Determination of Semi-Transparent Cirrus Cloud Temperature from Infrared Radiances: Application to METEOSAT

Szejwach, G.

doi:10.1175/1520-0450(1982)021<0384:dostcc>2.0.co;2

Cited by 80 publications

(38 citation statements)

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“…The line connecting the clusters is compared to theoretical calculations of the radiances for different cloud pressures. The intersection of the two gives the cloud height [19], [20]. The H O-intercept and CO slicing methods do not work well with low-level or multilayered clouds.…”

Section: Wind Retrieval Methodsmentioning

confidence: 99%

Cloud-drift and water vapor winds in the polar regions from MODIS

Key

Santek

Velden

et al. 2003

IEEE Trans. Geosci. Remote Sensing

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Abstract-Wind products from geostationary satellites have been generated for over 20 years and are now used in numerical weather prediction systems. However, geostationary satellites are of limited utility poleward of the midlatitudes. This study demonstrates the feasibility of deriving high latitude tropospheric wind information from polar-orbiting satellites. The methodology employed is based on the algorithms currently used with geostationary satellites, modified for use with the Moderate-Resolution Imaging Spectroradiometer (MODIS) infrared window and water vapor bands. These bands provide wind information throughout the troposphere in both clear and cloudy conditions. The project presents some unique challenges, including the irregularity of temporal sampling, varying viewing geometries, and uncertainties in wind vector height assignment as a result of low atmospheric water vapor amounts and thin clouds. A 30-day case study dataset has been produced and is being used in model impact studies. Preliminary results are encouraging: when the MODIS winds are assimilated in the European Centre for Medium Range Weather Forecasts (ECMWF) system and the NASA Data Assimilation Office system, forecasts of the geopotential height for the Arctic, the Northern Hemisphere extratropics, and the Antarctic are improved significantly.

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Section: Wind Retrieval Methodsmentioning

confidence: 99%

Cloud-drift and water vapor winds in the polar regions from MODIS

Key

Santek

Velden

et al. 2003

IEEE Trans. Geosci. Remote Sensing

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“…In the case of semi-transparent or sub-pixel clouds, however, this technique fails and the CO 2 slicing method is used. Here, infrared channel radiances at IR 108 and at IR 134 for black clouds located at different layers of the atmosphere are ratioed (Cayla and Tomassini, 1978;Szejwach, 1982;Nieman et al, 1993;Menzel et al, 1983;Schmetz et al, 1993). For both methods atmospheric profiles of temperature, pressure, water vapour and ozone are taken from ECMWF analyses with a 0.25 • ×0.25 • spatial resolution in longitude and latitude.…”

Section: Cloud Top Heightmentioning

confidence: 99%

Validation of cloud property retrievals with simulated satellite radiances: a case study for SEVIRI

Bugliaro

Zinner²,

Keil³

et al. 2011

Atmos. Chem. Phys.

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Abstract. Validation of cloud properties retrieved from passive spaceborne imagers is essential for cloud and climate applications but complicated due to the large differences in scale and observation geometry between the satellite footprint and the independent ground based or airborne observations. Here we illustrate and demonstrate an alternative approach: starting from the output of the COSMO-EU weather model of the German Weather Service realistic three-dimensional cloud structures at a spatial scale of 2.33 km are produced by statistical downscaling and microphysical properties are associated to them. The resulting data sets are used as input to the one-dimensional radiative transfer model libRadtran to simulate radiance observations for all eleven low resolution channels of MET-8/SEVIRI. At this point, both cloud properties and satellite radiances are known such that cloud property retrieval results can be tested and tuned against the objective input "truth". As an example, we validate a cloud property retrieval of the Institute of Atmospheric Physics of DLR and that of EUMETSAT's Climate Monitoring Science Application Facility CMSAF. Cloud detection and cloud phase assignment perform well. By both retrievals 88% of the pixels are correctly classified as clear or cloudy. The DLR algorithm assigns the correct thermodynamic phase to 95% of the cloudy pixels and the CMSAF retrieval to 84%. Cloud top temperature is slightly overestimated by the DLR code (+3

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“…radiance ratioing (also referred to as CO 2 absorption, CO 2 slicing and split window technique) (Smith et al, 1970;Smith and Platt, 1978;Menzel et al, 1983;Eyre and Menzel, 1989;Zhang and Menzel, 2002;Menzel et al, 2008), radiance fitting (e.g. Szejwach, 1982;Nieman et al, 1993;Schmetz et al, 1993) and optimal estimation (e.g. Heidinger and Pavolonis, 2009;Sayer et al, 2011;Watts et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

et al. 2017

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Abstract. Cirrus clouds play an important role in climate as they tend to warm the Earth-atmosphere system. Nevertheless their physical properties remain one of the largest sources of uncertainty in atmospheric research. To better understand the physical processes of cirrus clouds and their climate impact, enhanced satellite observations are necessary. In this paper we present a new algorithm, CiPS (Cirrus Properties from SEVIRI), that detects cirrus clouds and retrieves the corresponding cloud top height, ice optical thickness and ice water path using the SEVIRI imager aboard the geostationary Meteosat Second Generation satellites. CiPS utilises a set of artificial neural networks trained with SE-VIRI thermal observations, CALIOP backscatter products, the ECMWF surface temperature and auxiliary data.CiPS detects 71 and 95 % of all cirrus clouds with an optical thickness of 0.1 and 1.0, respectively, that are retrieved by CALIOP. Among the cirrus-free pixels, CiPS classifies 96 % correctly. With respect to CALIOP, the cloud top height retrieved by CiPS has a mean absolute percentage error of 10 % or less for cirrus clouds with a top height greater than 8 km. For the ice optical thickness, CiPS has a mean absolute percentage error of 50 % or less for cirrus clouds with an optical thickness between 0.35 and 1.8 and of 100 % or less for cirrus clouds with an optical thickness down to 0.07 with respect to the optical thickness retrieved by CALIOP. The ice water path retrieved by CiPS shows a similar performance, with mean absolute percentage errors of 100 % or less for cirrus clouds with an ice water path down to 1.7 g m −2 . Since the training reference data from CALIOP only include ice water path and optical thickness for comparably thin clouds, CiPS also retrieves an opacity flag, which tells us whether a retrieved cirrus is likely to be too thick for CiPS to accurately derive the ice water path and optical thickness.By retrieving CALIOP-like cirrus properties with the large spatial coverage and high temporal resolution of SEVIRI during both day and night, CiPS is a powerful tool for analysing the temporal evolution of cirrus clouds including their optical and physical properties. To demonstrate this, the life cycle of a thin cirrus cloud is analysed.

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Determination of Semi-Transparent Cirrus Cloud Temperature from Infrared Radiances: Application to METEOSAT

Cited by 80 publications

References 0 publications

Cloud-drift and water vapor winds in the polar regions from MODIS

Cloud-drift and water vapor winds in the polar regions from MODIS

Validation of cloud property retrievals with simulated satellite radiances: a case study for SEVIRI

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

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