A Multivariate Chain Model for Simulating Climatic Parameters from Daily Data

Abstract

A method is described for simultaneously simulating maximum and minimum temperatures and daily precipitation amounts in a physically consistent manner. The method ?chains? actual days from a historical dataset by defining a ?discriminant space? using multiple discriminant analysis. A set of analogous days is selected from discriminant space using a nearest-neighbor search. The next day in the chain is the day subsequent to a randomly selected day from the set of analogous days. The method was tested on data for Tucson and Safford, Arizona. A high degree of similarity between the simulated and observed data was found. A slight tendency to underestimate the variance of monthly average temperatures was noted. The distribution of monthly temperature extremes was quite well reproduced with the exception of a tendency to be conservative in predicting the warmest minimum temperatures and the coolest maximum temperatures. Very little difference between the simulated and observed distributions of diurnal temperature range was found. The median and 90th percentile of monthly precipitation totals were well reproduced. A tendency to underestimate the frequency of dry months was noted. The frequency of runs of wet and dry days of different lengths was found to be not significantly different for the simulated and observed data. Reproduction of wet-day run frequency for the first-order multivariate chain model was comparable to that using a two-state, first-order Markov chain.