P2.34 Structural analysis of SSM/I and TMI overpasses of tropical cyclones from 1987-2008

Thursday, 13 May 2010
Arizona Ballroom 7 (JW MArriott Starr Pass Resort)
Daniel S. Harnos, University of Illinois, Urbana, IL; and S. W. Nesbitt and K. R. Knapp

This study examines variability of convective features within tropical cyclones (TCs) as observed by the Special Sensor Microwave/Imager (SSM/I) from 1988-2008 from the HURSAT-MW dataset complimented with overpasses of the Tropical Rainfall Measuring Mission's Microwave Imager (TMI) from 1999-2008. The ability to infer internal storm microphysical structure through passive microwave sensors such as SSM/I and TMI without obscuration of features by cirrus outflow provides an opportunity to gain insight into how changes in TC convective structure and latent heat release modulate system intensity change.

Using the HURSAT-MW framework, we scale SSM/I and TMI data to a common resolution of 8 km and calculate polarization corrected brightness temperatures at 37 GHz and 85 GHz, in addition to rain rates, for each TC overpass. The overpasses are next linked to the International Best Track for Climate Stewardship for each respective overpass to determine progression of storm intensity and present storm motion. Each of the derived variables is then statistically analyzed as a function of azimuth and radii from the storm center in both true-north and storm-relative coordinate systems. Classifications of overpasses based on TC intensity change are also delineated into categories of rapid intensification (RI), intensifying, steady state, and weakening for further analysis.

We focus on the subset of storms undergoing RI and the differences of this group in comparison with the rest of the dataset in an effort to determine what signals may be present in passive microwave data as a function of storm type, environmental conditions, and oceanic basin. Asymmetries, convective bursts, and eyewall replacement cycles are among the features focused upon. Identification of key features related to intensity change from passive microwave sensors will benefit understanding forecasting of processes related to TC intensity changes.

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