Bourassa, M. A., H. Bonekamp, P. Chang, D. Chelton, J. Courtney, R. Edson, J. Figa, Y. He, H. Hersbach, K. Hilburn, Z. Jelenak, T. Lee, W. T. Liu, D. Long, K. Kelly, R. Knabb, E. Lindstorm, W. Perrie, M. Portabella, M. Powell, E. Rodriguez, D. Smith, A. Stoffelen, V. Swail, F. Wentz. (2010). Remotely Sensed Winds and Wind Stresses for Marine Forecasting and Ocean Modeling. In D. D.E. and Stammer Harrison J. Hall (Ed.), Proceedings of OceanObs'09: Sustained Ocean Observations and Information for Society (Vol. 2).
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., S. T. Gille, and C. A. Clayson. (2010). Surface Fluxes: Challenges for High Latitudes: Workshop report from the U.S. CLIVAR High Latitudes Surface Flux Working Group. U.S. CLIVAR Variations , 8 (1), 7,14.
Cronin, M. F., Gentemann, C. L., Edson, J., Ueki, I., Bourassa, M., Brown, S., et al. (2019). Air-Sea Fluxes With a Focus on Heat and Momentum. Front. Mar. Sci. , 6 .
Abstract: Turbulent and radiative exchanges of heat between the ocean and atmosphere (hereafter heat fluxes), ocean surface wind stress, and state variables used to estimate them, are Essential Ocean Variables (EOVs) and Essential Climate Variables (ECVs) influencing weather and climate. This paper describes an observational strategy for producing 3-hourly, 25-km (and an aspirational goal of hourly at 10-km) heat flux and wind stress fields over the global, ice-free ocean with breakthrough 1-day random uncertainty of 15 W m–2 and a bias of less than 5 W m–2. At present this accuracy target is met only for OceanSITES reference station moorings and research vessels (RVs) that follow best practices. To meet these targets globally, in the next decade, satellite-based observations must be optimized for boundary layer measurements of air temperature, humidity, sea surface temperature, and ocean wind stress. In order to tune and validate these satellite measurements, a complementary global in situ flux array, built around an expanded OceanSITES network of time series reference station moorings, is also needed. The array would include 500–1000 measurement platforms, including autonomous surface vehicles, moored and drifting buoys, RVs, the existing OceanSITES network of 22 flux sites, and new OceanSITES expanded in 19 key regions. This array would be globally distributed, with 1–3 measurement platforms in each nominal 10° by 10° box. These improved moisture and temperature profiles and surface data, if assimilated into Numerical Weather Prediction (NWP) models, would lead to better representation of cloud formation processes, improving state variables and surface radiative and turbulent fluxes from these models. The in situ flux array provides globally distributed measurements and metrics for satellite algorithm development, product validation, and for improving satellite-based, NWP and blended flux products. In addition, some of these flux platforms will also measure direct turbulent fluxes, which can be used to improve algorithms for computation of air-sea exchange of heat and momentum in flux products and models. With these improved air-sea fluxes, the ocean’s influence on the atmosphere will be better quantified and lead to improved long-term weather forecasts, seasonal-interannual-decadal climate predictions, and regional climate projections.
Curry, J. A., Bentamy, A., Bourassa, M. A., Bourras, D., Bradley, E. F., Brunke, M., et al. (2004). Seaflux. Bull. Amer. Meteor. Soc. , 85 (3), 409–424.
Daneshgar Asl, S., Dukhovskoy, D. S., Bourassa, M., & MacDonald, I. R. (2017). Hindcast modeling of oil slick persistence from natural seeps. Remote Sensing of Environment , 189 , 96–107.
Davis, S. R., Bourassa, M. A., Atlas, R., Ardizzone, J., Brin, E., O'Brien, J. J., et al. (2003). Near-realtime sea surface pressure Fields from NASA's SeaWinds scatterometer and their impact in NWP (H. Ritchie, Ed.). CAS/JSC Working Group on Numerical Experimentation, Research Activities in Atmospheric and Oceanic Modeling. Geneva, Switzerland: World Meteorological Organization.
DiNapoli, S. M., Bourassa, M. A., & Powell, M. D. (2012). Uncertainty and Intercalibration Analysis of H*Wind. J. Atmos. Oceanic Technol. , 29 (6), 822–833.
Dukhovskoy, D. S., Bourassa, M. A., Petersen, G. N., & Steffen, J. (2017). Comparison of the ocean surface vector winds from atmospheric reanalysis and scatterometer-based wind products over the Nordic Seas and the northern North Atlantic and their application for ocean modeling. J. Geophys. Res. Oceans , 122 (3), 1943–1973.
Fairall, C. W., Barnier, B., Berry, D.I, Bourassa, M.A., Bradley, E.F., Clayson, C.A., de Leeuw, G., Drennan, W.M., Gille, S.T., Gulev, S.K., Kent, E.C., McGillis, W.R., Quartly, G.D., Ryabinin, V., Smith, S.R., Weller, R.A., Yelland, M.J. and Zhang, H-M. (2010). Observations to Quantify Air-Sea Fluxes and Their Role in Climate Variability and Predictability. In D.(eds.) D.E. and Stammer Harrison J. Hall (Ed.), Proceedings of OceanObs'09: Sustained Ocean Observations and Information for Society, Vol. 2 (pp. 299–313). European Space Agency.
