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Kriging Kalman combined filter to estimate and pbkp_redict the evolution of climatic states in some weather stations in Ecuador

Abstract:

This article proposes a methodology that involves the Universal Kriging filter (UKF) and the Kalman filter (KF) to study temporal-space dynamic models. Spatial pbkp_rediction surfaces are constructed using the UKF and the KF is used to estimate the temporal effects. The UKF provides a successful estimation approach from the point of view of spatial statistics, while the KF describes a well-established recursive procedure to estimate the states and parameters in these models. The methodology is illustrated using 30-year time series of 3 meteorological stations in Ecuador. The model allows to make pbkp_redictions on temperature, precipitation and humidity, obtaining estimates of unknown states very similar when compared to original series. The root mean square error was used as a measure of goodness of fit to measure the estimation quality of the algorithm, obtaining satisfactory results.