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Adela García Guzmán and William C. Torrez

Abstract

A generalization of the chain-dependent process is proposed to describe daily rainfall. The model allows probabilities of rain occurrence to depend on the amount of rain in the day before. The influence of the antecedent amount of rain on the next day occurrence is called “feedback.” A method for identification of feedback effects in a series of observations is derived and application made to rainfall occurrence at Los Angeles. Performance of the model in reproducing other precipitation patterns, such as maximum daily rainfall or total amount of rainfall in successive n-day periods, was checked by the Monte Carlo method.

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Jose Roldan-Cañas, Adela Garcia-Guzman, and Alberto Losada-Villasante

Abstract

Knowledge of wind occurrence in any location is a valuable input in most agricultural studies. A bivariate stochastic process is proposed here as a model of daily wind occurrence. Two wind variables have been considered: 1) horizontal direction, fitted to a first-order Markov chain; and 2) speed, relative to the given direction, fitted to a gamma distribution. Performance of the model is tested by comparing the statistics of generated and experimentally observed data.

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