This research proposes to carry out a principal
component analysis using the maximum covariance method, with the aim of finding
the most robust spatio-temporal
relationships between several candidate predictors and the accumulated monthly
precipitation recorded in Cuba during the period 1980-2020. This process will make it possible to
establish quantitative relationships that, together with theoretical
considerations, make it possible to reduce the list of predictors to be used
for the purpose of obtaining seasonal predictions. The values of the predictors
are represented through monthly averages obtained from ERA5 reanalysis, while
monthly accumulated precipitation data were obtained from a national-scope grid
with 4 km of spatial resolution, used as predictand. The results obtained
reflect the highest spatio-temporal correlation values with the first
variability mode in all cases, indicating that the usual regime conditions are
predominant and have a greater coupling with the precipitation variability in
the analyzed temporal scale. In addition, they suggest that the candidates that
explain the transport of moisture at low levels, as well as the gradients
between the middle and lower troposphere, show the most robust associations. In
the same way, the surface temperature of tropical Atlantic Sea, the flow
related to Quasi-Biennial Oscillation and the thermodynamic indices, K Index
and Galvez-Davison Index, present good degrees of association, for which reason
they can be considered the most recommendable for carrying out forecasting
experiments.
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