  Six time series related to atmospheric phenomena are used as inputs for
experiments offorecasting with singular spectrum analysis (SSA). Existing
methods for SSA parametersselection are compared throughout their forecasting
accuracy relatively to an optimal aposteriori selection and to a naive
forecasting methods. The comparison shows that awidespread practice of
selecting longer windows leads often to poorer predictions. It alsoconfirms
that the choices of the window length and of the grouping are essential.
Withthe mean error of rainfall forecasting below 1.5%, SSA appears as a viable
alternative forhorizons beyond two weeks.
