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Journal of Applied Meteorology and Climatology

  • Aumann, H. H., and Coauthors, 2003: AIRS/AMSU/HSB on the Aqua mission: Design, science objectives, data products, and processing systems. IEEE Trans. Geosci. Remote Sens., 41, 253–264, doi:10.1109/TGRS.2002.808356.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bloom, H. J., 2001: The Cross-Track Infrared Sounder (CrIS): A sensor for operational meteorological remote sensing. Proc. IEEE Int. Geoscience and Remote Sensing Symp., Sydney, Australia, Institute of Electrical and Electronics Engineers, 1341–1343, doi:10.1109/IGARSS.2001.976838.

    • Crossref
    • Export Citation

  • Brindley, H., R. Bantges, J. Russell, J. Murray, C. Dancel, C. Belotti, and J. Harries, 2015: Spectral signatures of Earth’s climate variability over 5 years from IASI. J. Climate, 28, 1649–1660, doi:10.1175/JCLI-D-14-00431.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Clerbaux, C., and Coauthors, 2007: The IASI/MetOp mission: First observations and highlights of its potential contribution to GMES. Space Res. Today, 168, 19–24, doi:10.1016/S0045-8732(07)80046-5.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, doi:10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • DeSouza-Machado, S., L. L. Strow, and S. E. Hannon, 1997: kCompressed atmospheric radiative transfer algorithm (kCARTA). Satellite Remote Sensing of Clouds and the Atmosphere II, J. D. Haigh, Ed., International Society for Optical Engineering (SPIE Proceedings, Vol. 3220), 156, doi:10.1117/12.301147.

    • Crossref
    • Export Citation

  • Efron, B., 1979: Bootstrap methods: Another look at the jackknife. Ann. Stat., 7, 1–26, doi:10.1214/aos/1176344552.

  • Frankignoul, C., and P. Müller, 1979: Quasi-geostrophic response of an infinite β-plane ocean to stochastic forcing by the atmosphere. J. Phys. Oceanogr., 9, 104–127, doi:10.1175/1520-0485(1979)009<0104:QGROAI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Goody, R. M., and Y. L. Yung, 1989: Atmospheric Radiation: Theoretical Basis. 2nd ed. Oxford University Press, 519 pp.

    • Crossref
    • Export Citation

  • Gottwald, G. A., K. Peters, and L. Davies, 2016: A data-driven method for the stochastic parametrisation of subgrid-scale tropical convective area fraction. Quart. J. Roy. Meteor. Soc., 142, 349–359, doi:10.1002/qj.2655.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Higham, D. J., 2001: An algorithmic introduction to numerical simulations of stochastic differential equations. SIAM Rev., 43, 525–546, doi:10.1137/S0036144500378302.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kloeden, P. E., and E. Platen, 1992: Numerical Solution of Stochastic Differential Equations. Springer, 636 pp., doi:10.1007/978-3-662-12616-5.

    • Crossref
    • Export Citation

  • Laing, A., and J.-L. Evans, 2011: Introduction to Tropical Meteorology. 2nd ed. University Corporation for Atmospheric Research. [Available online at www.goes-r.gov/users/comet/tropical/textbook_2nd_edition/index.htm.]

  • Leroy, S. S., J. G. Anderson, and G. Ohring, 2008: Climate signal detection times and constraints on climate benchmark accuracy requirements. J. Climate, 21, 841–846, doi:10.1175/2007JCLI1946.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Li, J., 2008: AIRS level-1B calibration subset product (AIRXBCAL). NASA GES DISC, accessed 14 April 2016. [Available online at http://disc.sci.gsfc.nasa.gov/AIRS/documentation/readmes/README.AIRXBCAL.pdf.]

  • Lin, S.-J., and R. B. Rood, 1997: An explicit flux-form semi-Lagrangian shallow-water model on the sphere. Quart. J. Roy. Meteor. Soc., 123, 2477–2498, doi:10.1002/qj.49712354416.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Liou, K. N., 2002: An Introduction to Atmospheric Radiation. Academic Press, 583 pp.

  • McClatchey, R. A., R. W. Fenn, J. E. A. Selby, F. E. Volz and J. S. Garing, 1972: Optical properties of the atmosphere. Air Force Cambridge Research Laboratories Tech. Rep. AFCRL-72-0497, 113 pp.

    • Crossref
    • Export Citation

  • McNally, A. P., P. D. Watts, J. A. Smith, R. Engelen, G. A. Kelly, J. N. Thepaut, and M. Matricardi, 2006: The assimilation of AIRS radiance data at ECMWF. Quart. J. Roy. Meteor. Soc., 132, 935–957, doi:10.1256/qj.04.171.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Mlawer, E. J., V. H. Payne, J.-L. Moncet, J. S. Delamere, M. J. Alvarado and D. C. Tobin, 2012: Development and recent evaluation of the MT_CKD model of continuum absorption. Philos. Trans. Roy. Soc. London, 370A, 2520–2556, doi:10.1098/rsta.2011.0295.

    • Search Google Scholar
    • Export Citation

  • Mohan, H., 1979: Molecules of significance in planetary aeronomy, NASA Rep. 1030, 318 pp.

  • Pan, F., X. L. Huang, L. L. Strow, and H. Guo, 2015: Linear trends and closures of 10-yr observations of AIRS stratospheric channels. J. Climate, 28, 8939–8950, doi:10.1175/JCLI-D-15-0418.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rienecker, M. M., and Coauthors, 2011: MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24, 3624–3648, doi:10.1175/JCLI-D-11-00015.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rothman, L. S., and Coauthors, 2009: The HITRAN 2008 molecular spectroscopic database, J. Quant. Spectrosc. Radiat. Transfer, 110, 533–572, doi:10.1016/j.jqsrt.2009.02.013.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sardeshmukh, P. D., and P. Sura, 2009: Reconciling non-Gaussian climate statistics with linear dynamics. J. Climate, 22, 1193–1207, doi:10.1175/2008JCLI2358.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Schreier, M. M., B. H. Kahn, K. Suselj, J. Karlsson, S. C. Ou, Q. Yue, and S. L. Nasiri, 2014: Atmospheric parameters in a subtropical cloud regime transition derived by AIRS and MODIS: Observed statistical variability compared to ERA-Interim. Atmos. Chem. Phys., 14, 3573–3587, doi:10.5194/acp-14-3573-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Strow, L. L., and D. Reuter, 1988: Effect of line mixing on atmospheric brightness temperatures near 15 μm. Appl. Opt., 27, 872–878, doi:10.1364/AO.27.000872.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Strow, L. L., S. E. Hannon, S. De Souza-Machado, H. E. Motteler, and D. Tobin, 2003: An overview of the AIRS radiative transfer model. IEEE Trans. Geosci. Remote Sens., 41, 303–313, doi:10.1109/TGRS.2002.808244.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Strow, L. L., S. E. Hannon, S. De Souza-Machado, H. E. Motteler, and D. Tobin, 2006: Validation of the Atmospheric Infrared Sounder radiative transfer algorithm. J. Geophys. Res., D09S06, doi:10.1029/2005JD006146.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sura, P., 2011: A general perspective of extreme events in weather and climate. Atmos. Res., 101, 1–21, doi:10.1016/j.atmosres.2011.01.012.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sura, P., 2013: Stochastic models of climate extremes: Theory and observations. Extremes in a Changing Climate: Detection, Analysis and Uncertainty, A. AghaKouchak et al., Eds., Water Science and Technology Library, Vol. 65, Springer, 181–222.

    • Crossref
    • Export Citation

  • Sura, P., and J. Barsugli, 2002: A note on estimating drift and diffusion parameters from time series. Phys. Lett., 305A, 304–311, doi:10.1016/S0375-9601(02)01474-3.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sura, P., and P. D. Sardeshmukh, 2008: A global view of non-Gaussian SST variability. J. Phys. Oceanogr., 38, 639–647, doi:10.1175/2007JPO3761.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sura, P., and A. Hannachi, 2015: Perspectives of non-Gaussianity in atmospheric synoptic and low-frequency variability. J. Climate, 28, 5091–5115, doi:10.1175/JCLI-D-14-00572.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Susskind, J., C. D. Barnet, and J. M. Blaisdell, 2003: Retrieval of atmospheric and surface parameters from AIRS/AMSU/HSB data in the presence of clouds. IEEE Trans. Geosci. Remote Sens., 41, 390–409, doi:10.1109/TGRS.2002.808236.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tangborn, A., 2004: Wavelet approximation in propagation of error covariance propagation in data assimilation. Tellus, 56A, 16–28, doi:10.1111/j.1600-0870.2004.00034.x.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tian, B., E. J. Fetzer, B. H. Kahn, J. Teixeira, E. Manning, and T. Hearty, 2013: Evaluating CMIP5 models using AIRS tropospheric air temperature and specific humidity climatology. J. Geophys. Res. Atmos., 118, 114–134, doi:10.1029/2012JD018607.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Toth, R. A., 1991: ν2 band of H2 16O: Line strengths and transition frequencies. J. Opt. Soc. Amer., 8B, 2236–2255, doi:10.1364/JOSAB.8.002236.

  • Waugh, D. W., and T. M. Hall, 2002: Age of stratospheric air: Theory, observations, and models. Rev. Geophys., 40, 1010, doi:10.1029/2000RG000101.

  • Wong, S., and J. Teixeira, 2016: Extreme convection and tropical climate variability: Scaling of cold brightness temperatures to sea surface temperature. J. Climate, 29, 3893–3905, doi:10.1175/JCLI-D-15-0214.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wu, W.-S., R. J. Purser, and D. F. Parrish, 2002: Three-dimensional variational analysis with spatially inhomogeneous covariances. Mon. Wea. Rev., 130, 2905–2916, doi:10.1175/1520-0493(2002)130<2905:TDVAWS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Article Type:
Research Article

Non-Gaussian Analysis of Observations from the Atmospheric Infrared Sounder Compared with ERA and MERRA Reanalyses

Sergio De Souza-MachadoJoint Center for Earth Technology, University of Maryland, Baltimore County, Baltimore, Maryland

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Andrew TangbornJoint Center for Earth Technology, University of Maryland, Baltimore County, Baltimore, Maryland

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Philip SuraDepartment of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, Florida

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Christopher HepplewhiteJoint Center for Earth Technology, University of Maryland, Baltimore County, Baltimore, Maryland

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L. Larrabee StrowPhysics Department, University of Maryland, Baltimore County, Baltimore, Maryland

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Print Publication:
01 May 2017
DOI:
https://doi.org/10.1175/JAMC-D-16-0278.1
Page(s):
1463–1481
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Abstract

Statistical 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.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Andrew Tangborn, tangborn@umbc.edu

Abstract

Statistical 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.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Andrew Tangborn, tangborn@umbc.edu
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