25th Conference on Hurricanes and Tropical Meteorology

Tuesday, 30 April 2002: 2:45 PM
Precipitation and landmass interaction during Hurricane Georges (1998) landfall at Puerto Rico
Craig M. Orndorff, RSMAS/University of Miami, Miami, FL; and S. S. Chen and J. E. Tenerelli
Poster PDF (174.6 kB)
Quantitative precipitation forecasting (QPF) at landfall is a critical tropical cyclone forecast problem. Renewed emphasis has been placed in this area in the past few years following several significant flooding events (e.g., Hurricanes Mitch (1998) and Irene (1999)). The subject is made even more difficult when the affected landmass involves complex terrain, and few studies have examined TC orographic precipitation . The objective of this study is to examine the development and distribution of precipitation during Hurricane Georges (1998) interaction with the orography of Puerto Rico using an explicit numerical model simulation in conjuction with observational data.

Hurricane Georges made landfall on the southeast coast of Puerto Rico on the evening of 21 September. The interaction of Georges with the island terrain resulted in heavy and often localized precipitation as well as a weakening of the storm. The Penn State/NCAR MM5 is employed with a unique storm following coordinate and high resolution (1.67km) inner nest to provide the representation of storm structure and landmass topography necessary to investigate the effects of the island's orography on storm precipitation. Observation of Georges while it traversed the Caribbean was intensive, particularly during its interaction with Puerto Rico. A wealth of aircraft flight level data, dropsondes, land-based and airborne radar, as well as ocean and surface measurements were collected. This abundant observational data will be used to verify model simulated precipitation and storm structure and to further elucidate the physical processes at work. Preliminary model results are encouraging. Georges track and intensity are well represented, and simulated storm structure compares well with observations.

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