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    GOES ABI Detection of Thin Cirrus over Land

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    1520-0426-JTECH-D-21-0160.1.pdf
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    Author
    McHardy, T.M.
    Campbell, J.R.
    Peterson, D.A.
    Lolli, S.
    Garnier, A.
    Kuciauskas, A.P.
    Surratt, M.L.
    Marquis, J.W.
    Miller, S.D.
    Dolinar, E.K.
    Dong, X.
    Show allShow less
    Affiliation
    Department of Hydrology and Atmospheric Sciences, University of Arizona
    Issue Date
    2022
    Keywords
    Cirrus clouds
    Cloud retrieval
    Remote sensing
    
    Metadata
    Show full item record
    Publisher
    American Meteorological Society
    Citation
    McHardy, T. M., Campbell, J. R., Peterson, D. A., Lolli, S., Garnier, A., Kuciauskas, A. P., Surratt, M. L., Marquis, J. W., Miller, S. D., Dolinar, E. K., & Dong, X. (2022). GOES ABI Detection of Thin Cirrus over Land. Journal of Atmospheric and Oceanic Technology, 39(9), 1415–1429.
    Journal
    Journal of Atmospheric and Oceanic Technology
    Rights
    Copyright © 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    This study develops a new thin cirrus detection algorithm applicable to overland scenes. The methodology builds from a previously developed overwater algorithm, which makes use of the Geostationary Operational Environmental Satellite 16 (GOES-16) Advanced Baseline Imager (ABI) channel 4 radiance (1.378-μm “cirrus” band). Calibration of this algorithm is based on coincident Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) cloud profiles. Emphasis is placed on rejection of false detections that are more common in overland scenes. Clear-sky false alarm rates over land are examined as a function of precipitable water vapor (PWV), showing that nearly all pixels having a PWV of <0.4 cm produce false alarms. Enforcing an above-cloud PWV minimum threshold of ∼1 cm ensures that most low-/midlevel clouds are not misclassified as cirrus by the algorithm. Pixel-filtering based on the total column PWV and the PWV for a layer between the top of the atmosphere (TOA) and a predetermined altitude H removes significant land surface and low-/midlevel cloud false alarms from the overall sample while preserving over 80% of valid cirrus pixels. Additionally, the use of an aggressive PWV layer threshold preferentially removes noncirrus pixels such that the remaining sample is composed of nearly 70% cirrus pixels, at the cost of a much-reduced overall sample size. This study shows that lower-tropospheric clouds are a much more significant source of uncertainty in cirrus detection than the land surface. © 2022 American Meteorological Society.
    Note
    6 month embargo; online publication: 21 September 2022
    ISSN
    0739-0572
    DOI
    10.1175/JTECH-D-21-0160.1
    Version
    Final published version
    ae974a485f413a2113503eed53cd6c53
    10.1175/JTECH-D-21-0160.1
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    UA Faculty Publications

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