Computer Science – Performance
Scientific paper
Aug 2011
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011georl..3815810z&link_type=abstract
Geophysical Research Letters, Volume 38, Issue 15, CiteID L15810
Computer Science
Performance
2
Atmospheric Processes: Data Assimilation, Atmospheric Processes: Mesoscale Meteorology, Atmospheric Processes: Tropical Cyclones
Scientific paper
This study examines a hurricane prediction system that uses an ensemble Kalman filter (EnKF) to assimilate high-resolution airborne radar observations for convection-permitting hurricane initialization and forecasting. This system demonstrated very promising performance, especially on hurricane intensity forecasts, through experiments over all 61 applicable NOAA P-3 airborne Doppler missions during the 2008-2010 Atlantic hurricane seasons. The mean absolute intensity forecast errors initialized with the EnKF-analysis of the airborne Doppler observations at the 24- to 120-h lead forecast times were 20-40% lower than the National Hurricane Center's official forecasts issued at similar times. This prototype system was first implemented in real-time for Hurricane Ike (2008). It represents the first time that airborne Doppler radar observations were successfully assimilated in real-time into a hurricane prediction model. It also represents the first time that the convection-permitting ensemble analyses and forecasts for hurricanes were performed in real-time. Also unprecedented was the on-demand usage of more than 23,000 computer cluster processors simultaneously in real-time.
Gamache John F.
Marks Frank D.
Weng Yonghui
Zhang Fuqing
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