Objective Climatological Analysis of Extreme Weather Events in Arizona during the North American Monsoon

• Main Authors: Mazon, Jeremy J., Castro, Christopher L., Adams, David K., Chang, Hsin-I, Carrillo, Carlos M., Brost, John J.
• Other Authors: Univ Arizona, Dept Hydrol & Atmospher Sci
• Language: en
• Published: AMER METEOROLOGICAL SOC 2016
• Subjects: Empirical orthogonal functions; Climate classification/regimes; North America; Monsoons; Severe storms; Summer/warm season
• Online Access: http://hdl.handle.net/10150/622579; http://arizona.openrepository.com/arizona/handle/10150/622579

Almost one-half of the annual precipitation in the southwestern United States occurs during the North American monsoon (NAM). Given favorable synoptic-scale conditions, organized monsoon thunderstorms may affect relatively large geographic areas. Through an objective analysis of atmospheric reanalysis and observational data, the dominant synoptic patterns associated with NAM extreme events are determined for the period from 1993 to 2010. Thermodynamically favorable extreme-weather-event days are selected on the basis of atmospheric instability and precipitable water vapor from Tucson, Arizona, rawinsonde data. The atmospheric circulation patterns at 500 hPa associated with the extreme events are objectively characterized using principal component analysis. The first two dominant modes of 500-hPa geopotential-height anomalies of the severe-weather-event days correspond to type-I and type-II severe-weather-event patterns previously subjectively identified by Maddox et al. These patterns reflect a positioning of the monsoon ridge to the north and east or north and west, respectively, from its position in the "Four Corners" region during the period of the climatological maximum of monsoon precipitation from mid-July to mid-August. An hourly radar gauge precipitation product shows evidence of organized, westward-propagating convection in Arizona during the type-I and type-II severe weather events. This new methodological approach for objectively identifying severe weather events may be easily adapted to inform operational forecasting or analysis of gridded climate data.