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A Framework Based on 2-D Taylor Expansion for Quantifying the Impacts of Subpixel Reflectance Variance and Covariance on Cloud Optical Thickness and Effective Radius Retrievals Based on the Bispectral MethodThe bispectral method retrieves cloud optical thickness (τ) and cloud droplet effective radius (re) simultaneously from a pair of cloud reflectance observations, one in a visible or near-infrared (VIS/NIR) band and the other in a shortwave infrared (SWIR) band. A cloudy pixel is usually assumed to be horizontally homogeneous in the retrieval. Ignoring subpixel variations of cloud reflectances can lead to a significant bias in the retrieved τ and re. In the literature, the retrievals of τ and re are often assumed to be independent and considered separately when investigating the impact of subpixel cloud reflectance variations on the bispectral method. As a result, the impact on τ is contributed only by the subpixel variation of VIS/NIR band reflectance and the impact on re only by the subpixel variation of SWIR band reflectance. In our new framework, we use the Taylor expansion of a two-variable function to understand and quantify the impacts of subpixel variances of VIS/NIR and SWIR cloud reflectances and their covariance on the τ and re retrievals. This framework takes into account the fact that the retrievals are determined by both VIS/NIR and SWIR band observations in a mutually dependent way. In comparison with previous studies, it provides a more comprehensive understanding of how subpixel cloud reflectance variations impact the τ and re retrievals based on the bispectral method. In particular, our framework provides a mathematical explanation of how the subpixel variation in VIS/NIR band influences the re retrieval and why it can sometimes outweigh the influence of variations in the SWIR band and dominate the error in re retrievals, leading to a potential contribution of positive bias to the re retrieval. We test our framework using synthetic cloud fields from a large-eddy simulation and real observations from Moderate Resolution Imaging Spectroradiometer. The predicted results based on our framework agree very well with the numerical simulations. Our framework can be used to estimate the retrieval uncertainty from subpixel reflectance variations in operational satellite cloud products and to help understand the differences in τ and re retrievals between two instruments.
Document ID
20160009146
Acquisition Source
Goddard Space Flight Center
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Zhang, Z.
(Maryland Univ. Baltimore County Baltimore, MD, United States)
Werner, F.
(Maryland Univ. Baltimore County Baltimore, MD, United States)
Cho, H.-M.
(Maryland Univ. Baltimore County Baltimore, MD, United States)
Wind, G.
(Science Systems and Applications, Inc. Greenbelt, MD, United States)
Platnick, S.
(NASA Goddard Space Flight Center Greenbelt, MD United States)
Ackerman, A. S.
(NASA Goddard Inst. for Space Studies New York, NY, United States)
Di Girolamo, L.
(Illinois Univ. at Urbana-Champaign Urbana, IL, United States)
Marshak, A.
(NASA Goddard Space Flight Center Greenbelt, MD United States)
Meyer, K.
(Universities Space Research Association Columbia, MD, United States)
Date Acquired
July 19, 2016
Publication Date
June 18, 2016
Publication Information
Publication: Journal of Geophysical Research: Atmospheres
Publisher: Wiley
Volume: 121
Issue: 12
Subject Category
Meteorology And Climatology
Report/Patent Number
GSFC-E-DAA-TN33749
Funding Number(s)
CONTRACT_GRANT: NNX15AC77G
CONTRACT_GRANT: NNX14AJ25G
CONTRACT_GRANT: NNG12HP08C
CONTRACT_GRANT: NNG11HP16A
Distribution Limits
Public
Copyright
Other
Keywords
reflectance
clouds (meteorology)
optical thickness
bias

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