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In the Baltic modelling research, assin.ilation techniques were developed with advance. They were-concerned to model assimilated basic parameters and observed them directly. In present paper, the most important was the assimilation of surface information and its projection deep into temperature and salinity fields. In oceanic investigations altimetry viewed from satellite was the sea level changes projected far inside and predetermined surface-to-subsurface correlations. To obtain improved modelled hydrophysical fields, sea level variations measured at coastal gauges and efficient data assimilation were taken into account. A data assimilation algorithm has been developed and used in conjunction with a three-dimensional baroilinic model of the Baltic Sea. It was based on a time and space weighted nudging technique. The sea level data were inserted continuously by updating the model solution every time step. Several sensitivity experiments with different values of time and spatial weighting scales were performed. In first series of experiments, only sea level data (SL) were assimilated. In the next simulations, seawater temperature (SWT) and seawater salinity (SWS) related directly to SL were assimilated. To evaluate the effectiveness of the assimilation scheme, modelled sea level series and vertical profiles of seawater temperature and salinity in selected coastal gauges in the Gdansk Basin were examined. Evidently low but statistically essential correlation coefficients indicated nonlinear character of vertical mixing and transfer processes. Decreasing errors obtained while comparing the model results to a control case without assimilation confirmed a real transfer of surface information deep and usefulness of such approach in modelling.
Czasopismo
Rocznik
Tom
Strony
105--128
Opis fizyczny
bibliogr. 43 poz., tab., wykr.
Twórcy
autor
autor
- Institute ofOceanology of PAS Ul. Powstańców Warszawy 55, 81-712 Sopot, Poland Institute of Oceanography, University of Gdansk Al. Marszałka Piłsudskiego 46, 81-378 Gdynia, Poland, janj@ocean.univ.gda.pl
Bibliografia
- Belousov, S. L., Gandin, L. S., & Mashovich S. A., (1971). Computer Processing of Meteorological Data. Israel Program for Scientific Translations, 1-210.
- Blumberg, A. F. & Mellor, G. L. (1987). A description of a three-dimensional coastal ocean circulation model. [in:] Three-Dimensional Coastal Ocean Models, N. Heaps (ed.), Am. Geophys. Union, 1-208.
- Bock, K.-H. (1971), Monatskarten des Salzgehaltes der Ostsee, dargestellt fur verschiedene Tiefenhorizonte. Dt. hydrogr. Z., Erg.-H. R. B., No. 12, Hamburg. 1-148.
- Daley, R. (1991). Atmospheric data analysis. Cambridge University Press, 1-457.
- Emery, W.J. & Thomson R.E. (1997). Data and Analysis Methods in Physical Oceanography. Pergamon Press, Oxford, 1-634.
- Ezer, T. & Mellor, G.L. (1997). Data assimilation experiments in the Gulf Stream Region: how usefull are sattelite - derived surface data for nowcasting the subsurface field ? Journal of Atmospheric and Oceanic Technology, 14, 1379 - 1391.
- Ezer, T. & Mellor, G.L. (1994). Continuous assimilation of geosat data into three-dimensional primitive equation gulf stream model. Journal of Physical Oceanography, 24, 832 - 847.
- Fischer, M. & Latif, M. (1995), Assimilation of temperature and sea level observations into a primitive equation model of the tropical Pacific. J. Mar. Sys., 6, 31-46.
- Funkquist, L. (2004). Development of an analysis and data assimilation scheme in HIROMB, 7th HIROMB-Scientific Workshop, December 7-9, 2004, Helsinki.
- Funkquist, L. (2006). Data assimilation with the OI method. 9th HIROMB Scientific Workshop, 28-31 August 2006, SMHI, Gothenburg.
- Funkquist L. & Pemberton P. (2006). Development of an analysis and assimilation scheme for HIROMB, 9th HIROMB Scientific Workshop, 28-31 August 2006, SMHI, Gothenburg.
- Funkquist, L. (2006a). An Operational Data Assimilation System for the Baltic Sea. Report of the EU programme ODON,1-5.
- Ghil, M. (1989). Meteorological data assimilation for oceanographers. Part I: description and theoretical framework. Dyn. Atmos. Oceans, 13, 171-218
- Ghil, M. & Malanotte-Rizzoli P. (1991). Data assimilation in meteorology and oceanography. Adv. Geophys., 33, 141-266.
- Høyer, J.L. & She J. (2007), Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea. J. Mar. Sys., 65, 186 - 189.
- Høyer, J.L. & She, J. (2006). A new method to reduce noise on satellite sea surface temperature observations, Proceedings of EuroGOOS 4th Conference, 2005. Brest. 441-448.
- Jankowski, A. (2002a), Application of a coordinate baroclinic model to the Baltic Sea. Oceanologia, 44 (1), 59-80.
- Jankowski, A. (2002b). Variability of coastal water hydrodynamics in the southern Baltic - hindcast modelling of an upwelling event along the Polish coast. Oceanologia, 44 (4), 395-418.
- Kantha, L. & Clayson, C. (2000). Numerical models of oceans and oceanic processes. International Geophysical Series, 66, 1-750. Academic Press.
- Kundu, P.K. & Allen, J.S. (1976). Some three-dimensional characteristics of low-frequency current fluctuationbs near the Oregon coast. J. Phys. Oceanogr., 6, 181-199.
- Large, W. G. & Pond, S. (1981). Open ocean momentum flux measurements in moderate to strong winds. J. Phys. Oceanogr., 11, 324-336.
- Larsen, J., Høyer, J.L. & She, J. (2007). Validation of a hybrid optimal interpolation and Kalman filter scheme for sea surface temperature assimilation. J. Mar. Syst., 65, 122-133.
- Launiainen, J. & Vihma, T. (1990). Derivation of turbulent surface fluxes - an iterative flux - profile method allowing arbitrary observing heights. Environmental Software, 5, 3, 113-124.
- Le Dimet, F.X. & Talagrand O. (1986). Variational algorithms for analysis and assimilation of meteorological observations. Tellus, 38A, 97-110.
- Lehmann, A. (1995). A three-dimensional baroclinic eddy-resolving model of the Baltic Sea. Tellus, 47A, 5:2, 1013-1031.
- Lehmann, A. & Hinrichsen H. H. (2000). On the wind driven and thermohaline circulation of the Baltic Sea. Phys. Chem. Erth (B), 25, 2, 183 -189.
- Lenz, W. (1971). Monatskarten der Temperatur der Ostsee dargestellt fur verschiedene Tiefenhorizonte. Dt. hydrogr. Z., Erg.-H. R. B., 11, Hamburg, 1-148.
- Li, J.G., Killworth P.D. & Smeed A. (2003). Response of an eddy-permitting ocean model to the assimilation of sparse in situ data. J. Geophys. Res., 108, C4, 3111, doi:10.1029/2001JC001033.
- Malanotte-Rizoli, P. & Young R.E. (1992). How useful are localized clusters of traditional oceanographic measurements for data assimilation? Dyn. Atm. Ocean., 17, 23-61.
- Malanotte-Rizoli P. & Young R.E. (1995). Assimilation of global versus local data sets into a regional modelof the Gulf Stream. J. Geophys. Res., 100, 24773-24796.
- Meier, H.E.M. (2004). Variational data assimilation using the adjoint method: an application for the Baltic Sea.. Workshop on: Sea waves and ocean current modelling in marine ecology with emphasis on data assimilation, at the Marine Station in Hel, University of Gdansk.
- Meier, H.E.M. & Krauss, W. (1994). Data assimilation into a numerical model of the Baltic Sea using the adjoint method. Proceedings of the 19th Conference of the Baltic Oceanographers, Sopot, Poland, 447-458.
- Mellor, G.L.& Ezer T. (1994). A Gulf Stream model and an altimetry assimilation scheme. J. Geophys. Res., 96, 8779-8795.
- Oey, L.-Y. & Chen P. (1992). A model simulation of circulation in the northeast Atlantic shelves and seas. J. Geophys. Res., 97, 20087-20115.
- Pemberton, P. & Funkquist, L. (2006). Data Assimilation Experiments in the Baltic Sea. 9th HIROMB Scientific Workshop 2831, August 2006, SMHI, Gothenburg.
- Robinson, A.R., Lermusiaux P.F.J. & Sloan N.Q. (1998). Data assimilation. In: The Sea, 10, John Wiley and Sons, New York, NY, 541-594.
- Sarmiento, J.T. & Bryan K. (1982). An ocean transport model for the North Atlantic. J. Geophys. Res., 87, 394-408.
- She, J., Høyer J. L.& Larsen J. (2007). Assessment of sea surface temperature observational networks in the Baltic Sea and North Sea. J. Mar. Sys., 65, 314-335.
- She, J. & Nakamoto S., (1996). Spatial Sampling study for tropical Pacific with observed sea surface temperature. J. Atmo. Ocean. Tech., 13, 1189-1201.
- Seifert, T. & Kayser, B. (1995). A high resolution spherical grid topography of the Baltic Sea. Meereswissenschaftliche Berichte, 9, Institut fur Ostseeforschung, Warnemunde, 72-88.
- Sokolov, A., Andrejev, O., Wulff F. & Medina M.R. (1997), The Data Assimilation System for Data Analysis in the Baltic Sea. Systems Ecology Contributions. 3, Stockholm University, 1 - 66.
- Sørensen, J.V.T., Madsen H. & Madsen H. (2003). Data assimilation in a North Sea - Baltic Sea forecasting system: theoretical aspects. Geophysical Research Abstracts, 5, 03482.
- Tynana, C.T., Ainleyb D.G., Barthc J.A., Cowlesc T.J., Piercec S.D. & Spearb L.B. (2005). Cetacean distributions relative to ocean processes in the northern California Current Syste. Deep-Sea Research II, 52, 145-167.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS5-0008-0031
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