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My suggestion would be to do a Principal Component Analysis (PCA) or Empirical Orthogonal Functions (EOF) analysis on the wind data. The result of the analysis would be a set of modes of variability. You would be looking for modes that show areas that are large in magnitude but out of phase. As for the time scale, you need to check the eigenvectors and ...


5

Convergent cross mapping (CCM) is a recently developed tool to answer the question you've asked. It's based on tools developed in nonlinear time series analysis and dynamical systems theory. It allows you to: 1) determine if a causal relationship between two variables is present 2) establish the direction of causality 3) do so even in the presence of noise. ...


4

I can give some examples from atmospheric science: Wind and temperature in the vertical direction: as you increase in height, the temperature decreases due to the conservation of geopotential energy. Also the wind speed increases, due to the lack of friction. In data assimilation, spurious correlations are quite common, especially for large distances. ...


3

As an alternative to Convergent Cross Mapping (CCM), the recent Tigramite is a fast python library for causal discovery that promises to ... ... outperforms current approaches in detection power and scales up to high-dimensional datasets. It overcomes detection biases, especially when strong autocorrelations are present, and allows ranking associations in ...


1

One suggestion on generating the monthly flows is to use a seasonal ARMA model directly (e.g. see course notes), since you are looking at ARMA model for stochastic generation anyway. That would allow the explicit estimation of seasonal parameters for synthetic generation, and let you skip the step of going annual --> monthly, instead go monthly and calculate ...


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