I applied EOF (Empirical Orthogonal Function, a type of Principal Component Analysis, PCA) analysis to a dataset(geopotential height at 925hpa from ECMWF-interim reanalysis data) that contains 3 coordinates (time, longitude, latitude). Moreover, spatial field data at each time level was defined as sample or origin pattern. The results of the EOF showed that the first 4 PCs can explain 95% of the variance. How can I classify the sample or origin patterns into different PCs? In other words, which PC is similar to the origin patterns mostly?
I recommend you read Monahan et al. (1990) for a thorough explanation of Empirical Orthogonal Functions. I suggest you pay attention to the way the principal components are extracted. Ultimately, there is no guarantee that a sample belongs to a specific component. What you are working with is eigenvalues/eigenvectors and thus a specific data point can have influences from multiple components. The EOF provides a time series of eigenvalues for each component. The larger the eigenvalue for that component at a specific time, the more that component explains the data point.
If what you need is to assign a data point to a component, I would recommend a cluster analysis instead. For instance Smith & Aretxabaleta (2007) present an improved methodology to separate regimes that outperforms the EOF analysis in an ENSO-influenced dataset.