Leveraging Highly Accurate Data in Diagnosing Errors in Atmospheric Models

Stephen S. Leroy Harvard School of Engineering and Applied Sciences, Cambridge, Massachusetts

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Mark J. Rodwell European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

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Highly accurate data can serve the numerical weather prediction, climate prediction, and atmospheric reanalysis communities by better enabling the diagnosis of model error through the careful examination of the diagnostics of data assimilation, especially the firstguess departures and the analysis increments. The highly accurate data require no bias correction for instrument error, leaving the possibility of confusion with error in forward models for observations as the lone hindrance to the diagnosis of model error. With this scenario in mind, we conducted numerical experiments to investigate the potential confusion using the data assimilation system at the European Centre for Medium-Range Weather Forecasts. We found that large-scale systematic model error can be misattributed to error in the forward models for observations, thereby reducing systematic firstguess departures and impeding the mitigation of model error. The same large-scale model error generated a 20% increase in analyzed specific humidity near the tropopause, suggesting that current observational data cannot constrain the upper tropospheric humidity in current models, which contributes substantially to greenhouse forcing of the climate. We expect that the confusion of model error for an error in the forward models for observations occurs regardless of the objective method used to diagnose model error.

Our findings underline the importance for continued improvement in radiative transfer calculations and highlight the value of multiple sources of accurate data that are redundant in their sensitivity to atmospheric variables yet orthogonal in their radiation physics.

CORRESPONDING AUTHOR: Stephen S. Leroy, Anderson Group, 12 Oxford St., Link Building, Cambridge, MA 02138 E-mail: leroy@huarp.harvard.edu

Highly accurate data can serve the numerical weather prediction, climate prediction, and atmospheric reanalysis communities by better enabling the diagnosis of model error through the careful examination of the diagnostics of data assimilation, especially the firstguess departures and the analysis increments. The highly accurate data require no bias correction for instrument error, leaving the possibility of confusion with error in forward models for observations as the lone hindrance to the diagnosis of model error. With this scenario in mind, we conducted numerical experiments to investigate the potential confusion using the data assimilation system at the European Centre for Medium-Range Weather Forecasts. We found that large-scale systematic model error can be misattributed to error in the forward models for observations, thereby reducing systematic firstguess departures and impeding the mitigation of model error. The same large-scale model error generated a 20% increase in analyzed specific humidity near the tropopause, suggesting that current observational data cannot constrain the upper tropospheric humidity in current models, which contributes substantially to greenhouse forcing of the climate. We expect that the confusion of model error for an error in the forward models for observations occurs regardless of the objective method used to diagnose model error.

Our findings underline the importance for continued improvement in radiative transfer calculations and highlight the value of multiple sources of accurate data that are redundant in their sensitivity to atmospheric variables yet orthogonal in their radiation physics.

CORRESPONDING AUTHOR: Stephen S. Leroy, Anderson Group, 12 Oxford St., Link Building, Cambridge, MA 02138 E-mail: leroy@huarp.harvard.edu
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  • Anderson, J. G., J. A. Dykema, R. M. Goody, H. Hu, and D. B. Kirk-Davidoff, 2004: Absolute, spectrally-resolved, thermal radiance: A benchmark for climate monitoring from space. J. Quant. Spectrosc. Radiat. Transfer, 85, 367383, doi:10.1016/S0022-4073(03)00232-2.

    • Search Google Scholar
    • Export Citation
  • Auligné, T., A. P. McNally, and D. P. Dee, 2007: Adaptive bias correction for satellite data in a numerical weather prediction system. Quart. J. Roy. Meteor. Soc., 133, 631642, doi:10.1002/qj.56.

    • Search Google Scholar
    • Export Citation
  • Brown, A., S. Milton, M. Cullen, B. Golding, J. Mitchell, and A. Shelly, 2012: Unified modeling and prediction of weather and climate. Bull. Amer. Meteor. Soc., 93, 18651877, doi:10.1175/BAMS-D-12-00018.1.

    • Search Google Scholar
    • Export Citation
  • Dee, D. P., 2005: Bias and data assimilation. Quart. J. Roy. Meteor. Soc., 131, 33233343, doi:10.1256/qj.05.137.

  • Dee, D. P., and S. Uppala, 2009: Variational bias correction of satellite radiance data in the ERA-Interim reanalysis. Quart. J. Roy. Meteor. Soc., 135, 18301841, doi:10.1002/qj.493.

    • Search Google Scholar
    • Export Citation
  • Derber, J. C., and W.-S. Wu, 1998: The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon. Wea. Rev., 126, 22872299, doi:10.1175/1520-0493(1998)1262.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Healy, S. B., and J.-N. Thépault, 2006: Assimilation experiments with CHAMP GPS radio occultation measurements. Quart. J. Roy. Meteor. Soc., 132, 605623, doi:10.1256/qj.04.182.

    • Search Google Scholar
    • Export Citation
  • Klinker, E., and P. D. Sardeshmukh, 1992: The diagnostics of mechanical dissipation in the atmosphere from large-scale balance requirements. J. Atmos. Sci., 49, 608627, doi:10.1175/1520-0469(1992)0492.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kursinski, E. R., G. A. Hajj, J. T. Schofield, R. P. Linfield, and K. R. Hardy, 1997: Observing Earth's atmosphere with radio occultation measurements using the global positioning system. J. Geophys. Res., 102 (D10), 23 42923 465, doi:10.1029/97JD01569.

    • Search Google Scholar
    • Export Citation
  • Lorenc, A. C., 1986: Analysis methods for numerical weather prediction. Quart. J. Roy. Meteor. Soc., 112, 11771194, doi:10.1002/qj.49711247414.

    • Search Google Scholar
    • Export Citation
  • Poli, P., S. B. Healy, and D. P. Dee, 2010: Assimilation of global positioning system radio occultation data in the ECMWF ERA-Interim reanalysis. Quart. J. Roy. Meteor. Soc., 136, 19721990, doi:10.1002/qj.722.

    • Search Google Scholar
    • Export Citation
  • Pulido, M., S. Polavarapu, T. G. Shepherd, and J. Thuburn, 2012: Estimation of optimal gravity wave parameters for climate models using data assimilation. Quart. J. Roy. Meteor. Soc., 138, 298309, doi:10.1002/qj.932.

    • Search Google Scholar
    • Export Citation
  • Rodwell, J. J., and T. Jung, 2008: Understanding the local and global impacts of model physics changes: An aerosol example. Quart. J. Roy. Meteor. Soc., 134, 14791497, doi:10.1002/qj.298.

    • Search Google Scholar
    • Export Citation
  • Rodwell, M. J., and T. N. Palmer, 2007: Using numerical weather prediction to assess climate models. Quart. J. Roy. Meteor. Soc., 133, 129146, doi:10.1002/qj.23.

    • Search Google Scholar
    • Export Citation
  • Talagrand, O., 1997: Assimilation of observations, an introduction. J. Meteor. Soc. Japan, 75, 191209.

  • Wielicki, B.A., and Coauthors, 2013: Achieving climate change absolute accuracy in orbit. Bull. Amer. Meteor. Soc., 94, 15191539, doi:10.1175/BAMS-D-12-00149.1.

    • Search Google Scholar
    • Export Citation
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