%0 Journal Article %1 Hagedorn2005Rationale %A Hagedorn, Renate %A Doblas-Reyes, Francisco J. %A Palmer, T. N. %C ECMWF, Shinfield Park, Reading RG2 9AX, UK %D 2005 %I Munksgaard International Publishers %J Tellus A %K verification statistics skill seasonal %N 3 %P 219--233 %R 10.1111/j.1600-0870.2005.00103.x %T The rationale behind the success of multi-model ensembles in seasonal forecasting – I. Basic concept %U http://dx.doi.org/10.1111/j.1600-0870.2005.00103.x %V 57 %X The DEMETER multi-model ensemble system is used to investigate the rationale behind the multi-model concept. A comprehensive documentation of the differences in the single and multi-model performance in the DEMETER hindcast data set is given. Both deterministic and probabilistic diagnostics are used and a variety of analyses demonstrate the improvements achieved by using multi-model instead of single-model ensembles. In order to understand the reason behind the multi-model superiority, basic scenarios describing how the multi-model approach can improve over single-model skill are discussed. It is demonstrated that multi-model superiority is caused not only by error compensation but in particular by its greater consistency and reliability.

@article{Hagedorn2005Rationale, abstract = {The DEMETER multi-model ensemble system is used to investigate the rationale behind the multi-model concept. A comprehensive documentation of the differences in the single and multi-model performance in the DEMETER hindcast data set is given. Both deterministic and probabilistic diagnostics are used and a variety of analyses demonstrate the improvements achieved by using multi-model instead of single-model ensembles. In order to understand the reason behind the multi-model superiority, basic scenarios describing how the multi-model approach can improve over single-model skill are discussed. It is demonstrated that multi-model superiority is caused not only by error compensation but in particular by its greater consistency and reliability.}, added-at = {2018-06-18T21:23:34.000+0200}, address = {ECMWF, Shinfield Park, Reading RG2 9AX, UK}, author = {Hagedorn, Renate and Doblas-Reyes, Francisco J. and Palmer, T. N.}, biburl = {https://www.bibsonomy.org/bibtex/28db4792467755a0481b2a4d7598f906e/pbett}, citeulike-article-id = {171668}, citeulike-linkout-0 = {http://www.blackwell-synergy.com/doi/abs/10.1111/j.1600-0870.2005.00103.x}, citeulike-linkout-1 = {http://dx.doi.org/10.1111/j.1600-0870.2005.00103.x}, citeulike-linkout-2 = {http://www.ingentaconnect.com/content/mksg/tea/2005/00000057/00000003/art00002}, citeulike-linkout-3 = {http://www3.interscience.wiley.com/cgi-bin/abstract/118697312/ABSTRACT}, comment = {(private-note)Classic explaining why multi-model systems can have more skill, rather than just smudging out any skill.}, day = 1, doi = {10.1111/j.1600-0870.2005.00103.x}, interhash = {bfdb9503a3c48a99cdbedb289a16b855}, intrahash = {8db4792467755a0481b2a4d7598f906e}, issn = {0280-6495}, journal = {Tellus A}, keywords = {verification statistics skill seasonal}, month = may, number = 3, pages = {219--233}, posted-at = {2016-12-05 18:18:29}, priority = {2}, publisher = {Munksgaard International Publishers}, timestamp = {2018-06-22T18:36:38.000+0200}, title = {The rationale behind the success of multi-model ensembles in seasonal forecasting – I. Basic concept}, url = {http://dx.doi.org/10.1111/j.1600-0870.2005.00103.x}, volume = 57, year = 2005 }