Prediction of East Asian Summer Precipitation via Independent Component Analysis
- Authors
- Lim, Yaeji; Jo, Seongil; Lee, Jaeyong; Oh, Hee-Seok; Kang, Hyun-Suk
- Issue Date
- May-2012
- Publisher
- KOREAN METEOROLOGICAL SOC
- Keywords
- Canonical correlation analysis; climate change; independent component analysis; precipitation; prediction
- Citation
- ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES, v.48, no.2, pp 125 - 134
- Pages
- 10
- Journal Title
- ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES
- Volume
- 48
- Number
- 2
- Start Page
- 125
- End Page
- 134
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/48307
- DOI
- 10.1007/s13143-012-0012-8
- ISSN
- 1976-7633
1976-7951
- Abstract
- A new statistical postprocessing method is proposed for seasonal climate prediction. The proposed method is based on a combination of independent component analysis (ICA) and canonical correlation analysis (CCA). Since the classical CCA cannot handle high-dimensional data wherein the number of variables is larger than the number of observations, ICA is pre-performed to reduce the dimension of the data. It is well known that empirical orthogonal function (EOF) analysis is a popular method for dimension reduction in the climatology community; however, loss of information occurs when the data is not Gaussian distributed. To extend the scope of distribution assumption and improve the prediction ability simultaneously, we propose the ICA-based method. This study focuses on the prediction of future precipitation for the boreal summer (June-July-August; JJA) through 29 years (1979-2007) on East Asia region. Results of the proposed ICA-based method show an improvement in seasonal climate prediction in terms of correlation and root mean square error as compared with those of the GCM simulation and the EOF/CCA method.
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