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Gelaro, Ronald; Zhu, Yanqiu (2009)
Publisher: Co-Action Publishing
Journal: Tellus A
Languages: English
Types: Article
Subjects:
With the adjoint of a data assimilation system, the impact of any or all assimilated observations on measures of forecast skill can be estimated accurately and efficiently. The approach allows aggregation of results in terms of individual data types, channels or locations, all computed simultaneously. In this study, adjoint-based estimates of observation impact are compared with results from standard observing system experiments (OSEs) using forward and adjoint versions of the NASA GEOS-5 atmospheric data assimilation system. Despite important underlying differences in the way observation impacts are measured in the two approaches, the results show that they provide consistent estimates of the overall impact of most of the major observing systems in reducing a dry total-energy metric of 24-h forecast error over the globe and extratropics and, to a lesser extent, over the tropics. Just as importantly, however, it is argued that the two approaches provide unique, but complementary, information about the impact of observations on numerical weather forecasts. Moreover, when used together, they reveal both redundancies and dependencies between observing system impacts as observations are added or removed from the data assimilation system. Understanding these dependencies appears to pose an important challenge in making optimal use of the global observing system for numerical weather prediction.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Baker, N. and Daley, R. 2000. Observation and background adjoint sensitivity in the adaptive observation-targeting problem. Quart. J. R. Meteorol. Soc. 126, 1431-1454.
    • English, S., Saunders, R., Candy, B., Forsythe, M. and Collard, A. 2004. Met Office satellite data OSEs. In: Proceedings of Third WMO Workshop on the Impact of Various Observing Systems on Numerical Weather Prediction, Alpbach, Austria, WMO/TD No. 1228, 146-156.
    • Errico, R. M. 2000. Interpretations of the total energy and rotational energy norms applied to determination of singular vectors. Quart. J. R. Meteorol. Soc. 126, 1581-1599.
    • Errico, R. M. 2007. Interpretation of an adjoint-derived observational impact measure. Tellus 59A, 273-276.
    • Gelaro, R., Rosmond, T. E. and Daley, R. 2002. Singular vector calculations with an analysis error variance metric. Mon. Wea. Rev. 130, 1166-1186.
    • Gelaro, R., Zhu, Y. and Errico, R. M. 2007. Examination of various-order adjoint-based approximations of observation impact. Meteorologische Zeitschrift 16, 685-692.
    • Kelly, G., McNally, T., The´paut, J.-N. and Szyndel, M. 2004. OSEs of all main data types in the ECMWF operation system. In: Proceedings of Third WMO Workshop on the Impact of Various Observing Systems on Numerical Weather Prediction, Alpbach, Austria, WMO/TD No. 1228, 63-94.
    • Langland, R. H. and Baker, N. 2004. Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system. Tellus 56A, 189-201.
    • Le Marshall, J., Jung, J., Derber, J., Chahine, M., Treadon, R. and coauthors. 2006. Improving global analysis and forecasting with AIRS. Bull. Am. Meteorol. Soc. 87, 891-894.
    • Lin, S.-J. 2004. A vertically Lagrangian finite-volume dynamical core for global models. Mon. Wea. Rev. 132, 2293-2307.
    • Lord, S., Zapotocny, T. and Jung, J. 2004. Observing system experiments with NCEP's global forecast system. In: Proceedings of Third WMO Workshop on the Impact of Various Observing Systems on Numerical Weather Prediction, Alpbach, Austria, WMO/TD No. 1228, 56-62.
    • Marseille, G.-J., Stoffelen, A. and Barkmeijer, J. 2008. Sensitivity Observing System Experiment (SOSE)-a new effective NWP-based tool in designing the global observing system. Tellus 60A, 216- 233.
    • Orrell, D., Smith, L., Barkmeijer, J. and Palmer, T. N. 2001. Model error in weather forecasting. Nonlinear Processes Geophys. 8, 357-371.
    • Rabier, F., Klinker, E., Courtier, P. and Hollingsworth, A. 1996. Sensitivity of forecast errors to initial conditions. Quart. J. R. Meteorol. Soc. 122, 121-150.
    • Reynolds, C. A. and Gelaro, R. 2001. Remarks on Northern Hemisphere forecast error sensitivity from 1996-2000. Mon. Wea. Rev. 129, 2145- 2153.
    • Rienecker, M. M., Suarez, M. J., Todling, R., Bacmeister, J., Takacs, L. and co-authors. 2007. The GEOS-5 Data Assimilation Systemdocumentation of versions 5.0.1 and 5.1.0. NASA TM 104606, Technical Report Series on Global Modeling and Data Assimilation Volume 27.
    • Talagrand, O. 1981. A study of the dynamics of four-dimensional data assimilation. Tellus 33, 43-60.
    • Tre´molet, Y. 2008. Computation of observation sensitivity and observation impact in incremental variational data assimilation. Tellus 60A, 964-978.
    • Wu, W., Purser, R. J. and Parrish, D. F. 2002. Three dimensional variational analysis with spatially inhomogeneous covariances. Mon. Wea. Rev. 130, 2905-2916.
    • Z˘agar, N., Stoffelen, A., Marseille, G.-J., Accadia, C. and Schlu¨ssel, P. 2008. Impact assessment of simulated Doppler wind lidars with a multivariate variational assimilation in the tropics. Mon. Wea. Rev. 136, 2443-2460.
    • Zhu, Y. and Gelaro, R. 2008. Observation sensitivity calculations using the adjoint of the Gridpoint Statistical Interpolation (GSI) analysis system. Mon. Wea. Rev. 136, 335-351.
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