Assimilating NOAA SST data into the BSH operational circulation model for the North and Baltic Seas: Inference about the data

Journal of Marine Systems

Volumn 105-108, Issue , 2012, Pages 152-162

  Losa, Svetlana N ^a   Danilov, Sergey ^a   Schröter, Jens ^a   Nerger, Lars ^a   Mamann, Silvia ^b   Janssen, Frank ^b  

^a ALFRED WEGENER INSTITUTE FOR POLAR AND MARINE RESEARCH   (Germany)

^b FEDERAL MARITIME AND HYDROGRAPHIC AGENCY   (Germany)

Author keywords

Data assimilation; Ensemble Kalman filtering; Operational forecasting

Indexed keywords

BALTIC SEA; CIRCULATION MODELS; DATA ASSIMILATION; ENSEMBLE FORECASTING; ENSEMBLE KALMAN FILTERING; FORECAST IMPROVEMENTS; FORECASTING SYSTEM; IN-SITU DATA; LOCAL TEMPERATURE; MULTIVARIATE DATA; OPERATIONAL FORECASTING; PREDICTION MODEL; ROOT MEAN SQUARE DIFFERENCES; SATELLITE OBSERVATIONS; SEA SURFACE TEMPERATURE (SST); SYSTEMATIC MODELS;

ERROR STATISTICS; FORECASTING; SEA LEVEL; UNCERTAINTY ANALYSIS;

DATA PROCESSING;

AVHRR; DATA ASSIMILATION; ENSEMBLE FORECASTING; KALMAN FILTER; MULTIVARIATE ANALYSIS; NOAA SATELLITE; OCEANIC CIRCULATION; SATELLITE DATA; SEA SURFACE TEMPERATURE;

ATLANTIC OCEAN; BALTIC SEA; NORTH SEA;

EID: 84867335382     PISSN: 09247963     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jmarsys.2012.07.008     Document Type: Article

References (42)

1. Alenius P., Nekrasov A., Myrberg K. Variability of the baroclinic Rossby radius in the Gulf of Finland. Cont. Shelf Res. 2003, 23:563-573.
2. Bertino L., Lisæter K.A. The TOPAZ monitoring and prediction system for the Atlantic and Arctic Oceans. J. Oper. Oceanogr. 2008, 1(2):15-18.
3. Bishop C.H., Etherton B., Majumdar S. Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Weather Rev. 2001, 129:420-443.
4. Brasseur P., Bahurel P., Bertino L., Birol F., Brankart J.M., Ferry N., Losa S., Remy E., Schröter J., Skachko S., Testut C.E., Tranchant B., van Leeuwen P.J., Verron J. Data assimilation for marine monitoring and prediction: the MERCATOR operational assimilation systems and the MERSEA developments. Q. J. R. Meteorol. Soc. 2005, 131(613):3561-3582.
5. Brusdal K., Brankart J.M., Halberstadt G., Evensen G., Brasseur P., van Leeuwen P.J., Dombrowsky E., Verron J. A demonstration of ensemble based assimilation methods with a layered OGCM from the perspective of operational ocean forecasting systems. J. Mar. Syst. 2003, 40-41:253-289.
6. Cummings J., Bertino L., Brasseur P., Fukumori I., Kamachi M., Martin M., Mogensen K., Oke P., Testut C., Verron J., Weaver A. Ocean data assimilation systems for GODAE. Oceanography 2009, 22(3):96-109.
7. Dick S. Operationelles Modellsystem für Nord- und Ostsee. FORUM, Proc. Der Fachtagung 'EDV im Seeverkehr und maritimen Umweltschutz' 1997, 22-25. GAUSS, Bremen.
8. Dick S., Kleine E., Müller-Navarra S.H., Klein H., Komo H. The operational circulation model of BSH (BSHcmod) model description and validation. Berichte des BSH 2001.
9. Donlon C.J., Minnett P., Gentemann C., Nightingale T.J., Barton I.J., Ward B., Murray J. Towards improved validation of satellite sea surface skin temperature measurements for climate research. J. Climate 2002, 15(4):353-369.
10. Evensen G. Sequential data assimilation with a non-linear quasi-geostrophic model using Monte-Carlo methods to forecast error statistics. J. Geophys. Res. 1994, 99:10143-10162.
11. Fennel W., Seifert T., Kayser B. Rossby radii and phase speeds in the Baltic Sea. Cont. Shelf Res. 1991, 11:23-26.
12. Gaspari G., Cohn S.E. Construction of correlation functions in two and three dimensions. Q. J. R. Meteorol. Soc. 1999, 125:723-757.
13. Gentemann C., Donlon C., Stuart-Menteth A., Wentz F. Diurnal signals in satellite sea surface temperature measurements. Geophys. Res. Lett. 2003, 30(3):1140-1143.
14. Ghil M., Malonotte-Rizzoli P. Data assimilation in meteorology and oceanography. Adv. Geophys. 1991, 33:141-266.
15. Gordon N.J., Salmond D.J., Smith A.F.M. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc. F 1993, 140(2):107-113.
16. Hibler W.D. A dynamic/thermodynamic sea ice model. J. Phys. Oceanogr. 1979, 9:815-846.
17. Høyer J.L., She J. Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea. J. Mar. Syst. 2007, 65(1-4):176-189.
18. Hunt B.R., Kostelich E.J., Szunyogh I. Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter. Phys. D 2007, 230:112-126.
19. Janjić T., Nerger L., Albertella A., Schröter J., Skachko S. On domain localization in ensemble based Kalman filter algorithms. Mon. Weather Rev. 2011, 136(7):2046-2060.
20. Jazwinski A. Stochastic Processes and Filtering Theory 1970, Academic Press, New York.
21. Kivman G.A. Sequential parameter estimation for stochastic systems. Nonlinear Process. Geophys. 2003, 10(3):253-259.
22. Kleine E. Das operationalle Modell des BSH für Nord- und Ostsee:Konzeption und Ubersicht. Berichte des BSH, Bremen 1994.
23. Kleine E. A class of hybrid vertical co-ordinates for ocean circulation modelling. Proceedings of the 6th HIROMB Scientific Workshop 2003, 7-15. Petersburg, Morzaschita, St.
24. Kurapov A.L., Foley D., Strub P.T., Egbert G.D., Allen J.S. Variational assimilation of satellite observations in a coastal ocean model off Oregon. J. Geophys. Res. 2011, 116:1-19.
25. Larsen J., Høyer J., She J. Validation of a hybrid optimal interpolation and Kalman filter scheme for sea surface temperature assimilation. J. Mar. Syst. 2007, 65(1-4):122-133.
26. Miyoshi T., Yamane S. Local ensemble transform Kalman filtering with an AGCM at a T159/L48 resolution. Mon. Weather Rev. 2007, 135:3841-3861.
27. Nardelli B.B., Marullo S., Santoleri R. Diurnal variations in AVHRR SST fields: a strategy for removing warm layer effects from daily images. Remote. Sens. Environ. 2004, 95(1):47-56.
28. Nerger L., Danilov S., Hiller W., Schröter J. Using sea level data to constrain a finite-element primitive-equation model with a local SEIK filter. Ocean Dyn. 2006, 56:634-649.
29. Nerger, L., Hiller, W., in press. Software for ensemble-based data assimilation systems-implementation strategies and scalability. Comput. Geosci. http://dx.doi.org/10.1016/j.cageo.2012.03.026.
30. Nerger L., Hiller W., Schröter J. PDAF-the parallel data assimilation framework: experiences with Kalman filtering. Use of high performance computing in meteorology: proceedings of the Eleventh ECMWF Workshop on the Use of High Performance Computing in Meteorology. Singapore: World Scientific, Reading, UK, 25-29 October 2004 2005, 63-83. W. Zwieflhofer, G. Mozdzynski (Eds.).
31. Penduff T., Brasseur P., Testut C.-E., Barnier B., Verron J. A four-year eddy-permitting assimilation of sea-surface temperature and altimetric data in the South Atlantic Ocean. J. Mar. Res. 2002, 60(6):805-833.
32. Pham D.T. Stochastic methods for sequential data assimilation in strongly nonlinear systems. Mon. Weather Rev. 2001, 129:1194-1207.
33. Pham D.T., Verron J., Gourdeau L. Singular evolutive Kalman filters for data assimilation in oceanography. C. R. Acad. Sci. Paris, Earth Planet. Sci. 1998, 326:255-260.
34. Ryder P. GMES fast track marine core service: strategic implementation plan 2007.
35. She J., Jacob L., Høyer J.L., Larsen J. Assessment of sea surface temperature observational networks in the Baltic Sea and North Sea. J. Mar. Syst. 2007, 65:314-335.
36. Stanev E.V., Schulz-Stellenfleth J., Staneva J., Grayek S., Seemann J., Petersen W. Coastal observing and forecasting system for the German Bight-estimates of hydrophysical states. Ocean Sci. 2011, 7:569-583.
37. Storkey D., Blockley E.W., Furner R., Guiavarc'h C., Lea D., Martin M.J., Barciela R.M., Hines A., Hyder P., Siddorn J.R. Forrecasting the ocean state using NEMO: the new FOAM system. J. Oper. Oceanogr. 2010, 3(1):11-15.
38. Stuart-Menteth A.C., Robinson I.S., Donlon C.J. Sensitivity of the diurnal warm layer to meteorological fluctuations Part 2: a new parameterisation for diurnal warming. J. Atmos. Ocean Sci. 2004, 10:209-234.
39. Testut C.-E., Brasseur P., Brankart J.-M., Verron J. Assimilation of sea-surface temperature and altimetric observations during 1992-1993 into an eddy permitting primitive equation model of the North Atlantic Ocean. J. Mar. Syst. 2003, 40-41:291-316.
40. van Leeuwen P.J. Particle filtering in geophysical systems. Mon. Weather Rev. 2009, 137(12):4089-4098.
41. van Leeuwen P.J. Nonlinear data assimilation in geosciences: an extremely efficient particle filter. Q. J. R. Meteorol. Soc. 2010, 136.
42. van Leeuwen P.J., Evensen G. Data assimilation and inverse methods in terms of probabilistic formulation. Mon. Weather Rev. 1996, 124:2898-2913.