U.S.- Italy Research Workshop on the
Hydrometeorology, Impacts, and Management of Extreme Floods
Perugia (Italy), November 1995
METEOROLOGICAL AND CLIMATIC FACTORS AFFECTING EXTREME FLOODS: PROSPECTS FOR MESOSCALE MODEL PREDICTION AND SATELLITE PRECIPITATION MONITORING OF TERRAIN-FORCED EVENTS
Eric A. Smith
Department of Meteorology,
Florida State University,
Tallahassee, FL 32306,U.S.A.
Gregory J. Tripoli
Department of Atmospheric and Ocean Sciences,
University of Wisconsin,
Madison, WI 53706, U.S.A.
This study examines various meteorological and climatic factors leading up to various major damaging floods that have occurred in the continental United States in the last few decades, with a view toward explaining why a combination of mesoscale modeling and satellite-based precipitation monitoring may be the only realistic approach in obtaining timely inputs to a hydrological flood forecast model. The premise of this study is that a flood producing storm is a complex but understandable meteorological extreme, whose genesis conditions are only predictable with a mesoscale model because of the intricate synoptic scale-mesoscale interactions, but whose specific location, precipitation efficiency, and total rain production are best left to a real-time, geosynchronous satellite-based infrared rainfall monitoring algorithm, periodically calibrated by a passive microwave-based rain-profile algorithm. The study focuses on arguments concerning why such an approach is warranted, particularly in the context of the mountain areas of northern Italy, where terrain features serve to organize and focus storm development as an outgrowth from synoptic scale conditions favorable to storm occurrence. An example of applying a mesoscale model and satellite rainfall analysis to an extreme flood event that took place in the Genova area in late September, 1992, is presented. The pursuant analysis is designed to help develop the main themes of our argument that the main features of a flood producing storm occurring in mountain terrain can be predicted with such a model, while the specific precipitation features are identifiable and quantifiable from a combination of time-sequenced infrared and less frequent SSM/I images obtained from METEOSAT and DMSP satellites, respectively.