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 th...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.展开更多
文摘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.
