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Seminar joint with Oceanography: Satellite Altimeters and Synthetic Aperture Radars: Insights into Ocean Waves
17 October 2018 @ 3:30 pm - 4:30 pm
Dr. Justin E. Stopa
Department of Ocean and Resources Engineering
University of Hawaiʻi at Mānoa
Space-borne satellites are an integral component of Earth observing systems. Ocean surface waves, which have a continuous presence in the ocean, are routinely observed using altimeters and synthetic aperture radars (SAR). Altimeters and SARs are active sensors that operate in the microwave band which allows for continuous monitoring without restrictions related to time of day or cloud cover. In this work, both of these technologies are exploited to study various ocean wave characteristics. There are 30+ and 20+ years of wave observations from altimeters and SAR respectively making them an attractive data source to study climate variability. A project funded by the European Space Agency called the SeaState Climate Change Initiative aims to improve the data quality and homogeneity of these data records by using state-of-the-art processing and calibration techniques. Here, we describe the challenges associated with creating consistent time series from the satellite acquisitions. Difficulties mainly arise from the fact that the quantity and quality of the satellite acquisitions changes in time.
SAR is the unique satellite technology that measures sea surface roughness at high resolution (10s of m). Both wavelength and directional characteristics of ocean waves are readily extracted from the satellite imagery enabling an estimation of “swell” spectra. Here we describe the challenges associated with how geophysical wave parameters are extracted from the SAR imagery. Ocean waves are certainly the ubiquitous features captured in the ocean imagery. However other atmospheric or oceanic phenomena leave their imprint in the sea surface roughness including sea ice, icebergs, atmospheric instabilities, internal waves, biologic slicks, small rain cells, and oceanic fronts. Often each pattern is distinct, so, it is expected that machine learning techniques will be able to automate the classification. Here we will present preliminary results from an automated SAR classification model. Mapping these phenomena on a global scale at regular time intervals will provide an entirely new perspective of the global ocean, and will enable the scientific community to track changes and make connections between ocean phenomena and ecological, weather, and climate phenomena.