Theoretical examination of a multi-model composite for seasonal prediction

Details Theoretical examination of a multi-model composite for seasonal prediction Theoretical examination of a multi-model composite for seasonal prediction

Sep 2005

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2005georl..3218707y&link_type=abstract

Geophysical Research Letters, Volume 32, Issue 18, CiteID L18707

Computer Science

Performance

9

Atmospheric Processes: Global Climate Models (1626, 4928)

Scientific paper

The performance of a multi-model composite for seasonal prediction is theoretically examined in terms of a correlation skill. On the basis of theoretical analysis, we discuss the improvement of skill in the multi-model composite using the APCN multi-model seasonal prediction dataset. Although the skill of multi-model composite is generally increased by increasing the number of models, the highest skill can be obtained by selecting several skillful models which are less dependent each other.

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