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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
|Weihs, R. R., & Bourassa, M. A. (2014). Modeled diurnally varying sea surface temperatures and their influence on surface heat fluxes. J. Geophys. Res. Oceans, 119(7), 4101–4123.|
|Bourassa, M. A., Legler, D. M., & O'Brien, J. J. (1998). High temporal and spatial resolution wind fields from scatterometer observations (G. Staniforth, Ed.).|
Venugopal, T., Ali, M. M., Bourassa, M. A., Zheng, Y., Goni, G. J., Foltz, G. R., et al. (2018). Statistical Evidence for the Role of Southwestern Indian Ocean Heat Content in the Indian Summer Monsoon Rainfall. Sci Rep, 8(1), 12092.
Abstract: This study examines the benefit of using Ocean Mean Temperature (OMT) to aid in the prediction of the sign of Indian Summer Monsoon Rainfall (ISMR) anomalies. This is a statistical examination, rather than a process study. The thermal energy needed for maintaining and intensifying hurricanes and monsoons comes from the upper ocean, not just from the thin layer represented by sea surface temperature (SST) alone. Here, we show that the southwestern Indian OMT down to the depth of the 26 degrees C isotherm during January-March is a better qualitative predictor of the ISMR than SST. The success rate in predicting above- or below-average ISMR is 80% for OMT compared to 60% for SST. Other January-March mean climate indices (e.g., NINO3.4, Indian Ocean Dipole Mode Index, El Nino Southern Oscillation Modoki Index) have less predictability (52%, 48%, and 56%, respectively) than OMT percentage deviation (PD) (80%). Thus, OMT PD in the southwestern Indian Ocean provides a better qualitative prediction of ISMR by the end of March and indicates whether the ISMR will be above or below the climatological mean value.
Keywords: SEA-SURFACE TEMPERATURE; EL-NINO; EQUATORIAL PACIFIC; IMPACT; PREDICTION; ENSO; DIPOLE; REGION; SST
|Bourassa, M. A. (2009). The future of wind measurements from space. Space News, (Nov. 23), 2.|
|Bourassa, M. A., & Hughes, P. J. (2009). Impacts of High Resolution SST Fields on Objective Analyses of Wind Fields, and Practical Constraints Related to Sampling. In International GHRSST User Symposium, GHRSST (2).|
|Weissman, D. E., & Bourassa, M. A. (2008). Measurements of the Effect of Rain-induced Sea Surface Roughness on the Satellite Scatterometer Radar Cross Section. In XXIX General Assembly of the International Union of Radio Science, Union of Radio Science International (Vol. 4).|
|Smith, S. R., Maue, R. N., & Bourassa, M. A. (2008). 'Global Winds', State of the Climate in 2007. Bulletin of the American Meteorological Society, , 532–534.|
|Bourassa, M. A., R. N. Maue, S. R. Smith, P. J. Hughes, and J. Rolph. (2007). Global Winds: State of the Climate in 2006. Bulletin of the American Meteorological Society, 88(6), 135.|
|Bourassa, M. A., Legler, D. M., & O'Brien, J. J. (1996). Comparison of ERS scatterometer winds and IMET observations. In Third Workshop on ERS Applications, IFREMER, June, Brest (pp. 27–42).|