Application of Neural Network Principal Components to Climate Data

Principal component analysis (PCA) is one of the most widely used methods in the examination of climate data. However, PCA of a dataset is handicapped if the data size is large. PCA of a large dataset would require huge computer resources in terms of memory and CPU time. Neural network principal component analysis (NNPCA), which has been used mainly in the signal-processing field, can be a useful tool in the analysis of large climate datasets. NNPCA requirements of computer memory and CPU time are far less than what are Aneeded by conventional methods of PCA, such as singular value decomposition of the data matrix. In this paper, an NNPCA application to climate data is introduced. NNPCA is applied to reanalysis data of monthly and daily global maps of the 850-hPa geopotential height from the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis data. These data, covering the period from 1948 to 2003, are composed of 648 monthly maps and 20 117 daily ones. The first 10 principal components (PCs) of the daily data contribute up to 58.27% of the data total variance, whereas the first 10 PCs of monthly data represent 80.72% of the total variance. The first, second, and third PCs describe the annual variation, southern annular mode, and northern annular mode, respectively.