Smith, S. R., Bourassa, M. A., & Sharp, R. J. (1999). Establishing More Truth in True Winds.
J. Atmos. Oceanic Technol., 16(7), 939–952.
Smith, S. R., Briggs, K., Bourassa, M. A., Elya, J., & Paver, C. R. (2018). Shipboard automated meteorological and oceanographic system data archive: 2005-2017.
Geosci Data J, 5(2), 73–86.
Abstract: Since 2005, the Shipboard Automated Meteorological and Oceanographic System (SAMOS) initiative has been collecting, quality-evaluating, distributing, and archiving underway navigational, meteorological, and oceanographic observations from research vessels. Herein we describe the procedures for acquiring ship and instrumental metadata and the one-minute interval observations from 44 research vessels that have contributed to the SAMOS initiative from 2005 to 2017. The overall data processing workflow and quality control procedures are documented along with data file formats and version control procedures. The SAMOS data are disseminated to the user community via web, FTP, and Thematic Real-time Environmental Distributed Data Services from both the Marine Data Center at the Florida State University and the National Centers for Environmental Information, which serves as the long-term archive for the SAMOS initiative. They have been used to address topics ranging from air-sea interaction studies, the calibration, evaluation, and development of satellite observational products, the evaluation of numerical atmospheric and ocean models, and the development of new tools and techniques for geospatial data analysis in the informatics community. Maps provide users the geospatial coverage within the SAMOS dataset, with a focus on the Essential Climate/Ocean Variables, and recommendations are made regarding which versions of the dataset should be accessed by different user communities.
Smith, S. R., Hughes, P. J., & Bourassa, M. A. (2011). A comparison of nine monthly air-sea flux products.
Int. J. Climatol., 31(7), 1002–1027.
Smith, S. R., Lopez, N., & Bourassa, M. A. (2016). SAMOS air-sea fluxes: 2005-2014.
Geosci. Data J., 3(1), 9–19.
Smith, S. R., Servain, J., Legler, D. M., Stricherz, J. N., Bourassa, M. A., & O'Brien, J. J. (2004). In Situ-Based Pseudo-Wind Stress Products for the Tropical Oceans.
Bull. Amer. Meteor. Soc., 85(7), 979–994.
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.
Verschell, M. A., Bourassa, M. A., Weissman, D. E., & O'Brien, J. J. (1999). Ocean model validation of the NASA scatterometer winds.
J. Geophys. Res., 104(C5), 11359–11373.
Villas Bôas, A. B., Ardhuin, F., Ayet, A., Bourassa, M. A., Brandt, P., Chapron, B., et al. (2019). Integrated Observations of Global Surface Winds, Currents, and Waves: Requirements and Challenges for the Next Decade.
Front. Mar. Sci., 6.
Abstract: Ocean surface winds, currents, and waves play a crucial role in exchanges of momentum, energy, heat, freshwater, gases, and other tracers between the ocean, atmosphere, and ice. Despite surface waves being strongly coupled to the upper ocean circulation and the overlying atmosphere, efforts to improve ocean, atmospheric, and wave observations and models have evolved somewhat independently. From an observational point of view, community efforts to bridge this gap have led to proposals for satellite Doppler oceanography mission concepts, which could provide unprecedented measurements of absolute surface velocity and directional wave spectrum at global scales. This paper reviews the present state of observations of surface winds, currents, and waves, and it outlines observational gaps that limit our current understanding of coupled processes that happen at the air-sea-ice interface. A significant challenge for the coming decade of wind, current, and wave observations will come in combining and interpreting measurements from (a) wave-buoys and high-frequency radars in coastal regions, (b) surface drifters and wave-enabled drifters in the open-ocean, marginal ice zones, and wave-current interaction �hot-spots,� and (c) simultaneous measurements of absolute surface currents, ocean surface wind vector, and directional wave spectrum from Doppler satellite sensors.
Vose, R. S., Applequist, S., Bourassa, M. A., Pryor, S. C., Barthelmie, R. J., Blanton, B., et al. (2014). Monitoring and Understanding Changes in Extremes: Extratropical Storms, Winds, and Waves.
Bull. Amer. Meteor. Soc., 95(3), 377–386.
Wallcraft, A. J., Kara, A. B., Barron, C. N., Metzger, E. J., Pauley, R. L., & Bourassa, M. A. (2009). Comparisons of monthly mean 10 m wind speeds from satellites and NWP products over the global ocean.
J. Geophys. Res., 114(D16).