final_program_abstracts[1]

11 IMSC Session Program
Multi-model ensemble forecasting of rainfall over East Asia
region using regularized regression
Friday - Parallel Session 1
Yaeji Lim 1 , Seongil Jo 1 , Jaeyong Lee 1 , Hee-Seok Oh 1 and Hyun-Suk Kang 2
1 Department of Statistics, Seoul National University, Seoul, Korea
2
National Institute of Meteorological Research, Korea Meteorological
Administration, Seoul, Korea
In this study, a statistical ensemble forecasting method is proposed for the prediction
of rainfall over the East Asia region based on regularized regression approach. The
proposed method consists of two steps, preprocessing step and ensemble step: (1) In
the preprocessing step, we generate predicted values by applying a method to each
individual GCM model output. The preprocessing step can be implemented using
various methods such as EOF/CCA, regularized CCA and regularized regression
methods including LASSO and ridge regression. (2) The ensemble step combines the
results from the preprocessing step by using regularized regression methods and
provides the final prediction. Key features of the proposed method are as follows: (1)
it can evaluate ontribution of models for prediction by selecting some models rather
than considering all models, (2) using regularized regression allows to use highdimensional
data, so that we can consider all locations as well as various climate
variables, and (3) it is computationally effcient, and hence, various climate data
including observations and model output can be analyzed. The proposed method is
applied to monthly outputs of nine GCM models on boreal summer (June, July, and
August) from 1983 to 2002. The prediction skill of the ensemble forecast is examined
against observations and corresponding outputs of each constituent model. The result
shows that the proposed method is capable to improve the forecast by adjusting each
model before combing and taking the strength of each model.
Abstracts 360