Ali, M. M., Bourassa, M. A., Bhowmick, S. A., Sharma, R., & Niharika, K. (2016). Retrieval of Wind Stress at the Ocean Surface From AltiKa Measurements. IEEE Geosci. Remote Sensing Lett. , 13 (6), 821–825.
Nyadjro, E. S., Jensen, T. G., Richman, J. G., & Shriver, J. F. (2017). On the Relationship Between Wind, SST, and the Thermocline in the Seychelles-Chagos Thermocline Ridge. IEEE Geosci. Remote Sensing Lett. , 14 (12), 2315–2319.
Bourassa, M. A., & Weissman, D. E. (2003). The development and application of a sea surface stress model function for the QuikSCAT and ADEOS-II SeaWinds scatterometers. In IEEE International Symposium on Geoscience and Remote Sensing (IGARSS) (pp. 239–241).
Dukhovskoy, D., & Bourassa, M. (2011). Comparison of ocean surface wind products in the perspective of ocean modeling of the Nordic Seas. In OCEANS 2011 .
Hoffman, R. N., Privé, N., & Bourassa, M. (2017). Comments on “Reanalyses and Observations: What's the Difference?”. Bull. Amer. Meteor. Soc. , 98 (11), 2455–2459.
Abstract: Are there important differences between reanalysis data and familiar observations and measurements? If so, what are they? This essay evaluates four possible answers that relate to: the role of inference, reliance on forecasts, the need to solve an ill-posed inverse problem, and understanding of errors and uncertainties. The last of these is argued to be most significant. The importance of characterizing uncertainties associated with results—whether those results are observations or measurements, analyses or reanalyses, or forecasts—is emphasized.
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).
Bourassa, M. A., & McBeth Ford, K. (2010). Uncertainty in Scatterometer-Derived Vorticity. J. Atmos. Oceanic Technol. , 27 (3), 594–603.
Nof, D., Jia, Y., Chassignet, E., & Bozec, A. (2011). Fast Wind-Induced Migration of Leddies in the South China Sea. J. Phys. Oceanogr. , 41 (9), 1683–1693.
Zheng, Y., Bourassa, M. A., & Hughes, P. (2013). Influences of Sea Surface Temperature Gradients and Surface Roughness Changes on the Motion of Surface Oil: A Simple Idealized Study. J. Appl. Meteor. Climatol. , 52 (7), 1561–1575.
Zheng, Y., Zhang, R., & Bourassa, M. A. (2014). Impact of East Asian Winter and Australian Summer Monsoons on the Enhanced Surface Westerlies over the Western Tropical Pacific Ocean Preceding the El Niño Onset. J. Climate , 27 (5), 1928–1944.
