Interrelation of aerosols, water vapor and clouds on a global scale

Mukai, Sonoyo; Sano, Itaru; Yasumoto, Masayoshi

doi:10.1016/s0273-1177(03)90541-2

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…Aerosols can act as nuclei on which water condenses to form cloud droplets at super saturations lower than those needed for homogeneous water nucleation (Cruz and Pandis, 1997). It has been confirmed that the indirect effect of aerosols that play sufficient role as cloud condensation nuclei (Mukai et al, 2003). A few groups of clouds were distributed at altitude 10 to 15 km at 11.82N and 100.91 E to −12.62 S to 106.18 E. On 5 February 2009, numerous clouds located at 10 to 15 km height within 5 N and 101.9 E to −11.08 S and 105.8 E. Maxima in the cloud-top altitude distribution mainly occurred in the upper troposphere, between 12 and 15 km and in the lower troposphere, below about 4 km.…”

Section: Resultsmentioning

confidence: 89%

Two-dimensional Simulation of Aerosol?Cloud Profile

Alias¹,

MatJafri²,

San³

et al. 2009

MAS

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Developments of algorithm and computer graphics simulation are important to distinguish between aerosols and clouds in remote sensing data and images. The distribution of aerosols and clouds are need to be distinguishable in order to study their interactions with one another and identify both affecting factors towards Earth's climate stability. The objective of this paper is to expand the current work done in building new algorithm and simulation method to differentiate aerosols and clouds in spaceborne lidar data and images using image processing and computer graphics software, PCI Geomatica 10.1 and SCION Image. The new algorithm and simulation that has developed showed good results and clarify the vertical distribution of aerosols and clouds in the atmosphere. Plot profiles of clouds on both days showed higher pixel values than aerosols. Aerosols have been found consistently to have higher mean at altitude 0−5 km and 15−20 km on both days.

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Section: Resultsmentioning

confidence: 89%

Two-dimensional Simulation of Aerosol?Cloud Profile

Alias¹,

MatJafri²,

San³

et al. 2009

MAS

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“…CALIPSO is orbiting at 705 km height from Earth's surface and in 98º inclination orbit. It has ability to provide daily maps of aerosols and clouds distribution globally [12]. CALIPSO used Nd:YAG based lidar system consist three main instruments at the CALIPSO payload which are the CloudAerosol LIDAR with Orthogonal Polarization (CALIOP), an Imaging Infrared Radiometer (IIR), and a moderate spatial resolution Wide-Field Camera (WFC) [13].…”

Section: Introductionmentioning

confidence: 99%

LIDAR and image processing of aerosol-cloud vertical profile

Alias

Othman

MatJafri

et al. 2012

2012 IEEE International Conference on Control System, Computing and Engineering

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Light Detection and Ranging (LIDAR) was on board a satellite named CALIPSO which is one of the satellites flying in synchronized with other A-Train satellites. Backscattering profiles from LIDAR can determine the height and profiles of the clouds. Air pollution and other particulate matter trends in the atmosphere will affect the growth of clouds. This paper is focusing in analysing LIDAR and remote sensing data using image processing technique. Findings of this paper signify a weighty progress in the understanding of the processes with significant effects for both climate science and sustainable development.

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“…There have been a number of successful AIE observations using satellites. Such measurements include the aerosol retrieval products of the Advanced Very High Resolution Radiometer (AVHRR) [ Harshvardhan et al , 2002; Nakajima et al , 2001; Rudich et al , 2003; Schwartz et al , 2002; Sekiguchi et al , 2003], Sea‐viewing Wide Field‐of‐view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS) [ Gassó and Hegg , 2003; Tanre et al , 1999], Polarization and Directionality of the Earth's Reflectances (POLDER) [ Bréon et al , 2002; Deuze et al , 2000, 2001; Mukai et al , 2003; Quaas et al , 2004; Rosenfeld , 2000; Rosenfeld and Feingold , 2003; Sekiguchi et al , 2003] and Tropical Rainfall Measuring Mission (TRMM) satellite [ Rosenfeld , 2000].…”

Section: Introductionmentioning

confidence: 99%

On the determination of a cloud condensation nuclei from satellite: Challenges and possibilities

Kapustin¹,

Clarke²,

Shinozuka³

et al. 2006

J. Geophys. Res.

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[1] We use aerosol size distributions measured in the size range from 0.01 to 10+ mm during Transport and Chemical Evolution over the Pacific (TRACE-P) and Aerosol Characterization Experiment-Asia (ACE-Asia), results of chemical analysis, measured/modeled humidity growth, and stratification by air mass types to explore correlations between aerosol optical parameters and aerosol number concentration. Size distributions allow us to integrate aerosol number over any size range expected to be effective cloud condensation nuclei (CCN) and to provide definition of a proxy for CCN (CCN proxy ). Because of the internally mixed nature of most accumulation mode aerosol and the relationship between their measured volatility and solubility, this CCN proxy can be linked to the optical properties of these size distributions at ambient conditions. This allows examination of the relationship between CCN proxy and the aerosol spectral radiances detected by satellites. Relative increases in coarse aerosol (e.g., dust) generally add only a few particles to effective CCN but significantly increase the scattering detected by satellite and drive the Angstrom exponent (a a a) toward zero. This has prompted the use of a so-called aerosol index (AI) on the basis of the product of the aerosol optical depth and the nondimensional a a a, both of which can be inferred from satellite observations. This approach biases the AI to be closer to scattering values generated by particles in the accumulation mode that dominate particle number and is therefore dominated by sizes commonly effective as CCN. Our measurements demonstrate that AI does not generally relate well to a measured proxy for CCN unless the data are suitably stratified. Multiple layers, complex humidity profiles, dust with very low a a a mixed with pollution, and size distribution differences in pollution and biomass emissions appear to contribute most to method limitations. However, we demonstrate that these characteristic differences result in predictable influences on AI. These results suggest that inference of CCN from satellites will be challenging, but new satellite and model capabilities could possibly be integrated to improve this retrieval.

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Interrelation of aerosols, water vapor and clouds on a global scale

Cited by 4 publications

References 20 publications

Two-dimensional Simulation of Aerosol?Cloud Profile

Two-dimensional Simulation of Aerosol?Cloud Profile

LIDAR and image processing of aerosol-cloud vertical profile

On the determination of a cloud condensation nuclei from satellite: Challenges and possibilities

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