STATISTICAL MODELING OF CLIMATE PARAMETERS

Abstract

Climate variations more or less decide the environmental dynamics of an area. Extreme changes of climatic conditions cause problems in anywhere, especially to the agricultural sector. In this respect, knowledge of the likely climate and its impact could add value to Agro-environmental management. In this study, a forecasting approach that incorporates climatic variability is presented. Using meteorological data collected, the time series model: Auto Regressive Integrated Moving Average (ARIMA) is applied to estimate future variation in meteorological parameters. The parameter of each model is estimated with the time series module of software SPSS. The annual rainfall, temperature, relative humidity is forecasted with the observed data.