P1.20 Understanding the internal structure of Tropical Cyclones through synergistic use of Cloud Profiling Radar and Microwave Radiometer data

Thursday, 12 November 2009
James Rios, NOAA/CREST, New York, NY; and Z. J. Luo

Tropical Cyclones or hurricanes are a major environmental hazard. Every season thousands of lives are threatened and countries are forced to manage billions of dollars in damage to infrastructure and vital services. Building upon my Bachelor's thesis supervised by Prof. Johnny Luo, my Master's research will continue to focus on characterization of hurricane structure using data from multiple platforms. In my Bachelor's thesis, I correlated AMSR-E 89-GHz brightness temperature (which is sensitive to ice particles lofted by convection) with CloudSat Cloud Profiling Radar (CPR) echo characteristics. All 2008 Tropical Cyclones were analyzed through MATLAB using HDF-EOS data produced by the Naval Research Lab and CloudSat Data Processing Center. This provided a multi-platform characterization of each hurricane's structure, which offered a comprehensive view not possible through the use of any single satellite alone. Scatter plots of 89-GHz Brightness Temperature (Tb) vs. radar echo top height clearly revealed a common pattern of decreasing Tb with increasing echo top height for all subjects surveyed. Now, other AMSR-E channels (e.g., 37 GHz sensitive to liquid particles) are also being compared with CloudSat data, yielding extra information about hurricane structures. Statistical analysis such as multiple linear regression is being explored to quantify the relationship. The synergistic use of AMSR-E and CloudSat data may offer an opportunity for us to extrapolate the structure of the hurricane beyond the curtain-like swath of the CloudSat radar (~ 1.3 km wide) to a much wider area. Furthermore, the joint AMSR-E/CloudSat analysis may also prove useful in quickly assessing the intensity of a Hurricane, leading to new insights to the lifecycle of a Tropical Cyclone.

Presentation Type: Poster Technical Area: Remote Sensing and Satellites Affiliation: Graduate Student Center Affiliation: CREST-CCNY

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