Obtaining Accurate Ocean Surface Winds in Hurricane Conditions: A Dual-Frequency Scatterometry Approach

• Published in: IEEE transactions on geoscience and remote sensing Vol. 48; no. 8; pp. 3101 - 3113
• Main Authors: Stiles, Bryan W, Hristova-Veleva, Svetla M, Dunbar, R Scott, Chan, Samuel, Durden, Stephen L, Esteban-Fernandez, Daniel, Rodriguez, Ernesto, Poulsen, W Lee, Gaston, Robert W, Callahan, Philip S
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.08.2010; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied geophysics; Artificial neural networks; Computer simulation; Earth sciences; Earth, ocean, space; Exact sciences and technology; Hurricanes; Internal geophysics; Marine geology; Mathematical analysis; Mathematical models; Missions; Neural networks; ocean surface winds; Oceans; Pollution measurement; Precipitation; Predictive models; radar; Radar measurements; Rain; Scatterometers; scatterometry; Sea measurements; Sea surface; Studies; Wind forecasting; models; atmospheric precipitation; scatterometry; simulation; neural networks; remote sensing; contamination; frequency; radar; ocean; hurricanes; winds; ocean surface winds; radar methods; storms; high resolution
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2010.2045765

We describe a method for retrieving winds from colocated Ku- and C-band ocean wind scatterometers. The method utilizes an artificial neural network technique to optimize the weighting of the information from the two frequencies and to use the extra degrees of freedom to account for rain contamination in the measurements. A high-fidelity scatterometer simulation is used to evaluate the efficacy of the technique for retrieving hurricane force winds in the presence of heavy precipitation. Realistic hurricane wind and precipitation fields were simulated for three Atlantic hurricanes, Katrina and Rita in 2005 and Helene in 2006, using the Weather Research and Forecasting model. These fields were then input into a radar simulation previously used to evaluate the Extreme Ocean Vector Wind Mission dual-frequency scatterometer mission concept. The simulation produced high-resolution dual-frequency normalized radar cross-section (NRCS) measurements. The simulated NRCS measurements were binned into 5 x 5 km wind cells. Wind speeds in each cell were estimated using an artificial neural network technique. The method was shown to retrieve accurate winds up to 50 m/s even in intense rain.