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Theoretical examination of a multi‐model composite for seasonal prediction
Author(s) -
Yoo Jin Ho,
Kang InSik
Publication year - 2005
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2005gl023513
Subject(s) - composite number , climatology , geology , environmental science , meteorology , econometrics , atmospheric sciences , computer science , mathematics , physics , algorithm
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.