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Multimodel ensemble forecasting of rainfall over East Asia: regularized regression approach
Author(s) -
Lim Yaeji,
Jo Seongil,
Lee Jaeyong,
Oh HeeSeok,
Lee SangGoo,
Park Yongtae,
Kang HyunSuk
Publication year - 2014
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.3938
Subject(s) - regression , computer science , regularization (linguistics) , regression analysis , ensemble forecasting , data assimilation , climatology , meteorology , statistics , mathematics , machine learning , artificial intelligence , geography , geology
This paper considers the problem of predicting the rainfall over East Asia from multimode outputs. For this purpose, we propose a new multimode ensemble method based on regularized regression approach, which consists of two steps, the pre‐processing step and the ensemble step. In the pre‐processing step, we improve prediction from each model output using regularized regression, and in the ensemble step, we apply regularization‐based regression method to combine the result from the pre‐processing step. The main benefits of the proposed method are that it improves prediction accuracy, and it is capable of solving the singularity problem so that it can integrate many climate variables from multimode outputs for a better prediction. The proposed method is applied to monthly outputs from nine general circulation models ( GCMs ) on boreal summer (June, July, and August) over 20 years (1983–2002). The prediction ability of the proposed ensemble forecast is compared with the observations and the outputs (prediction) from each GCM . The results show that the proposed method is capable of improving forecast accuracy by adjusting each model before combining.