Adaptation of the Three-Dimensional Variational Data Assimilation Scheme for Water Temperature and Salinity Assimilation into the Operational System of the Gulf of Finland (GULFOOS)

трехмерная вариационная ассимиляция данных, моделирование, оперативная океанография, Финский залив

1. Neelov I.A. et al. A simulation of the Gulf of Finland ecosystem with 3-D model // Proc. Estonian Academy of Science: Biology, Ecology. 2003. V.52, N 3. P.346–359.

2. Оперативная океанографическая система Финского залива GULFOOS. Российский Государственный гидрометеорологический университет. http://gulfoos.rshu.ru/.

3. Millero F., Kremling I. The densities of the Baltic Sea deep waters // Deep Sea Research. 1976. V.23. P.611– 622.

4. Funkquist L. HIROMB, an operational eddy-resolving model for the Baltic Sea // Bulletin of the Maritime Institute in Gdansk. 2001. V.28, N 2. P.7–16.

5. High Resolution Limited Area Modelling project HIRLAM. http://hirlam.org.

6. Проблемы исследования и математического моделирования экосистемы Балтийского моря: Международный проект «Балтика». Вып.5: Экосистемные модели. Оценка современного состояния Финского залива. Ч.2: Гидрометеорологические, гидрохимические, гидробиологические, геологические условия и динамика вод Финского залива/ Под ред. И.Н.Давидана и др. СПб.: Гидрометеоиздат, 1997. 449 с.

7. Counillon F., Bertino L. Ensemble Optimal Interpolation: multivariate properties in the Gulf of Mexico // Tellus-A. 2009. V.61. P.296–308.

8. Bertino L., Lisæter K.A. The TOPAZ monitoring and prediction system for the Atlantic and Arctic Oceans // J. of operational oceanography. 2008. V.1, N 2. P.15–18.

9. Brasseur P., Bahurel P., Bertino L. et al. Data assimilation for marine monitoring and prediction: the MERCATOR operational assimilation systems and the MERSEA developments // Quarterly J. of the Royal Meteorolog. Soc. 2005. V.131, N 613. P.3561–3582.

10. Brusdal K., Brankart J.M., Halberstadt G.et al. A demonstration of ensemble based assimilation methods with a layered OGCM from the perspective of operational ocean forecasting systems // J. of Marine Systems. 2003. V.40–41. P.253–289.

11. Nerger L., Danilov S., Hiller W. et al. Using sea level data to constrain a finite-element primitive-equation model with a local SEIK filter // Ocean Dynamics. 2006. V.56. P.634–649.

12. Penduff T., Brasseur P., Testut C.-E. et al. A four-year eddy-permitting assimilation of sea-surface temperature and altimetric data in the South Atlantic Ocean // J. of Marine Res. 2002. V.60, N 6. P.805–833.

13. Testut C.-E., Brasseur P., Brankart J.-M. et al. Assimilation of sea-surface temperature and altimetric observations during 1992–1993 into an eddy permitting primitive equation model of the north Atlantic ocean // J. of Marine Systems. 2003. V.40-41. P.291–316.

14. Li Z., Chao Y., McWilliams J.C.et al. A three-dimensional variational data assimilation scheme for the regional ocean modeling system // J. of Atmosph. and Oceanic Technol. 2008. V.25. P.2074–2090.

15. Mogensen K., Balmaseda M.A., Weaver A.T. et al. NEMOVAR: A variational data assimilation system for the NEMO ocean model // ECMWF Newsletter.2009. V.120. P.17–22.

16. Moore A.M., Arango H.G., Broquet G. et al. The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems, Part I: System overview and formulation // Progress in Oceanography. 2011. V.91. P.34–49, doi:10.1016/j.pocean.2011.05.004.

17. Moore A.M., Arango H.G., Broquet G. et al. The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems: II Performance and application to the California Current system // Progress in Oceanography. 2011. V.91. P.50–73.

18. Moore A.M., Arango H.G., Broquet G. et al. The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems: III Observation impact and observation sensitivity in the California Currentsystem // Progress in Oceanography.2011. V.91. P.74–94.

19. Funkquist L., Pemberton P., Sigg R. Development of an Operational Data Assimilation System for the Baltic Sea // SMHI Reports Oceanography. 2006. 35 p.

20. Funkquist L. An operational data assimilation system for the Baltic Sea // European Operational Oceanography: Present and Future. 2006. Proceedings of the 4th International Conference on EuroGOOS / Eds. H.Dahlin et al. France, Brest. 2006. P.656–660.

21. North Sea-Baltic Sea operational ocean model BSHcmod. Danish Meteorological Institute. http://ocean.dmi.dk/models/bshcmod.uk.php.

22. Zhuang S.Y., Fu W.W., She J.A pre-operational 3-D variational data assimilation system in the North/Baltic Sea // Ocean Sci. Discussion. 2011. V.8. P.1131–1160. doi:10.5194/osd-8-1131-2011.

23. Dobricic S, Pinardi N. An oceanographic three-dimensional variational data assimilation scheme // Ocean Modelling. 2008. V.22. P.89–105.

24. Lorenc A.C. Development of an operational variational assimilation scheme // J. of the Meteorolog. Soc. of Japan. 1997. V.75. P.339–346.

25. Weaver A.T., Vialard J., Anderson D.L.T. Three- and four-dimensional variational assimilation with an ocean general circulation model of the tropical Pacific Ocean. Part 1: formulation, internal diagnostics and consistency checks // Monthly Weather Rev. 2003. V.131. P.1360–1378.

26. Степанюк И.А. Методы измерения морских внутренних волн. СПб.: Изд. РГГМУ, 2002. 138 с.

27. Басова С.Л. и др. // Экологическая обстановка в Санкт-Петербурге и Ленинградской области в 1996 году. Справочно-аналитический обзор. СПб.: Гидрометеоиздат, 1997. C.67−76.

28. Gran V., Pitka¨nen H. Denitrification in estuarine sediments in the eastern Gulf of Finland, Baltic Sea // Hydrobiol. 1999. V.393. P.107–115.