Empirical Distributions and Production Analysis: A Documentation Using Meteorological Data

Weather 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 and temperature variables is developed with endpoints of the empirical functions distributed exponentially, stacked, and standard. The statistical properties observed in the historical series appears to be closely maintained in the simulated series. EMPIRICAL DISTRIBUTIONS AND PRODUCTION ANALYSIS: A DOCUMENTATION USING METEOROLOGICAL DATA