27th Conference on Hurricanes and Tropical Meteorology

P4.19

Validation of QuikSCAT wind retrievals in hurricanes

Christopher C. Hennon, Univ. of North Carolina, Asheville, NC; and D. G. Long and F. J. Wentz

QuikSCAT wind speed retrievals in over a dozen tropical cyclones are validated against Stepped Frequency Microwave Radiometer (SFMR) observations and wind speed analyses (H*Wind). Four QuikSCAT retrieval products are evaluated: 25.0 km resolution Near Real Time (NRT), 12.5 km NRT, 2.5 km Ultra Hi-Resolution, and an Ultra Hi-Res product empirically adjusted for hurricane environments.

Tropical cyclones (TCs) present a difficult challenge for active scatterometry due to heavy rain and wind speeds beyond the upper design limits of retrieval algorithms. Rain effects have qualitatively been well documented but little validation work has been performed due to difficulty in obtaining reliable surface truth measurements in TCs. This study exploits SFMR observations taken from National Oceanic and Atmospheric Administration (NOAA) research aircraft from within tropical cyclones. In addition, comparisons are also made to H*Wind analyses. H*Wind is a data assimiliation and analysis tool that produces a gridded (~6 km resolution) surface wind speed snapshot. Recent Atlantic Basin tropical cyclone seasons were searched for collocations of QuikSCAT passes with available SFMR data.

Results will be presented that show a consistent low bias in the QuikSCAT retrievals near the tropical cyclone core. This suggests that attenuation of the signal by rain dominates over any enhanced backscatter. Retrievals outside of the tropical cyclone core are remarkably accurate. A statistical comparison of the four retrieval algorithms shows that the low resolution NRT product has approximately a 40% lower root mean square error overall. However, there are large differences in algorithm performance from case to case.

Poster Session 4, Tropical Cyclone Structure and Intensity
Tuesday, 25 April 2006, 1:30 PM-5:00 PM, Monterey Grand Ballroom

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