Local rainfall modelling based on global climate information: A data-based approach. (September 2020) - Mendoza, Daniel E.; Samaniego, Esteban P.; Mora, Diego E.; Espinoza, Mauricio J.; Pacheco, Esteban A.; Avilés, Alex M.
Abstract: Modelling climate is complex due to multi-scale interactions and strong nonlinearities. However, climate signals are typically quasi-periodical and are likely to depend on exogenous - variables . Motivated by this insight, we propose a strategy to circumvent modelling complexity based on the following ideas. 1) The observed signals can be decomposed into non-stationary trends and quasi-periodicities through Dynamic-Harmonic-Regressions (DHR). 2) The main-frequencies and decomposed signals can be used for constructing a harmonic model with varying parameters depending on exogenous-variables . 3) The State-Dependent-Parameter (SDP) technique allows for the dynamical estimation of these parameters . The resulting DHR-SDP combined approach is applied to rainfall-monthly modelling, using global-climate signals as exogenous-variables . As a result, 1) the model yields better predictions than standard alternative techniques; 2) the model is robust regarding data limitations and useful for several-steps-ahead forecasting; 3) interesting relations between global-climate states and the local rainfall's seasonality are obtained from the SDP estimated functions. Highlights: Global climate information drives a non-linear local climate model based on DHR and SDP techniques. The model is useful for predictive and gap-filling purposes, outperforming other related stochastic alternatives. The model has the potential to establish climate indicators and can be a starting point for the identification of non-linear climate mechanisms through different scales. The model could be a complementary tool when combined with more complex deterministic options.