Cloud Properties inferred from 812-µm Data

Strabala, Kathleen I.; Ackerman, Steven A.; Menzel, W. Paul

doi:10.1175/1520-0450(1994)033<0212:cpifd>2.0.co;2

Cited by 280 publications

(209 citation statements)

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“…MODIS 1 km cloud products (information available online at http://daac.gsfc.nasa. gov/MODIS/products.shtml) include, but are not limited to, the cloud mask (confident clear, probably clear, confident cloudy, and probably cloudy) [Ackerman et al, 1998], the cloud-phase mask (water clouds, ice clouds, and mixed phase) [Strabala et al, 1994;Baum et al, 2000], the cloud classification mask [Li et al, 2003;Li et al, 2007], the cloud particle size (CPS), and the cloud optical thickness Platnick et al, 2003]. With a collocation methodology developed by Nagle [1998], AIRS subpixel cloud detection and characterization can be derived by taking advantage of high spatial resolution MODIS cloud products.…”

Section: Methodsmentioning

confidence: 99%

Advanced infrared sounder subpixel cloud detection with imagers and its impact on radiance assimilation in NWP

Wang

et al. 2014

Geophysical Research Letters

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Accurate cloud detection is very important for infrared (IR) radiance assimilation; improved cloud detection could reduce cloud contamination and hence improve the assimilation. Although operational numerical weather prediction (NWP) centers are using IR sounder radiance data for cloud detection, collocated high spatial resolution imager data could help sounder subpixel cloud detection and characterization. IR sounder radiances with improved cloud detection using Atmospheric Infrared Sounder (

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Section: Methodsmentioning

confidence: 99%

Advanced infrared sounder subpixel cloud detection with imagers and its impact on radiance assimilation in NWP

Wang

et al. 2014

Geophysical Research Letters

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“…Strabala et al (1994) demonstrated how the BTD of 8 µm and 11 µm, and of 11 µm and 12 µm, can distinguish between cirrus and water clouds using brightness temperature measurements from the High Resolution Infrared Sounder (HIRS) and the Advanced Very High Resolution Radiometer (AVHRR). Although passive nadir sounders are well-equipped to measure clouds globally due to their high horizontal resolution, their limited vertical resolution cannot resolve cloud top heights with high accuracy (Weisz et al, 2007).…”

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confidence: 99%

MIPAS detection of cloud and aerosol particle occurrence in the UTLS with comparison to HIRDLS and CALIOP

et al. 2012

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Abstract. Satellite infrared emission instruments require efficient systems that can separate and flag observations which are affected by clouds and aerosols. This paper investigates the identification of cloud and aerosols from infrared, limb sounding spectra that were recorded by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), a high spectral resolution Fourier transform spectrometer on the European Space Agency's (ESA) ENVISAT (Now inoperative since April 2012 due to loss of contact). Specifically, the performance of an existing cloud and aerosol particle detection method is simulated with a radiative transfer model in order to establish, for the first time, confident detection limits for particle presence in the atmosphere from MIPAS data. The newly established thresholds improve confidence in the ability to detect particle injection events, plume transport in the upper troposphere and lower stratosphere (UTLS) and better characterise cloud distributions utilising MIPAS spectra. The method also provides a fast front-end detection system for the MIPClouds processor; a processor designed for the retrieval of macro-and microphysical cloud properties from the MIPAS data.It is shown that across much of the stratosphere, the threshold for the standard cloud index in band A is 5.0 although threshold values of over 6.0 occur in restricted regimes. Polar regions show a surprising degree of uncertainty at altitudes above 20 km, potentially due to changing stratospheric trace gas concentrations in polar vortex conditions and poor signal-to-noise due to cold atmospheric temperatures. The optimised thresholds of this study can be used for much of the time, but time/composition-dependent thresholds are recommended for MIPAS data for the strongly perturbed polar stratosphere. In the UT, a threshold of 5.0 applies at 12 km and above but decreases rapidly at lower altitudes. The new thresholds are shown to allow much more sensitive detection of particle distributions in the UTLS, with extinction detection limits above 13 km often better than 10 −4 km −1 , with values approaching 10 −5 km −1 in some cases.Comparisons of the new MIPAS results with cloud data from HIRDLS and CALIOP, outside of the poles, establish a good agreement in distributions (cloud and aerosol top heights and occurrence frequencies) with an offset between MIPAS and the other instruments of 0.5 km to 1 km between 12 km and 20 km, consistent with vertical oversampling of extended cloud layers within the MIPAS field of view. We conclude that infrared limb sounders provide a very consistent picture of particles in the UTLS, allowing detection limits which are consistent with the lidar observations. Investigations of MIPAS data for the Mount Kasatochi volcanic eruption on the Aleutian Islands and the Black Saturday fires in Australia are used to exemplify how useful MIPAS limb sounding data were for monitoring aerosol injections into the UTLS. It is shown that the new thresholds allowed such events to be much more effectively derived fro...

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“…Cloud radiative properties in both channels are dependent upon the cloud particle size. Scattering processes and the dependence on particle size are stronger in the 8.7-lm channel relative to the 10.8-lm channel (Strabala et al 1994). Therefore, for larger particles, the DT 8.7-10.8 increases.…”

Section: Parameters For Identification Of Raining Pixelsmentioning

confidence: 99%

Rainfall detection over northern Algeria by combining MSG and TRMM data

Ouallouche

Ameur

2014

Appl Water Sci

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In this paper, a new method to delineate rain areas in northern Algeria is presented. The proposed approach is based on the blending of the geostationary meteosat second generation (MSG), infrared channel with the low-earth orbiting passive tropical rainfall measuring mission (TRMM). To model the system designed, we use an artificial neural network (ANN). We seek to define a relationships between three parameters calculated from TRMM microwave imager (TMI) associated with four parameters from infrared sensors of MSG satellite and two classes (rain, no-rain) from precipitation radar (PR) TRMM data. The seven spectral parameters issued from MSG and TMI are used as input data. Rain/no-rain classes from PR are used as the output data of this ANN. Two steps are necessary: training and validation. Results in the developed scheme are compared with the results of a reference method which is the scattering index (SI) method. The result shows that the developed model works very well and overcomes the shortcomings of the SI method.

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Cloud Properties inferred from 812-µm Data

Cited by 280 publications

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Advanced infrared sounder subpixel cloud detection with imagers and its impact on radiance assimilation in NWP

Advanced infrared sounder subpixel cloud detection with imagers and its impact on radiance assimilation in NWP

MIPAS detection of cloud and aerosol particle occurrence in the UTLS with comparison to HIRDLS and CALIOP

Rainfall detection over northern Algeria by combining MSG and TRMM data

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Cloud Properties inferred from 812-µm Data

Cited by 280 publications

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Advanced infrared sounder subpixel cloud detection with imagers and its impact on radiance assimilation in NWP

Advanced infrared sounder subpixel cloud detection with imagers and its impact on radiance assimilation in NWP

MIPAS detection of cloud and aerosol particle occurrence in the UTLS with comparison to HIRDLS and CALIOP

Rainfall detection over northern Algeria by combining MSG and TRMM data

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Cloud Properties inferred from 812-µm Data