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P. Albert
,
R. Bennartz
, and
J. Fischer

Abstract

The “differential absorption technique” is used to derive columnar water vapor contents above clouds. Radiative transfer simulations were carried out for two different spectral channels, one channel within the ρστ–water water absorption band and one window channel. The simulations were performed for two different instruments, ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) and the Polarization and Directionality of the Earth Reflectances (POLDER) instrument on the ADEOS platform.

Vertical weighting functions of the contribution of different cloud layers to the total absorption by water vapor have been calculated that state that in case of clouds above ocean surfaces, the total absorption is determined mainly by the water vapor content above the clouds, while over land surfaces the influence of the lower atmospheric layers increases.

Radiative transfer simulations have been performed for a large number of cloudy atmospheric profiles and have been used to develop a regression-type algorithm for the retrieval of water vapor content above clouds with a theoretical accuracy between 1 and 3 kg m−2. A first verification using POLDER measurements together with radio soundings shows a mean rms error of 1.8 kg m−2 over ocean and 2.0 kg m−2 over land surfaces.

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P. Albert
,
R. Bennartz
,
R. Preusker
,
R. Leinweber
, and
J. Fischer

Abstract

This paper presents first validation results for an algorithm developed for the retrieval of integrated columnar water vapor from measurements of the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board the polar-orbiting Terra and Aqua platforms. The algorithm is based on the absorption of reflected solar radiation by atmospheric water vapor and allows the retrieval of integrated water vapor above cloud-free land surfaces. A comparison of the retrieved water vapor with measurements of the Microwave Water Radiometer at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site for a 10-month period in 2002 showed an rms deviation of 1.7 kg m−2 and a bias of 0.6 kg m−2. A comparison with radio soundings in central Europe from July 2002 to April 2003 showed an rms deviation of 2 kg m−2 and a bias of −0.8 kg m−2.

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P. Bauer
,
L. Schanz
,
R. Bennartz
, and
P. Schlüssel

Abstract

The status of current rainfall-retrieval techniques by satellite radiometry has been evaluated by recent international algorithm intercomparison projects. As a general result, passive microwave techniques perform superiorly for instantaneous applications over oceans, while infrared or combined infrared–microwave techniques show improved monthly rainfall accumulations, mainly due to the high temporal sampling by geosynchronous observations. Merging microwave, visible, and infrared imagery data available on the same satellite such as the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and the Visible Infrared Scanner (VIRS) provides further potential for the improvement of instantaneous retrievals. A case study is shown that demonstrates the stepwise degradation of information contained in the microwave signals when three-dimensional cloud effects and realistic antenna patterns are simulated for a convective cloud obtained from Doppler polarization radar soundings. Simultaneous visible and infrared data may contribute mainly to better rain-regime classification, in particular when sophisticated cloud identification techniques and cloud parameter retrievals are incorporated. Although the beam-filling problem is not solved by the TMI–VIRS combination alone, some other progress, for example, concerning better coastline treatment, is shown.

With respect to monthly products and the climatologically important observation of diurnal rainfall variations, the TRMM sensor combination will provide a calibration standard to be applied to geosynchronous sensors.

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Matthew D. Shupe
,
David D. Turner
,
Von P. Walden
,
Ralf Bennartz
,
Maria P. Cadeddu
,
Benjamin B. Castellani
,
Christopher J. Cox
,
David R. Hudak
,
Mark S. Kulie
,
Nathaniel B. Miller
,
Ryan R. Neely III
,
William D. Neff
, and
Penny M. Rowe

Cloud and atmospheric properties strongly influence the mass and energy budgets of the Greenland Ice Sheet (GIS). To address critical gaps in the understanding of these systems, a new suite of cloud- and atmosphere-observing instruments has been installed on the central GIS as part of the Integrated Characterization of Energy, Clouds, Atmospheric State, and Precipitation at Summit (ICECAPS) project. During the first 20 months in operation, this complementary suite of active and passive ground-based sensors and radiosondes has provided new and unique perspectives on important cloud–atmosphere properties.

High atop the GIS, the atmosphere is extremely dry and cold with strong near-surface static stability predominating throughout the year, particularly in winter. This low-level thermodynamic structure, coupled with frequent moisture inversions, conveys the importance of advection for local cloud and precipitation formation. Cloud liquid water is observed in all months of the year, even the particularly cold and dry winter, while annual cycle observations indicate that the largest atmospheric moisture amounts, cloud water contents, and snowfall occur in summer and under southwesterly flow. Many of the basic structural properties of clouds observed at Summit, Greenland, particularly for low-level stratiform clouds, are similar to their counterparts in other Arctic regions.

The ICECAPS observations and accompanying analyses will be used to improve the understanding of key cloud–atmosphere processes and the manner in which they interact with the GIS. Furthermore, they will facilitate model evaluation and development in this data-sparse but environmentally unique region.

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Vinia Mattioli
,
Christophe Accadia
,
Catherine Prigent
,
Susanne Crewell
,
Alan Geer
,
Patrick Eriksson
,
Stuart Fox
,
Juan R. Pardo
,
Eli J. Mlawer
,
Maria Cadeddu
,
Michael Bremer
,
Carlos De Breuck
,
Alain Smette
,
Domenico Cimini
,
Emma Turner
,
Mario Mech
,
Frank S. Marzano
,
Pascal Brunel
,
Jerome Vidot
,
Ralf Bennartz
,
Tobias Wehr
,
Sabatino Di Michele
, and
Viju O. John
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