Objective Discrimination and Pooling Models in the Ensemble

  • Eugene Genikhovich
  • Tatiana Pavlova
  • Alexander Ziv
Conference paper
Part of the NATO Science for Peace and Security Series C: Environmental Security book series (NAPSC)


Data assimilating and pooling the model predictions in the multi-model ensemble, described in this paper, are based on the techniques of approximation and regularization of multidimensional vectors in the linear Euclidean space with the use of the non-orthogonal vector basis. This approach has been successfully applied to (i) the time series of the annual temperatures averaged over the globe and northern hemisphere corresponding to the last 100years, and (ii) the fields of concentrations of atmospheric pollutants over Europe. Quantitative estimates of the efficiency of the proposed technique are presented in the paper.


Root Mean Square Error Numerical Weather Prediction Nitrogen Dioxide Objective Discrimination Observational Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was partially funded by the Russian Foundation for Basic Research (grant # 08-05-00569-a). We would like to acknowledge the participants of the project GEMS for kindly providing all necessary data sets.


  1. 1.
    Genikhovich EL, Pavlova TV, Kattsov VM (2010) On combining climate models in ensembles. Proc Main Geophys Observatory 561:28–46 (in Russian)Google Scholar
  2. 2.
    Knutti R (2010) The end of model democracy? Clim Change 102(3–4):395–404. doi:10.1007/s10584-010-9800-2CrossRefGoogle Scholar
  3. 3.
    Mallet V, Sportisse B (2006) Ensemble-based air quality forecasts: a multimodel approach applied to ozone. J Geophys Res 111:D18302.1–D18302.11. doi:10.1029/2005JD006675Google Scholar
  4. 4.
    Mallet V, Stoltz G, Mauricette B (2009) Ozone ensemble forecast with machine learning algorithms. J Geophys Res 114:D05307.1–D05302.13Google Scholar
  5. 5.
    Meehl GA, Covey C, Delworth T, Latif M, McAvaney B, Mitchell JFB, Stouffer RJ, Taylor KE (2007) The WCPR CMIP3 multimodel dataset: a new era in climate change research. Bull Am Meteorol Soc 88:1383–1394. doi:10/1175/BAMS-88-9-1383CrossRefGoogle Scholar
  6. 6.
  7. 7.
    Riccio A, Guinta G, Galmarini S (2007) Seeking for the rational basis of the median model: the optimal combination of multi-model ensemble results. Atmos Chem Phys 7:6085–6098, CrossRefGoogle Scholar
  8. 8.
    Strauss D, Shukla J, Paolino D, Schubert S, Suarez M, Pegion P, Kumar A (2003) Predictability of the seasonal mean atmospheric circulation during autumn, winter, and spring. J Climate 16:3629–3649CrossRefGoogle Scholar
  9. 9.
    Tippett MK, DelSole T, Mason SJ, Barnston AG (2008) Regression-based method for finding coupled patterns. J Climate 21:4384–4398CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Eugene Genikhovich
    • 1
  • Tatiana Pavlova
    • 1
  • Alexander Ziv
    • 1
  1. 1.Voeikov Main Geophysical ObservatorySt. PetersburgRussia

Personalised recommendations