Chaos Theory of Mathematics as seen from a New Perspective for Weather Forecasting

Abstract

In this work, 8 meteorological variables were modeled in the Yabú station, Cuba, for which the daily database of this meteorological station was used, where the meteorological variables were taken into account are: extreme temperatures, extreme humidity and its average value, precipitation, wind force and cloudiness corresponding to the period 1977 to 2021. A linear mathematical model was obtained using the Objective Regressive Regression (ORR) methodology for each variable, which explains its behavior according to these variables, 15, 13, 10 and 8 years in advance. The calculation of the mean error with respect to the persistence forecast in temperatures, wind strength and cloudiness, as well as the persistence model was better with respect to humidity, this allows having valuable long-term information of the weather in a locality, which results in better decision making in the different aspects of the economy and society that are impacted by the weather forecast. It is concluded that these models allow long-term weather forecasting, opening a new possibility for forecasting, so that weather chaos can be overcome if this way of forecasting is used; moreover, it is the first time that an ORR model is applied to weather forecasting processes for a specific day so many years in advance.