Abstract

AbstractAn algorithm that allows an automatic analysis of cirrus properties from Advanced Very High Resolution Radiometer (AVHRR) observations is presented. Further investigations of the information content and physical meaning of the brightness temperature differences (BTD) between channels 4 (11 μm) and 5 (12 μm) of the radiometer have led to the development of an automatic procedure to provide global estimates both of the cirrus cloud temperature and of the ratio of the equivalent absorption coefficients in the two channels, accounting for scattering effects. The ratio is useful since its variations are related to differences in microphysical properties. Assuming that cirrus clouds are composed of ice spheres, the effective diameter of the particle size distribution can be deduced from this microphysical index.The automatic procedure includes first, a cloud classification and a selection of the pixels corresponding to the envelope of the BTD diagram observed at a scale of typically 100 × 100 pixels. The classification, which uses dynamic cluster analysis, takes into account spectral and spatial properties of the AVHRR pixels. The selection is made through a series of tests, which also guarantees that the BTD diagram contains the necessary information, such as the presence of both cirrus-free pixels and pixels totally covered by opaque cirrus in the same area. Finally, the cloud temperature and the equivalent absorption coefficient ratio are found by fitting the envelope of the BTD diagram with a theoretical curve. Note that the method leads to the retrieval of the maximum value of the equivalent absorption coefficient ratio in the scene under consideration. This, in turn, corresponds to the minimum value of the effective diameter of the size distribution of equivalent Mie particles.The automatic analysis has been applied to a series of 21 AVHRR images acquired during the International Cirrus Experiment (ICE’89). Although the dataset is obviously much too limited to draw any conclusion at the global scale, it is large enough to permit derivation of cirrus properties that are statistically representative of the cirrus systems contained therein. The authors found that on average, the maximum equivalent absorption coefficient ratio increases with the cloud-top temperature with a jump between 235 and 240 K. More precisely, for cloud temperatures warmer than 235 K, the retrieved equivalent absorption coefficient ratio sometimes corresponds to very small equivalent spheres (diameter smaller than 20 μm). This is never observed for lower cloud temperatures. This change in cirrus microphysical properties points out that ice crystal habits may vary from one temperature regime toanother. It may be attributed to a modification of the size and/or shape of the particles.

Highlights

  • Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/ JCLI-D-17-0277.s1.Corresponding author: Dr Zhibo Zhang, zhibo.zhang@umbc. edu Marine boundary layer (MBL) clouds cover about 1/5 of the ocean surface and serve as a strong modulator of the radiative energy budget of the earth–atmosphere system (Klein and Hartmann 1993; Wood 2012)

  • We compare the COSP–CloudSat simulations based on modeled cloud fields (i.e., CAM5–Base and CAM5–Cloud Layers Unified by Binormals (CLUBB)) with the CloudSat products, with special attention on the warm rain process

  • This study presents a comprehensive evaluation of the simulated MBL cloud properties in two CAM runs: CAM5–Base and CAM5–CLUBB

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Summary

15 MARCH 2018

An Evaluation of Marine Boundary Layer Cloud Property Simulations in the Community Atmosphere Model Using Satellite Observations: Conventional Subgrid Parameterization versus CLUBB. Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, Maryland. Joint Center for Earth Systems Technology, and Physics Department, University of Maryland, Baltimore County, Baltimore, Maryland. Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington

Introduction
Evaluation of warm rain simulation
Summary and outlook
Full Text
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