EVALUATION AND ANALYSIS OF DAILY ATMOSPHERIC CIRCULATION PATTERNS OF THE 500 HPA PRESSURE FIELD OVER THE SOUTHWESTERN USA

Daily atmospheric circulation patterns (CP's) are defined and analyzed on the basis of the 500 hPa pressure field for the purpose of describ ing and then later generating local hydroclimatological variables such as precipitation under possible climate change over the southwestern USA. To obtain the CP's we first use a so-called objective or automate d clustering method, namely, principal com ponent analysis (PCA) coupl ed with K-means clustering algorithm. To obtain a set of CP types that are more distinguishable and so more useful for our purpose we follow two possible ways: (1) reduce subjectively the number of CP's from PC A coupled with K-means clustering analysis by aggregating the types on the basis of the precipitation producing characteristics, (2) perform K-means clustering analysis with fewer types. Thus we have three diff erent cluster systems: original types from the result of PCA coupled w ith K-means clustering (8 or 9 types depending on the season), types f rom the K-means clustering analysis with fewer types (5 or 6 types in each season) and the subjectively aggregated types (3 types in any sea sons). We compare them from the point of view of information content f or precipitation modeling. An analysis is made for these types for com parison: statistical properties of these patterns are evaluated and an alyzed using first observed data, and then General Circulation Model ( GCM) outputs for 1 X CO2 and 2 X CO2 scenarios to estimate climate cha nge effects, On the basis of the historical circulation pattern catalo gue and observed precipitation data in Arizona, simple calculations ar e provided to find the ''precipitation-producing'' types of each syste m in each season. Three indices are evaluated using the same observed precipitation data from ten Arizona stations in order to measure objec tively the information content of each type in the three cluster syste ms. It turns out that the larger number of types in a given season giv es higher information content as we expected.