• Bergman, J. W., , H. Hendon, , and K. Weickman, 2001: Intraseasonal air–sea interactions at the onset of El Niño. J. Climate, 14 , 17021719.

    • Search Google Scholar
    • Export Citation
  • Hasselmann, K., 1988: PIPs and POPs—A general formalism for the reduction of dynamical systems in terms of principal interaction patterns and principal oscillation patterns. J. Geophys. Res, 93 , 11 01511 020.

    • Search Google Scholar
    • Export Citation
  • McPhaden, M. J., 1999: Genesis and evolution of the 1997–1998 El Niño. Science, 283 , 950954.

  • Penland, C., 1989: Random forcing and forecasting using principal oscillation pattern analysis. Mon. Wea. Rev, 117 , 21652185.

  • ——,. 1996: A stochastic model of IndoPacific sea surface temperature anomalies. Physica D, 96 , 534558.

  • ——, and Magorian, T., 1993: Prediction of Niño 3 sea surface temperature anomalies using linear inverse modeling. J. Climate, 6 , 10671076.

    • Search Google Scholar
    • Export Citation
  • ——, and ——,. 1994: A balance condition for stochastic numerical models with application to the El Niño–Southern Oscillation. J. Climate, 7 , 13521372.

    • Search Google Scholar
    • Export Citation
  • ——, and Sardeshmukh, P. D., 1995a: Error and sensitivity of geophysical eigensystems. J. Climate, 8 , 19881998.

  • ——, and ——,. 1995b: The optimal growth of tropical sea surface temperature anomalies. J. Climate, 8 , 19992024.

  • ——, Matrosova, L., , K. Weickmann, , and C. Smith, 2000: Forecast of tropical SSTs using linear inverse modeling (LIM). Experimental Long-Lead Forecast Bulletin, Center for Ocean–Land–Atmosphere Studies, Vol. 9, 34–37.

    • Search Google Scholar
    • Export Citation
  • von Storch, H., , T. Bruns, , I. Fischer-Bruns, , and K. Hasselmann, 1988: Principal oscillation pattern analysis of the 30–60 day oscillation in a GCM equatorial troposphere. J. Geophys. Res, 93 , 11 02111 036.

    • Search Google Scholar
    • Export Citation
  • Woodruff, S., , S. Lubker, , K. Wolter, , S. Worley, , and J. Elms, 1993: Comprehensive Ocean–Atmosphere Data Set (COADS) Release 1a: 1980–92. Earth Syst. Monitor, 4 , 38.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 48 48 6
PDF Downloads 25 25 4

Expected and Actual Errors of Linear Inverse Model Forecasts

View More View Less
  • 1 NOAA–CIRES/Climate Diagnostics Center, Boulder, Colorado
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

The authors discuss forecast uncertainty, which should be employed for the purpose of judging prediction model performance, and actual forecast errors, which can be employed by users to judge the reliability of the forecasts. Realistic a priori estimates of forecast uncertainty for linear inverse modeling sea surface temperature predictions are presented and compared with the actual forecast errors occurring during the course of real-time predictions. The a priori estimates are of size similar to the actual errors except during the warmest phase of observed El Niño events.

Corresponding author address: Dr. Cécile Penland, NOAA–CIRES/Climate Diagnostics Center, R/CDC1, 325 Broadway, Boulder, CO 80305-3328. Email: mcp@cdc.noaa.gov

Abstract

The authors discuss forecast uncertainty, which should be employed for the purpose of judging prediction model performance, and actual forecast errors, which can be employed by users to judge the reliability of the forecasts. Realistic a priori estimates of forecast uncertainty for linear inverse modeling sea surface temperature predictions are presented and compared with the actual forecast errors occurring during the course of real-time predictions. The a priori estimates are of size similar to the actual errors except during the warmest phase of observed El Niño events.

Corresponding author address: Dr. Cécile Penland, NOAA–CIRES/Climate Diagnostics Center, R/CDC1, 325 Broadway, Boulder, CO 80305-3328. Email: mcp@cdc.noaa.gov

Save