Atmospheric Liquid Water Content Derived from Parameterization of Nimbus 6 Scanning Microwave Spectrometer Data

Liou, Kuo‐Nan; Duff, Alan D.

doi:10.1175/1520-0450(1979)018<0099:alwcdf>2.0.co;2

Cited by 16 publications

(3 citation statements)

References 3 publications

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“…The primary advantage of microwave frequencies within this context is that cirrus clouds are transparent, so that only water clouds are sensed. This allows cloud liquid water content of low cloud over the ocean to be inferred [Liou and Duff, 1979] and partially removes the cirrus and low and middle cloud ambiguity [Yeh and Liou, 1983]. Further, this advantage may be exploited to infer cloud thickness [Pandey et al, 1983].…”

Section: Comparisons Of Results With Retrieved Quantities Have Largelmentioning

confidence: 99%

Satellite remote sensing of meteorological parameters for global numerical weather prediction

Isaacs¹,

Hoffman²,

Kaplan³

1986

Reviews of Geophysics

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Remote sensing of meteorological parameters helps to provide the initial conditions for numerical weather prediction (NWP). Desired fields include those of temperature, moisture, winds, clouds, and surface properties. For high horizontal resolution and global coverage, satellite data are an unrivaled source of information. The basic form of this information is satellite sensor‐incident, wavelength‐dependent radiance (or equivalently, brightness temperature). The process of retrieving meteorological information from the observed radiances involves solving an inverse problem. Often the inverse problem is ill posed or poorly conditioned. A variety of methods have been used to make the inverse problem more tractable: in essence, some a priori information is required. The inverse problem is the opposite of the calculation of radiances, given the relevant meteorological profiles, surface conditions, and sensor geometry. This so‐called forward problem is an integral part of physical retrieval methods which are now generally favored over conventional statistical retrieval methods. Physical retrieval methods, in practice, actually rely to a certain degree on a priori statistics. Temperature retrievals, especially in the southern hemisphere, are of proven importance in NWP involving global scale models. Vertical temperature profiles are obtained from observed radiances in a set of emission bands with varying optical depths. In comparison to conventional radiosonde data, current temperature retrievals have coarse vertical resolution. There is also some evidence that retrieved synoptic features are somewhat smoothed horizontally in spite of the high horizontal resolution of the temperature retrievals. The usefulness of other geophysical parameters for NWP is not well established and is largely untested, although a great many retrieval methods have been proposed or developed for a variety of potentially interesting parameters. The reason for this is twofold: these data are expected to have the most impact on mesoscale modeling, but mesoscale analysis systems are still relatively primitive. Moisture variables—that is, specific humidity, clouds, and precipitation—are retrievable and are potentially very useful. It is theoretically possible to retrieve specific humidity profiles by using methods analogous to those used to retrieve temperatures. However, results to date with available sensors have not been wholly satisfactory. Other moisture parameters are somewhat easier to retrieve; for example, cloud cover may be obtained by a variety of techniques from imagery in visible and/or infrared spectral regions, and vertically integrated water vapor is well correlated with emissions at microwave frequencies, especially over the ocean. Unfortunately, these parameters are not readily assimilated by a global NWP model, and special methods are required. A variety of other meteorological parameters which may be used for NWP are retrievable. These include winds at the surface from microwave sensors and cloud drift winds aloft, as well as o...

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Section: Comparisons Of Results With Retrieved Quantities Have Largelmentioning

confidence: 99%

Satellite remote sensing of meteorological parameters for global numerical weather prediction

Isaacs¹,

Hoffman²,

Kaplan³

1986

Reviews of Geophysics

View full text Add to dashboard Cite

show abstract

“…The primary advantage of microwave frequencies within this context is that cirrus clouds are transparent so that only water clouds are sensed. This allows cloud liquid water content of low cloud over the ocean to be inferred (Liou and Duff, 1979) and partially removes the cirrus and low and middle cloud ambiguity (Yeh and Liou, 1983). Chahine (198?…”

Section: Respectively B(t) Is Surface Infrared Emission and C Is Clomentioning

confidence: 99%

Outlook for Improved Numerical Weather Prediction Using Satellite Data with a Special Emphasis on the Hydrological Variables,

Kaplan¹,

Hoffman²,

Isaacs³

et al. 1983

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“…It is apparent that to simulate the upwelling brightness temperature requires prior knowledge of (1) the atmospheric transmittance of a given frequency, which, in general, is related to the relevant gaseous and temperature profiles, and (2) the surface characteristics, which are described by the surface temperature and surface emissivity. The transmittance program employed in this study has been described in a previous AFGL report by Liou et al (1979) and so we shall not give further discussion here. However, the surface emissivity and surface temperature are important factors in the brightness temperature computation and require some analysis.…”

Section: Comparisons Between Ssm/t Data and Calculated Brightness Temmentioning

confidence: 99%

Investigation of the Forward Radiative Transfer Problem Utilizing DMSP and Nimbus 6 Data.

Liou¹,

Aufderhaar²,

Hutchison³

et al. 1980

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Atmospheric Liquid Water Content Derived from Parameterization of Nimbus 6 Scanning Microwave Spectrometer Data

Cited by 16 publications

References 3 publications

Satellite remote sensing of meteorological parameters for global numerical weather prediction

Satellite remote sensing of meteorological parameters for global numerical weather prediction

Outlook for Improved Numerical Weather Prediction Using Satellite Data with a Special Emphasis on the Hydrological Variables,

Investigation of the Forward Radiative Transfer Problem Utilizing DMSP and Nimbus 6 Data.

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