A probabilistic approach for weather forecast using spatio-temporal inter-relationships among climate variables

Weather forecast is one of the major services provided by the meteorological departments. It has huge impact on the global economy, agriculture, industry, transport and so on. Weather attributes (climate variables), like air temperature, pressure, precipitation, humidity etc. are meteorological variables which depend both on the associated region (or space) and time. They are also highly inter-related to one another in spatio-temporal scale. Therefore, the analysis of these spatio-temporal inter-relationships can be helpful for forecasting weather of any region for any point of time. While there exist several approaches to weather forecast, there is only little work that deals with such spatio-temporal inter-relationships among multiple climate variables. This paper presents a probabilistic approach based on fuzzy Bayesian network (FBN) to forecast the weather condition. The approach first predicts the spatio-temporal interrelationships among different climate variables. Then the predicted relationships are utilized to forecast the weather condition of the particular region. To deal with uncertainty and imprecision present in data, the proposed weather-forecast approach uses the principles of a newly defined FBN, named as NFBN. The proposed approach has been evaluated with data sets from Fetch-Climate Explorer of Microsoft Research, and found to perform better than several existing forecasting techniques.