Abstract

Factors that shape the weather are studied to design models to make predictions. Understanding the relationships among these factors contribute to a better knowledge of atmospheric phenomena. However, conventional statistical techniques do not take into account the dependent relationships among these factors. Bayesian learning models are used in learning processes, which consist in quantification of conditional probability, resulting in the identification of causal relationships between the variables. In this paper the use of Bayesian networks for probabilistic analysis allows us to determine spatial and temporal dependencies among climatic variables not observable with other methods. Considering an incomplete meteorological data set from three years and three sites with distinct climates, the dependent relationships between climatic variables are observable in different proportions for each type of climate. It is possible to determine the influences among variables, temperature, humidity, dew point, pressure, wind speed and precipitation, through the use of Bayesian networks that permit us to understand their interaction in different climates.

Key words: Probability models, climate, forecast, data mining, K2 algorithm.