Simulation of meteorological data for use in agricultural production studies
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Meteorological variation is a primary source of risk and uncertainty in the production of agricultural commodities. Incorporation of meteorological variables in simulation models requires the recreation of the same stochastic relationships which underlie the basic meteorological process. This paper presents a methodology for using Monte Carlo techniques to simulate meteorological values on an aggregated basis (e.g. monthly or quarterly) using empirical distributions. An example for precipitation (rainfall) and temperature variables is developed with endpoints of the empirical functions distributed exponentially, stacked, and standard. Statistical properties, with the exception of the standard deviation, of the historical series appears to be fairly well maintained in the simulated series when the endpoints are not stacked or distributed exponentially. © 1990.
author list (cited authors)
Van Tassell, L. W., Richardson, J. W., & Conner, J. R.