Seasonal Autoregressive Models for Estimating the Probability of Frost in Rafsanjan

This work develops a statistical model to assess the frost risk in rafsanjan, one of the largest pistachioproduction regions in the world. these models can be used to estimate the probability that a frost happens in agiven time-period during the year; a frost happens after 10 warm days in the growing season. these probabilityestimates then can be used for: (1) assessing the agroclimate risk of investing in this industry; (2) pricing ofweather derivatives. autoregressive models with time-varying coefficients and different lags are compared usingaic/bic/aicc and cross validation criterions. the optimal model is an ar (1) with both intercept and the “autoregressivecoefficients” vary with time. the long-term trends are also accounted for and estimated from data.the optimal models are then used to simulate future weather from which the probabilities of appropriate hazardevents are estimated.