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Evaluation of Radiative Transfer Models With Clouds

Aumann, Hartmut H. and Chen, Xiuhong and Fishbein, Evan and Geer, Alan and Havemann, Stephan and Huang, Xianglei and Liu, Xu and Liuzzi, Giuliano and DeSouza-Machado, Sergio and Manning, Evan M. and Masiello, Guido and Matricardi, Marco and Moradi, Isaac and Natraj, Vijay and Serio, Carmine and Strow, Larrabee and Vidot, Jerome and Wilson, R. Chris and Wu, Wan and Yang, Qiguang and Yung, Yuk L. (2018) Evaluation of Radiative Transfer Models With Clouds. Journal of Geophysical Research. Atmospheres, 123 (11). pp. 6142-6157. ISSN 2169-897X. http://resolver.caltech.edu/CaltechAUTHORS:20181127-160003740

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Abstract

Data from hyperspectral infrared sounders are routinely ingested worldwide by the National Weather Centers. The cloud‐free fraction of this data is used for initializing forecasts which include temperature, water vapor, water cloud, and ice cloud profiles on a global grid. Although the data from these sounders are sensitive to the vertical distribution of ice and liquid water in clouds, this information is not fully utilized. In the future, this information could be used for validating clouds in National Weather Center models and for initializing forecasts. We evaluate how well the calculated radiances from hyperspectral Radiative Transfer Models (RTMs) compare to cloudy radiances observed by AIRS and to one another. Vertical profiles of the clouds, temperature, and water vapor from the European Center for Medium‐Range Weather Forecasting were used as input for the RTMs. For nonfrozen ocean day and night data, the histograms derived from the calculations by several RTMs at 900 cm^(−1) have a better than 0.95 correlation with the histogram derived from the AIRS observations, with a bias relative to AIRS of typically less than 2 K. Differences in the cloud physics and cloud overlap assumptions result in little bias between the RTMs, but the standard deviation of the differences ranges from 6 to 12 K. Results at 2,616 cm^(−1) at night are reasonably consistent with results at 900 cm^(−1). Except for RTMs which use full scattering calculations, the bias and histogram correlations at 2,616 cm^(−1) are inferior to those at 900 cm^(−1) for daytime calculations.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1029/2017jd028063DOIArticle
ORCID:
AuthorORCID
Aumann, Hartmut H.0000-0002-4311-7546
Havemann, Stephan0000-0002-3259-091X
Huang, Xianglei0000-0002-7129-614X
Liu, Xu0000-0002-0473-3143
Liuzzi, Giuliano0000-0003-3638-5750
Masiello, Guido0000-0002-7986-8296
Matricardi, Marco0000-0001-7514-9473
Moradi, Isaac0000-0003-2194-1427
Natraj, Vijay0000-0003-3154-9429
Serio, Carmine0000-0002-5931-7681
Strow, Larrabee0000-0001-5999-3519
Additional Information:© 2018. American Geophysical Union. Received 14 NOV 2017. Accepted 4 APR 2018. Accepted article online 16 APR 2018. Published online 13 JUN 2018. The work described in this paper was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Work at JPL and UMBC was funded by NASA ROSES, with the longtime support of Ramesh Kakar of NASA Headquarters. Steve Broberg helped with technical editing. The work at LARC was supported by NASA NAST‐I and CLARREO projects. The work at NASA/GMAO was funded under NASA grant NNX17AE79A Goddard Space Flight Center Cooperative Agreement. The work at the University of Michigan was supported by NASA grant NNX15AC25G. The work at Meteo France was supported by the EUMETSAT NWP‐SAF program. The work at the UK Met Office was supported as part of the science program theme “Improved use of Satellite Data”. The 7,377 atmospheric states and associated AIRS spectra used for this paper can be found at ftp://thunder.jpl.nasa.gov/hha/Cloudy_RTA. The file readme.20160518.txt defines various parameters. The file AIRS_SRF_m140f.mat defines the AIRS SRF for each of the 2,378 channels in a MATLAB Version 7.0 format.
Funders:
Funding AgencyGrant Number
NASA/JPL/CaltechUNSPECIFIED
NASANNX17AE79A
NASANNX15AC25G
EUMETSAT NWP‐SAFUNSPECIFIED
Meteorological Office (UK)UNSPECIFIED
Subject Keywords:infrared; hyperspectral; cloud; radiative transfer; weather forecasting; climate
Record Number:CaltechAUTHORS:20181127-160003740
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20181127-160003740
Official Citation:Aumann, H. H., Chen, X., Fishbein, E., Geer, A., Havemann, S., Huang, X., et al. (2018). Evaluation of radiative transfer models with clouds. Journal of Geophysical Research: Atmospheres, 123, 6142–6157. https://doi.org/10.1029/2017JD028063
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:91260
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:28 Nov 2018 19:09
Last Modified:02 Jan 2019 21:13

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