A study of heavy spillover precipitation which contributed to the Reno floods of 1997 and 2005.

摘要

Forecasting precipitation in mountainous regions is a very demanding and arduous task. Some examples of devastating flooding events in mountainous areas include the Big Thompson Flood of 1976 in the Rocky Mountains (Maddox 1978) and the Piedmont flood event of 1994 in the Alps (Buzzi et al. 1998). The flooding events in the Sierra Nevada during 1997 and 2005 had a major impact on the Truckee River Valley including the Reno metropolitan area, however not much has been documented with either case. Both flooding events in Reno involved a phasing of multi-level jet streaks that enabled ageostrophic and diabatic adjustments to create processes that led to flooding rains. It is the juxtaposition of cold air aloft, tropical air stream from the South Pacific and hydrometeors that traverse complex terrain that led to the extreme leeside liquid precipitation accumulation to a significant elevation. A verification of the simulation of the 1997 and 2005 case studies employing the Operational Multiscale Environment model with Grid Adaptivity (OMEGA e.g., Bacon et al. 2000) will be performed. What makes OMEGA a novel approach for modeling research and forecasting is that it employs an unstructured grid that can be used to adapt to certain features like clouds and terrain therefore enhancing the local resolution of key orographic forcing features. The OMEGA adaptive grid simulations were performed with static grid adaptivity to 1 km resolution over the Sierra Nevada. These simulations where then validated against asynoptic and synoptic observations including Doppler and surface rainfall observations. The goals for this research are to (1) understand the precursor physical and dynamical processes which cause such an event, (2) explore whether or not an unstructured, static, adaptive grid will produce accurate simulations of an extreme leeside rainfall event and (3) to apply cognitive information processing employing the method of case-based reasoning to the problem of extreme Sierra Nevada precipitation events. The purpose of case-based reasoning is to create a synergistic cyber-relationship between the meteorologist and the forecast model by exchanging feedback towards improving a forecasting algorithm.