A Monte Carlo experiment was done to create a statistically significant test table for selecting EOF modes whose signals are above the noise level. Numerical analysis showed that it is feasible to choose a shorter data set between temporal and spatial dimensions for conducting an EOF analysis, and thus simplify calculation of the covariance matrix and solution of its eigenvalues and eigenvectors. The experiment showed that the dominant EOF modes remained the same. This approach is more computationally efficient, reducing both computing time and computer memory requirement, while still retaining the dominant signal. A Monte Carlo experiment was done to create a significant significant table for selecting EOF modes whose signals are above the noise level. Numerical analysis showed that it is feasible to choose a shorter data set between temporal and spatial dimensions for conducting an EOF analysis, and thus simplify calculation of the covariance matrix and solution of its eigenvalues ​​and eigenvectors. The experiment showed that the dominant EOF modes remain the same. This approach is more computationally efficient, reducing both computing time and computer memory requirement, while still retaining the dominant signal.