007 and 0.011 for the respective channels. For this dataset, a minor misregistration of the HRV channel relative to the narrowband channels of 0.36±0.11 km in East and 0.06±0.10 km in South direction is observed and corrected for, which should be negligible for most applications.

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Section: Resultsmentioning

confidence: 99%

Downscaling of METEOSAT SEVIRI 0.6 and 0.8 μm channel radiances utilizing the high-resolution visible channel

Deneke

Roebeling

2010

Atmos. Chem. Phys.

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“…The green component is modulated by the solar reflectance of 3.7 µm, which is roughly inversely proportional to the cloud drop effective radius, and the blue is modulated by the temperature. This provides a color scale that classifies cloud composition and temperature by its colors, as described by Rosenfeld and Lensky (1998) and Lensky and Rosenfeld (2008). …”

Section: Contrast Of Cloud Properties Reflected By Viirs and Modismentioning

confidence: 99%

“…The qualitative approach was also applied to the geostationary satellite. With the rich spectral information of the SEVIRI instrument on the METEOSAT Second Generation (MSG) satellite, Lensky and Rosenfeld (2008) developed five RGB combinations to represent the additional information on microphysics of clouds in relation to the environmental and meteorological elements.…”

Section: Introductionmentioning

confidence: 99%

High-resolution (375 m) cloud microstructure as seen from the NPP/VIIRS satellite imager

Liu

Zhu

et al. 2014

Atmos. Chem. Phys.

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Abstract. VIIRS (Visible Infrared Imaging RadiometerSuite), onboard the Suomi NPP (National Polar-orbiting Partnership) satellite, has an improved resolution of 750 m with respect to the 1000 m of the Moderate Resolution Imaging Spectroradiometer for the channels that allow retrieving cloud microphysical parameters such as cloud drop effective radius (r e ). VIIRS also has an imager with five channels of double resolution of 375 m, which was not designed for retrieving cloud products. A methodology for a high-resolution retrieval of r e and microphysical presentation of the cloud field based on the VIIRS imager was developed and evaluated with respect to MODIS in this study. The tripled microphysical resolution with respect to MODIS allows obtaining new insights for cloud-aerosol interactions, especially at the smallest cloud scales, because the VIIRS imager can resolve the small convective elements that are sub-pixel for MODIS cloud products. Examples are given for new insights into ship tracks in marine stratocumulus, pollution tracks from point and diffused sources in stratocumulus and cumulus clouds over land, deep tropical convection in pristine air mass over ocean and land, tropical clouds that develop in smoke from forest fires and in heavy pollution haze over densely populated regions in southeastern Asia, and for pyrocumulonimbus clouds.It is found that the VIIRS imager provides more robust physical interpretation and refined information for cloud and aerosol microphysics as compared to MODIS, especially in the initial stage of cloud formation. VIIRS is found to identify significantly more fully cloudy pixels when small boundary layer convective elements are present. This, in turn, allows for a better quantification of cloud-aerosol interactions and impacts on precipitation-forming processes.

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“…The cloud classification for the training data set has been made through a careful visual inspection of the SEVIRI images. The clear and cloudy pixels were selected manually after observing the spectral characteristics in SEVIRI IR/VIS images as well as in their RGB composition, a useful practice for distinguishing cloudy classes (Lensky and Rosenfeld, 2008). In order to collect the training samples for the convective cloud class, the cloudy SEVIRI pixels have been matched with the corresponding PEMW-RR and radar-derived RR values, if available.…”

Section: Cloud Classification Algorithm Descriptionmentioning

confidence: 99%

A statistical approach for rain intensity differentiation using Meteosat Second Generation–Spinning Enhanced Visible and InfraRed Imager observations

Ricciardelli

Cimini

Paola

et al. 2014

Hydrol. Earth Syst. Sci.

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Abstract. This study exploits the Meteosat Second Generation (MSG)-Spinning Enhanced Visible and Infrared Imager (SEVIRI) observations to evaluate the rain class at high spatial and temporal resolutions and, to this aim, proposes the Rain Class Evaluation from Infrared and Visible observation (RainCEIV) technique. RainCEIV is composed of two modules: a cloud classification algorithm which individuates and characterizes the cloudy pixels, and a supervised classifier that delineates the rainy areas according to the three rainfall intensity classes, the non-rainy (rain rate value < 0.5 mm h −1 ) class, the light-to-moderate rainy class (0.5 mm h −1 ≤ rain rate value < 4 mm h −1 ), and the heavyto-very-heavy-rainy class (rain rate value ≥ 4 mm h −1 ). The second module considers as input the spectral and textural features of the infrared and visible SEVIRI observations for the cloudy pixels detected by the first module. It also takes the temporal differences of the brightness temperatures linked to the SEVIRI water vapour channels as indicative of the atmospheric instability strongly related to the occurrence of rainfall events.The rainfall rates used in the training phase are obtained through the Precipitation Estimation at Microwave frequencies, PEMW (an algorithm for rain rate retrievals based on Atmospheric Microwave Sounder Unit (AMSU)-B observations). RainCEIV's principal aim is that of supplying preliminary qualitative information on the rainy areas within the Mediterranean Basin where there is no radar network coverage. The results of RainCEIV have been validated against radar-derived rainfall measurements from the Italian Operational Weather Radar Network for some case studies limited to the Mediterranean area. The dichotomous assessment related to daytime (nighttime) validation shows that RainCEIV is able to detect rainy/non-rainy areas with an accuracy of about 97 % (96 %), and when all the rainy classes are considered, it shows a Heidke skill score of 67 % (62 %), a bias score of 1.36 (1.58), and a probability of detection of rainy areas of 81 % (81 %).

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Clouds-Aerosols-Precipitation Satellite Analysis Tool (CAPSAT)

Cited by 220 publications

References 27 publications

Downscaling of METEOSAT SEVIRI 0.6 and 0.8 μm channel radiances utilizing the high-resolution visible channel

Downscaling of METEOSAT SEVIRI 0.6 and 0.8 μm channel radiances utilizing the high-resolution visible channel

High-resolution (375 m) cloud microstructure as seen from the NPP/VIIRS satellite imager

A statistical approach for rain intensity differentiation using Meteosat Second Generation–Spinning Enhanced Visible and InfraRed Imager observations

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