A Comparison of Several Techniques to Assign Heights to Cloud Tracers

Nieman, Steven J.; Schmetz, Johannes; Menzel, W. Paul

doi:10.1175/1520-0450(1993)032<1559:acostt>2.0.co;2

Cited by 110 publications

(80 citation statements)

References 8 publications

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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%

“…The final retrieved cloud top pressure is the averaged cloud top pressure obtained using single sounding channels. If this first step fails, the radiance ratioing method, adapted from the CO 2 slicing by (Smith et al, 1970;Chahine, 1974;Smith et al, 1974;Smith and Platt, 1978;Menzel et al, 1983;Eyre and Menzel, 1989;Nieman et al, 1993), is applied successively to the window IR 108 and the sounding channels WV 073, WV 062 and IR 134 until a result is obtained. In case this result is warmer than the corresponding IR 108 brightness temperature, the method for opaque clouds is used instead.…”

Section: Cloud Top Heightmentioning

confidence: 99%

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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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Section: Cloud Top Heightmentioning

confidence: 99%

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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show abstract

“…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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“…In this method the window channel Brightness Temperature (BT) within the target area and a mean value for the coldest 20% of the sample were used to represent the temperature at the cloud top and this temperature was compared with a numerical forecast of the vertical temperature profile to arrive at the height of the cloud. The carbon dioxide (CO 2 ) slicing algorithm (Menzel et al, 1983) is the most accurate and dependable method of assigning heights to semi transparent tracers (Nieman et al, 1993). In the absence of the CO 2 -absorption channel, the H 2 Ointercept algorithm (Schmetz et al, 1993) is found to be the best method.…”

Section: Height Assignment Techniquesmentioning

confidence: 99%

An inter‐comparison of Kalpana‐1 and Meteosat‐7 atmospheric motion vectors against radiosonde winds and NWP forecasts during monsoon 2011

Rani

Gupta

2013

Meteorological Applications

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Geostationary satellites, Kalpana-1 (74 • E) and • E) have overlapping spatial coverage over the Indian region. In this study, the atmospheric motion vectors (AMVs) derived from the above two satellites were compared separately with respect to in situ radiosonde (RS) winds and short-range numerical weather prediction (NWP) forecasts from the National Centre for Medium Range Weather Forecasting's (NCMRWF) T574L64 Global modelling system in terms of statistical parameters vis., speed bias and root mean square vector difference (RMSVD) during the 2011 monsoon. Kalpana-1 AMVs showed high RMSVD with respect to the in situ RS winds and short range NWP forecasts. However, the monsoon features, the low level jet (LLJ) and the tropical easterly jet (TEJ) were clearly depicted in the mean observed AMVs from both the satellites. The high error associated with Kalpana-1 winds compared to that of Meteosat-7 winds could be due to the difference in the spatial resolution of different channels and the algorithm used for deriving the winds.

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A Comparison of Several Techniques to Assign Heights to Cloud Tracers

Cited by 110 publications

References 8 publications

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

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

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

An inter‐comparison of Kalpana‐1 and Meteosat‐7 atmospheric motion vectors against radiosonde winds and NWP forecasts during monsoon 2011

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