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|Ali, M. M., Nagamani, P. V., Sharma, N., Venu Gopal, R. T., Rajeevan, M., Goni, G. J., et al. (2015). Relationship between ocean mean temperatures and Indian summer monsoon rainfall. Atmos. Sci. Lett., 16(3), 408–413.|
|Bourassa, M. A., Legler, D. M., O'Brien, J. J., & Smith, S. R. (2003). SeaWinds validation with research vessels. J. Geophys. Res., 108(C2).|
|Holbach, H. M., & Bourassa, M. A. (2014). The Effects of Gap-Wind-Induced Vorticity, the Monsoon Trough, and the ITCZ on East Pacific Tropical Cyclogenesis. Mon. Wea. Rev., 142(3), 1312–1325.|
Holbach, H. M., Uhlhorn, E. W., & Bourassa, M. A. (2018). Off-Nadir SFMR Brightness Temperature Measurements in High-Wind Conditions. J. Atmos. Oceanic Technol., 35(9), 1865–1879.
Abstract: Wind and wave-breaking directions are investigated as potential sources of an asymmetry identified in off-nadir remotely sensed measurements of ocean surface brightness temperatures obtained by the Stepped Frequency Microwave Radiometer (SFMR) in high-wind conditions, including in tropical cyclones. Surface wind speed, which dynamically couples the atmosphere and ocean, can be inferred from SFMR ocean surface brightness temperature measurements using a radiative transfer model and an inversion algorithm. The accuracy of the ocean surface brightness temperature to wind speed calibration relies on accurate knowledge of the surface variables that are influencing the ocean surface brightness temperature. Previous studies have identified wind direction signals in horizontally polarized radiometer measurements in low to moderate (0�20 m s−1) wind conditions over a wide range of incidence angles. This study finds that the azimuthal asymmetry in the off-nadir SFMR brightness temperature measurements is also likely a function of wind direction and extends the results of these previous studies to high-wind conditions. The off-nadir measurements from the SFMR provide critical data for improving the understanding of the relationships between brightness temperature, surface wave�breaking direction, and surface wind vectors at various incidence angles, which is extremely useful for the development of geophysical model functions for instruments like the Hurricane Imaging Radiometer (HIRAD).
Keywords: Tropical cyclones; Wind; Air-sea interaction; Microwave observations; Remote sensing; Surface observations
|Morey, S. L., Zavala-Hidalgo, J., & O'Brien, J. J. (2005). The seasonal variability of continental shelf circulation in the northern and western Gulf of Mexico from a high-resolution numerical model. In W. Sturges, & A. Lugo-Fernandez (Eds.), New Developments in the Circulation of the Gulf of Mexico. Geophys. Mongr. Ser., (161).|
Paget, A. C., Bourassa, M. A., & Anguelova, M. D. (2015). Comparing in situ and satellite-based parameterizations of oceanic whitecaps. J. Geophys. Res. Oceans, 120(4), 2826–2843.
Keywords: whitecap fraction; foam fraction; whitecap coverage; breaking waves; actively breaking waves; air-sea interaction processes; in situ whitecap observations scatterometers; QuikSCAT; WindSat; microwave radiometry; passive remote sensing; satellite oceanography
|Subrahmanyam, B., Murty, V. S. N., Sharp, R. J., & O'Brien, J. J. (2005). Air-sea Coupling During the Tropical Cyclones in the Indian Ocean: A Case Study Using Satellite Observations. Pure appl. geophys., 162(8-9), 1643–1672.|
|Weissman, D. E., & Bourassa, M. A. (2011). The Influence of Rainfall on Scatterometer Backscatter Within Tropical Cyclone Environments-Implications on Parameterization of Sea-Surface Stress. In IEEE Transactions on Geoscience and Remote Sensing (Vol. 49, pp. 4805–4814).|
Wentz, F. J., Ricciardulli, L., Rodriguez, E., Stiles, B. W., Bourassa, M. A., Long, D. G., et al. (2017). Evaluating and Extending the Ocean Wind Climate Data Record. IEEE J Sel Top Appl Earth Obs Remote Sens, 10(5), 2165–2185.
Abstract: Satellite microwave sensors, both active scatterometers and passive radiometers, have been systematically measuring near-surface ocean winds for nearly 40 years, establishing an important legacy in studying and monitoring weather and climate variability. As an aid to such activities, the various wind datasets are being intercalibrated and merged into consistent climate data records (CDRs). The ocean wind CDRs (OW-CDRs) are evaluated by comparisons with ocean buoys and intercomparisons among the different satellite sensors and among the different data providers. Extending the OW-CDR into the future requires exploiting all available datasets, such as OSCAT-2 scheduled to launch in July 2016. Three planned methods of calibrating the OSCAT-2 sigmao measurements include 1) direct Ku-band sigmao intercalibration to QuikSCAT and RapidScat; 2) multisensor wind speed intercalibration; and 3) calibration to stable rainforest targets. Unfortunately, RapidScat failed in August 2016 and cannot be used to directly calibrate OSCAT-2. A particular future continuity concern is the absence of scheduled new or continuation radiometer missions capable of measuring wind speed. Specialized model assimilations provide 30-year long high temporal/spatial resolution wind vector grids that composite the satellite wind information from OW-CDRs of multiple satellites viewing the Earth at different local times.
Keywords: Radar cross section; remote sensing; satellite applications; sea surface; wind
|Yu, L., & Jin, X. (2014). Confidence and sensitivity study of the OAFlux multisensor synthesis of the global ocean surface vector wind from 1987 onward. J. Geophys. Res. Oceans, 119(10), 6842–6862.|