Use of spectral cloud emissivities and their related uncertainties to infer ice cloud boundaries: methodology and assessment using CALIPSO cloud products
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Published:2019-09-19
Issue:9
Volume:12
Page:5039-5054
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ISSN:1867-8548
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Container-title:Atmospheric Measurement Techniques
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language:en
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Short-container-title:Atmos. Meas. Tech.
Author:
Kim Hye-Sil, Baum Bryan A.ORCID, Choi Yong-Sang
Abstract
Abstract. Satellite-imager-based operational cloud property
retrievals generally assume that a cloudy pixel can be treated as being
plane-parallel with horizontally homogeneous properties. This assumption can
lead to high uncertainties in cloud heights, particularly for the case of
optically thin, but geometrically thick, clouds composed of ice particles.
This study demonstrates that ice cloud emissivity uncertainties can be used
to provide a reasonable range of ice cloud layer boundaries, i.e., the
minimum to maximum heights. Here ice cloud emissivity uncertainties are
obtained for three IR channels centered at 11, 12, and 13.3 µm. The
range of cloud emissivities is used to infer a range of ice cloud
temperature and heights, rather than a single value per pixel as provided by
operational cloud retrievals. Our methodology is tested using MODIS
observations over the western North Pacific Ocean during August 2015. We
estimate minimum–maximum heights for three cloud regimes, i.e.,
single-layered optically thin ice clouds, single-layered optically thick
ice clouds, and multilayered clouds. Our results are assessed through
comparison with CALIOP version 4 cloud products for a total of 11873 pixels.
The cloud boundary heights for single-layered optically thin clouds show
good agreement with those from CALIOP; biases for maximum (minimum) heights
versus the cloud-top (base) heights of CALIOP are 0.13 km (−1.01 km). For
optically thick and multilayered clouds, the biases of the estimated cloud
heights from the cloud top or cloud base become larger (0.30/−1.71 km, 1.41/−4.64 km). The vertically resolved boundaries for ice clouds can contribute new
information for data assimilation efforts for weather prediction and
radiation budget studies. Our method is applicable to measurements provided
by most geostationary weather satellites including the GK-2A advanced
multichannel infrared imager.
Publisher
Copernicus GmbH
Subject
Atmospheric Science
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