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|Conroy, B. J., Steinberg, D. K., Stukel, M. R., Goes, J. I., & Coles, V. J. (2016). Meso- and microzooplankton grazing in the Amazon River plume and western tropical North Atlantic. Limnol. Oceanogr., 61(3), 825–840.|
|Cornillon, P., Adams, J., Blumenthal, M. B., Chassignet, E., Davis, E., Hankin, S., et al. (2009). NVODS and the Development of OPeNDAP. Oceanog., 22(2), 116–127.|
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.
Culin, J. C. (2006). Wintertime ENSO Variability in Wind Direction Across the Southeast United States. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Changes in wind direction in association with the phases of the El Niño-Southern Oscillation (ENSO) are identified over the Southeast region of the United States during the winter season (December-February). Wind roses, which depict the percentage of time the wind comes from each direction and can graphically identify the prevailing wind, are computed according to a 12-point compass for 24 stations in the region. Unfolding the wind rose into a 12-bin histogram visually demonstrates the peak frequencies in wind direction during each of the three (warm, cold and neutral) phases of ENSO. Normalized values represent the number of occurrences (counts) per month per ENSO phase, and comparison using percent changes illustrates the differences between phases. Based on similarities in wind direction characteristics, regional topography and results from a formal statistical test, stations are grouped into five geographic regions, with a representative station used to describe conditions in that region. Locations in South Florida show significant differences in the frequencies in wind direction from easterly directions during the cold phase and northerly directions during the warm phase. North Florida stations display cold phase southerly directions, and westerly and northerly directions during the warm phase, both of which are significant for much of the winter. Coastal Atlantic stations reveal winds from westerly directions for both phases. The Piedmont region demonstrates large variability in wind direction due to the influence from the Appalachian Mountains, but generally identifies warm phase and cold phase winds with more zonal influences rather than just from south or north. The Mountainous region also indicates southerly cold phase winds and northerly warm phase winds, but also reveals less of an influence from ENSO or significantly different distributions. Comparisons between observed patterns and those obtained using the NCEP/NCAR Reanalysis data reveal how the model-derived observations resolve the ENSO influence on surface wind direction at selected locations. Overall, resolution of the strength of the signals is not achieved, though the depiction of the general pattern is fair at two of the three locations. Connections between the synoptic flow and surface wind direction are examined via relationships to the storm track associated with the 250 hPa jet stream and sea level pressure patterns during each extreme ENSO phase. Discussion of reasons the NCEP reanalysis illustrates surface wind direction patterns different from those derived from observations is included.
|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.|
|Danabasoglu, G., Yeager, S. G., Bailey, D., Behrens, E., Bentsen, M., Bi, D., et al. (2014). North Atlantic simulations in Coordinated Ocean-ice Reference Experiments phase II (CORE-II). Part I: Mean states. Ocean Modelling, 73, 76–107.|
|Danabasoglu, G., Yeager, S. G., Kim, W. M., Behrens, E., Bentsen, M., Bi, D., et al. (2016). North Atlantic simulations in Coordinated Ocean-ice Reference Experiments phase II (CORE-II). Part II: Inter-annual to decadal variability. Ocean Modelling, 97, 65–90.|
|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.|
Davidson, F., Alvera-Azcárate, A., Barth, A., Brassington, G. B., Chassignet, E. P., Clementi, E., et al. (2019). Synergies in Operational Oceanography: The Intrinsic Need for Sustained Ocean Observations. Front. Mar. Sci., 6.
Abstract: Operational oceanography can be described as the provision of routine oceanographic information needed for decision-making purposes. It is dependent upon sustained research and development through the end-to-end framework of an operational service, from observation collection to delivery mechanisms. The core components of operational oceanographic systems are a multi-platform observation network, a data management system, a data assimilative prediction system, and a dissemination/accessibility system. These are interdependent, necessitating communication and exchange between them, and together provide the mechanism through which a clear picture of ocean conditions, in the past, present, and future, can be seen. Ocean observations play a critical role in all aspects of operational oceanography, not only for assimilation but as part of the research cycle, and for verification and validation of products. Data assimilative prediction systems are advancing at a fast pace, in tandem with improved science and the growth in computing power. To make best use of the system capability these advances would be matched by equivalent advances in operational observation coverage. This synergy between the prediction and observation systems underpins the quality of products available to stakeholders, and justifies the need for sustained ocean observations. In this white paper, the components of an operational oceanographic system are described, highlighting the critical role of ocean observations, and how the operational systems will evolve over the next decade to improve the characterization of ocean conditions, including at finer spatial and temporal scales.
|Davis, S. (2002). Some impacts of SeaWinds data in numerical weather prediction. Master's thesis, Florida State University, Tallahassee, FL.|