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dc.contributor.authorDe Souza-Machado, Sergio
dc.contributor.authorTangborn, Andrew
dc.contributor.authorSura, Philip
dc.contributor.authorHepplewhite, Christopher
dc.contributor.authorStrow, L. Larrabee
dc.date.accessioned2018-09-19T20:05:20Z
dc.date.available2018-09-19T20:05:20Z
dc.date.issued2017-05-17
dc.description© Copyright 17 May 2017, American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC §108) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a website or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. All AMS journals and monograph publications are registered with the Copyright Clearance Center (http://www.copyright.com). Questions about permission to use materials for which AMS holds the copyright can also be directed to permissions@ametsoc.org. Additional details are provided in the AMS Copyright Policy statement, available on the AMS website (http://www.ametsoc.org/CopyrightInformation).en_US
dc.description.abstractStatistical relationships between higher-order moments of probability density functions (PDFs) are used to analyze top-of-atmosphere radiance measurements made by the Atmospheric Infrared Sounder (AIRS) and radiance calculations from the ECMWF Re-Analysis (ERA) and the Modern-Era Retrospective Analysis for Research and Applications (MERRA) over a 10-yr period. The statistical analysis used in this paper has previously been applied to sea surface temperature, and here the authors show that direct satellite radiance observations of atmospheric variability also exhibit stochastic forcing characteristics. The authors have chosen six different AIRS channels based on the sensitivity of their measured radiances to a variety of geophysical properties. In each of these channels, the authors have found evidence of correlated additive and multiplicative (CAM) stochastic forcing. In general, channels sensitive to tropospheric humidity and surface temperature show the strongest evidence of CAM forcing, while those sensitive to stratospheric temperature and ozone exhibit the weakest forcing. Radiance calculations from ERA and MERRA agree well with AIRS measurements in the Gaussian part of the PDFs but show some differences in the tails, indicating that the reanalyses may be missing some extrema there. The CAM forcing is investigated through numerical simulation of simple stochastic differential equations (SDEs). The authors show how measurements agree better with weaker CAM forcing, achieved by reducing the multiplicative forcing or by increasing the spatial correlation of the added noise in the case of an SDE with one spatial dimension. This indicates that atmospheric models could be improved by adjusting nonlinear terms that couple long and short time scales.en_US
dc.description.sponsorshipThis work was funded by NASA ROSES Grant NNX14AK58G: Climate studies using AIRS, radiative transfer and spectroscopy. We also acknowledge the High Performance Computing Facility (HPCF) at UMBC, where the computations were carried out. Comments from the three reviewers greatly helped to improve the quality of this paper.en_US
dc.format.extent19 pagesen_US
dc.genrejournal articleen_US
dc.identifierdoi:10.13016/M2G15TF5D
dc.identifier.citationSergio De Souza-Machado, Andrew Tangborn, Philip Sura, Christopher Hepplewhite, L. Larrabee Strow, Non-Gaussian Analysis of Observations from the Atmospheric Infrared Sounder Compared with ERA and MERRA Reanalyses, Journal of Applied Meteorology and Climatology, Volume 57 No. 10, 2018, https://doi.org/10.1175/JAMC-D-16-0278.1en_US
dc.identifier.urihttps://doi.org/10.1175/JAMC-D-16-0278.1
dc.identifier.urihttp://hdl.handle.net/11603/11319
dc.language.isoen_USen_US
dc.publisherAmerican Meteorological Society (AMS)en_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Physics Department
dc.rightsThis item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.
dc.subjectHigh Performance Computing Facilty (HPCF)en_US
dc.subjecthigher-order moments of probability density functions (PDFs)
dc.subjecttop-of-atmosphere radiance measurements
dc.subjectAtmospheric Infrared Sounder (AIRS)
dc.subjectradiance calculations from the ECMWF Re-Analysis (ERA)
dc.subjectModern-Era Retrospective Analysis for Research and Applications (MERRA)
dc.subjectdirect satellite radiance observations of atmospheric variability
dc.subjectstochastic forcing characteristics
dc.subjectcorrelated additive and multiplicative (CAM) stochastic forcing
dc.subjectImproving atmospheric models
dc.titleNon-Gaussian Analysis of Observations from the Atmospheric Infrared Sounder Compared with ERA and MERRA Reanalysesen_US
dc.typeTexten_US


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