A MULTIVARIATE CHAIN MODEL FOR SIMULATING CLIMATIC PARAMETERS FROM DAILY DATA

A method is described for simultaneously simulating maximum and minimu m temperatures and daily precipitation amounts in a physically consist ent manner. The method ''chains'' actual days from a historical datase t by defining a ''discriminant space'' using multiple discriminant ana lysis. A set of analogous days is selected from discriminant space usi ng a nearest-neighbor search. The next day in the chain is the day sub sequent to a randomly selected day from the set of analogous days. The method was tested on data for Tucson and Safford, Arizona. A high deg ree of similarity between the simulated and observed data was found. A slight tendency to underestimate the variance of monthly average temp eratures was noted. The distribution of monthly temperature extremes w as quite well reproduced with the exception of a tendency to be conser vative 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 me dian and 90th percentile of monthly precipitation totals were well rep roduced. A tendency to underestimate the frequency of dry months was n oted. The frequency of runs of wet and dry days of different lengths w as found to be not significantly different for the simulated and obser ved data. Reproduction of wet-day run frequency for the first-order mu ltivariate chain model was comparable to that using a two-state, first -order Markov chain.