88th Annual Meeting (20-24 January 2008)

Wednesday, 23 January 2008: 11:00 AM
Analysis of the tropical cyclone environment using COSMIC data
204 (Ernest N. Morial Convention Center)
Pat J. Fitzpatrick, Mississippi State Univ., Stennis Space Center, MS; and C. Hill, Y. Lau, and D. Stettner
Recently (April 14, 2006), a new constellation of satellites was successfully launched. These low-orbitting satellites will provide real-time data globally. Called the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC), these satellites will provide temperature, moisture, and refractivity profiles (Anthes, Ricken, and Kuo 2000; Cucurull et al. 2006). COSMIC relies on a technology known as radio occultation. As radio signals from GPS satellites pass through the atmosphere, the paths of the signals are bent and their progress slowed. The rate of these changes depends on atmospheric density along the signal path. COSMIC's low-Earth-orbiting (LEO) satellites take advantage of this effect by intercepting the GPS radio signals just above Earth's horizon and precisely measuring the bend and signal delay along the signal path. These parameters can then be used to estimate temperature and moisture information from refractivity. Because radio signals can pass through thick cloud cover and precipitation, weather conditions will not interfere with data gathering, as often occurs with remote sensing platforms. Therefore, this technology could be very useful to the monitoring of tropical cyclones. More information is available at: http://www.cosmic.ucar.edu.

This study will investigate COSMIC's capabilities in studying the tropical cyclone environment. COSMIC's temperature, moisture, and refractivity values will be validated against Terra/Aqua data, and against reconnaissance data for the 2006 and 2007 hurricane seasons. The usefullness of COSMIC in SAL events will also be examined and compared against CALIPSO data. The ability of COSMIC to observe critical regions inaccessible to other platforms will be assessed. This analysis will also provide observation error characteristics critical to future data assimilation work.

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