113 research outputs found

    Volume, Heat and Salt Transport in the North-Eastern Bering Sea During 2007-2010 Derived Through the 4dvar Data Assimilation of In-Situ and Satellite Observations

    Get PDF
    The rich collection of BEST-BSIERP observations and other sources of data provide an excellent opportunity for synthesis through modeling and data assimilation to improve our understanding of changes in physical forcings of the Bering ecosystem in response to climate change. Assimilating data of different origins, which may be sparse in space and time, is difficult using simple algorithms (traditional optimal interpolation, correlation analysis etc.). The 4Dvar approach is effective for performing spatiotemporal interpolation of sparse data via interpolation (covariance) functions with scales based on ocean dynamics (Bennett, 2002).NSF Arctic Progra

    Model-data synthesis and high resolution simulation of the Bering Sea

    Get PDF
    The Bering Sea is the source of over 50% of the total US fish catch and the home to immense populations of birds and marine mammals. This extraordinarily productive ecosystem is vulnerable to climate regime shifts that have occurred over the past decades. These regime shifts are closely linked to warming and cooling of the atmosphere and ocean, and the coincident retreat or expansion of the sea ice cover with strong interannual and decadal variability. Here we investigate changes in the Bering ice/ocean system in recent years. One of key tools for this investigation is the Bering Ecosystem STudy ice-ocean Modeling and Assimilation System (BESTMAS) for synthesis and modeling of the Bering ice/ocean system

    Towards an integrated observation and modeling system in the New York Bight using variational methods. Part I : 4DVAR data assimilation

    Get PDF
    Author Posting. © The Author(s), 2010. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Ocean Modelling 35 (2010): 119-133, doi:10.1016/j.ocemod.2010.08.003.Four-dimensional Variational data assimilation (4DVAR) in the Regional Ocean Modeling System (ROMS) is used to produce a best-estimate analysis of ocean circulation in the New York Bight during spring 2006 by assimilating observations collected by a variety of instruments during an intensive field program. An incremental approach is applied in an overlapped cycling system with 3-day data assimilation window to adjust model initial conditions. The model-observation mismatch for all observed variables is reduced substantially. Comparisons between model forecast and independent observations show improved forecast skill for about 15 days for temperature and salinity, and 2 to 3 days for velocity. Tests assimilating only certain subsets of the data indicate that assimilating satellite sea surface temperature improves the forecast of surface and subsurface temperature but worsens the salinity forecast. Assimilating in situ temperature and salinity from gliders improves the salinity forecast but has little effect on temperature. Assimilating HF-radar surface current data improves the velocity forecast by 1-2 days yet worsens the forecast of subsurface temperature. During some time periods the convergence for velocity is poor as a result of the data assimilation system being unable to reduce errors in the applied winds because surface forcing is not among the control variables. This study demonstrates the capability of 4DVAR data assimilation system to reduce model-observation mismatch and improve forecasts in the coastal ocean, and highlights the value of accurate meteorological forcing.This work was funded by National Science Foundation grant OCE-0238957

    Physical synthesis of iron oxide nanoparticles and their biological activity in vivo

    Get PDF
    The physical synthesis of iron oxide nanoparticles obtained from the vapor phase using the electron beam physical vapor deposition method is considered. The results of studying the structure of porous condensates of iron-sodium chloride compound, chemical and phase compositions, as well as nanoparticles size are presented. With a rapid removal from vacuum, iron nanoparticles are oxidized in the air to magnetite. In the initial state, they have signi¬cant sorption capacity with respect to oxygen and moisture, therefore, with further heating in the air, the porous condensate mass decreases up to the temperature 650°C, primarily due to the desorption of physically sorbed moisture. Physically adsorbed oxygen participates in oxidation of Fe3O4–Fe2O3 in the range of 380–650°C. An increase in condensation temperature is accompanied by an increase of nanoparticle size, as a result of which the total surface area of nanoparticles is signicantly reduced, and, consequently, their sorption capacity is decreased. Even without stabilization, such nanoparticles studied as ex tempore prepared aqueous dispersion have characteristic anti-anemic effect in the laboratory animals that can be used in medicine
    corecore