final_program_abstracts[1]

11 IMSC Session Program
Assessing the similarity between time series of two
meteorological fields using the RV coefficient
Wednesday - Poster Session 5
Jacqueline Potts and Claus Dieter-Mayer
Biomathematics and Statistics Scotland, UK
The technique of singular value decomposition of the covariance matrix, also known
as maximum covariance analysis, has been widely used in the atmospheric sciences to
compare time series of two meteorological fields. Similarity between the ordinations
is often assessed by means of the correlation between the data points on two
corresponding axes. Maximum covariance analysis is also used in the biological
sciences, where it is known as co-inertia analysis. In the biological literature the RV
coefficient is often reported as a measure of the overall similarity between two data
sets. This statistic could also be used to assess the overall similarity between time
series of two meteorological fields. The significance of the RV coefficient may be
assessed by means of a permutation test. If the two meteorological fields are
completely independent, a value of the RV coefficient close to zero would be
expected. However, where there are more grid points or observing stations than the
number of observations at each, values of the RV coefficient may be misleadingly
high. An adjusted RV coefficient is presented, which is unbiased in the case of
independent fields.
Abstracts 213