Satellite SST products from the Copernicus Marine Environment Service were tested for data assimilation in the sub-regional marine forecasts. The sub-regional setup of the HBM model was used in the northeastern Baltic, covering also the Gulf of Finland and the Gulf of Riga. Two assimilation methods – successive corrections and optimal interpolation – were implemented on the daily forecasts from April to December 2015. Independent daily FerryBox data from the ship track between Tallinn and Helsinki were used for validation. Higher SST forecast errors of the reference model were found near the shallower northwestern coasts. During the calm heating period in spring and early summer, the reference model produced in these regions too warm waters compared with the satellite and FerryBox observations. Too cold waters, compared to the observations, were modelled during the cooling period from late summer to winter. Although data assimilation reduced these errors, improving the treatment of coastal–offshore exchange in the core forecast model would be useful.
Anding, D. and Kauth, R. 1970. Estimation of sea surface temperature from space. Remote Sens. Environ., 1(4), 217–220.
https://doi.org/10.1016/S0034-4257(70)80002-5
Axell, L. 2013. BSRA-15: A Baltic Sea Reanalysis 1990–2004. SMHI.
Axell, L. and Liu, Y. 2016. Application of 3-D ensemble variational data assimilation to a Baltic Sea reanalysis 1989–2013. Tellus A, 68, 24220.
https://doi.org/10.3402/tellusa.v68.24220
Berg, P. and Poulsen, J. W. 2012. Implementation Details for HBM. DMI Technical Report No. 12-11. Copenhagen.
Bonekamp, H., Montagner, F., Santacesaria, V., Nogueira Loddo, C., Wannop, S., Tomazic, I., et al. 2016. Core operational Sentinel-3 marine data product services as part of the Copernicus Space Component. Ocean Sci., 12(3), 787–795.
https://doi.org/10.5194/os-12-787-2016
Brasseur, P., Bahurel, P., Bertino, L., Birol, F., Brankart, J. M., Ferry, N., et al. 2005. Data assimilation for marine monitoring and prediction: the MERCATOR operational assimilation systems and the MERSEA developments. Q. J. Roy. Meteor. Soc., 131(613), 3561–3582.
https://doi.org/10.1256/qj.05.142
Buettner, K. J. and Kern, C. D. 1965. The determination of infrared emissivities of terrestrial surfaces. J. Geophys. Res., 70(6), 1329–1337.
https://doi.org/10.1029/JZ070i006p01329
Canizares, R., Madsen, H., Jensen, H. R., and Vested, H. J. 2001. Developments in operational shelf sea modelling in Danish waters. Estuar. Coast. Shelf S., 53(4), 595–605.
https://doi.org/10.1006/ecss.1999.0629
Cressman, G. P. 1959. An operational objective analysis system. Mon. Weather Rev., 87(10), 367–374.
https://doi.org/10.1175/1520-0493(1959)087<0367:AOOAS>2.0.CO;2
Cummings, J. A. 2005. Operational multivariate ocean data assimilation. Q. J. Roy. Meteor. Soc., 131(613), 3583–3604.
https://doi.org/10.1256/qj.05.105
Derber, J. and Rosati, A. 1989. A global oceanic data assimilation system. J. Phys. Oceanogr., 19(9), 1333–1347.
https://doi.org/10.1175/1520-0485(1989)019<1333:AGODAS>2.0.CO;2
Donlon, C. J., Minnett, P. J., Gentemann, C., Nightingale, T. J., Barton, I. J., Ward, B., and Murray, M. J. 2002. Toward improved validation of satellite sea surface skin temperature measurements for climate research. J. Climate, 15(4), 353–369.
https://doi.org/10.1175/1520-0442(2002)015<0353:TIVOSS>2.0.CO;2
Elken, J., Nõmm, M., and Lagemaa, P. 2011. Circulation patterns in the Gulf of Finland derived from the EOF analysis of model results. Boreal Environ. Res., 16, 84–102.
Fu, W. 2016. On the role of temperature and salinity data assimilation to constrain a coupled physical–biogeochemical model in the Baltic Sea. J. Phys. Oceanogr., 46(3), 713–729.
https://doi.org/10.1175/JPO-D-15-0027.1
Fu, W., Høyer, J. L., and She, J. 2011a. Assessment of the three dimensional temperature and salinity observational networks in the Baltic Sea and North Sea. Ocean Sci., 7(1), 75.
https://doi.org/10.5194/os-7-75-2011
Fu, W., She, J., and Zhuang, S. 2011b. Application of an Ensemble Optimal Interpolation in a North/Baltic Sea model: assimilating temperature and salinity profiles. Ocean Model., 40(3), 227–245.
https://doi.org/10.1016/j.ocemod.2011.09.004
Fu, W., She, J., and Dobrynin, M. 2012. A 20-year reanalysis experiment in the Baltic Sea using three-dimensional variational (3DVAR) method. Ocean Sci., 8(5), 827–844.
https://doi.org/10.5194/os-8-827-2012
Funkquist, L. 2006. An operational data assimilation system for the Baltic Sea. In European Operational Oceanography: Present and Future (Dahlin, H., Flemming, N. C., Marchand, P., and Petersson, S. E., eds). EuroGOOS Office, Norrköping, Sweden, and European Commission, Brussels, Belgium, pp. 656–660.
Gandin, L. S. 1963. Objective Analysis of Meteorological Fields. Gidrometizdat, Leningrad. (English translation No. 1373 by Israel Program for Scientific Translations (1965), Jerusalem.)
Ghil, M., Halem, M., and Atlas, R. 1979. Time-continuous assimilation of remote-sounding data and its effect on weather forecasting. Mont. Weather Rev., 107(2), 140–171.
https://doi.org/10.1175/1520-0493(1979)107<0140:TCAORS>2.0.CO;2
Høyer, J. L. and She, J. 2007. Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea. J. Marine Syst., 65(1), 176–189.
https://doi.org/10.1016/j.jmarsys.2005.03.008
Ide, K., Courtier, P., Ghil, M., and Lorenc, A. C. 1997. Unified notation for data assimilation: operational, sequential and variational. J. Meteorol. Soc. JpN. Ser. II, 75(1B), 181–189.
Ivanov, S. V., Kosukhin, S. S., Kaluzhnaya, A. V., and Boukhanovsky, A. V. 2012. Simulation-based collaborative decision support for surge floods prevention in St. Petersburg. J. Comput. Sci., 3(6), 450–455.
https://doi.org/10.1016/j.jocs.2012.08.005
Kalman, R. E. and Bucy, R. S. 1961. New results in linear filtering and prediction theory. J. Basic Eng., 83(3), 95–108.
https://doi.org/10.1115/1.3658902
Kikas, V. and Lips, U. 2016. Upwelling characteristics in the Gulf of Finland (Baltic Sea) as revealed by Ferrybox measurements in 2007–2013. Ocean Sci., 12(3), 843–859.
https://doi.org/10.5194/os-12-843-2016
Laanemets, J., Väli, G., Zhurbas, V., Elken, J., Lips, I., and Lips, U. 2011. Simulation of mesoscale structures and nutrient transport during summer upwelling events in the Gulf of Finland in 2006. Boreal Environ. Res., 16, 15–26.
Lagemaa, P. 2012. Operational Forecasting in Estonian Marine Waters. TUT Press.
Lips, U., Lips, I., Kikas, V., and Kuvaldina, N. 2008. Ferrybox measurements: a tool to study meso-scale processes in the Gulf of Finland (Baltic Sea). In 2008 IEEE/OES US/EU-Baltic International Symposium, 27–29 May 2008, Tallinn, Estonia, pp. 334–339.
Lips, U., Kikas, V., Liblik, T., and Lips, I. 2016. Multi-sensor in situ observations to resolve the sub-mesoscale features in the stratified Gulf of Finland, Baltic Sea. Ocean Sci., 12(3), 715–732.
https://doi.org/10.5194/os-12-715-2016
Liu, Y., Zhu, J., She, J., Zhuang, S., Fu, W., and Gao, J. 2009. Assimilating temperature and salinity profile observations using an anisotropic recursive filter in a coastal ocean model. Ocean Model., 30(2), 75–87.
https://doi.org/10.1016/j.ocemod.2009.06.005
Liu, Y., Meier, H. E., and Axell, L. 2013. Reanalyzing temperature and salinity on decadal time scales using the Ensemble Optimal Interpolation data assimilation method and a 3D ocean circulation model of the Baltic Sea. J. Geophys. Res. Oceans, 118(10), 5536–5554.
https://doi.org/10.1002/jgrc.20384
Liu, Y., Meier, H. M., and Eilola, K. 2014. Improving the multiannual, high-resolution modelling of biogeochemical cycles in the Baltic Sea by using data assimilation. Tellus A, 66, 24908.
https://doi.org/10.3402/tellusa.v66.24908
Lorenc, A. C. 1986. Analysis methods for numerical weather prediction. Q. J. Roy. Meteor. Soc., 112(474), 1177–1194.
https://doi.org/10.1002/qj.49711247414
Losa, S. N., Danilov, S., Schröter, J., Nerger, L., Maβmann, S., and Janssen, F. 2012. Assimilating NOAA SST data into the BSH operational circulation model for the North and Baltic Seas: inference about the data. J. Marine Syst., 105, 152–162.
https://doi.org/10.1016/j.jmarsys.2012.07.008
Losa, S. N., Danilov, S., Schröter, J., Janjić, T., Nerger, L., and Janssen, F. 2014. Assimilating NOAA SST data into BSH operational circulation model for the North and Baltic Seas: Part 2. Sensitivity of the forecast’s skill to the prior model error statistics. J. Marine Syst., 129, 259–270.
https://doi.org/10.1016/j.jmarsys.2013.06.011
Martin, M., Dash, P., Ignatov, A., Banzon, V., Beggs, H., Brasnett, B., et al. 2012. Group for High Resolution Sea Surface temperature (GHRSST) analysis fields inter-comparisons. Part 1: A GHRSST multi-product ensemble (GMPE). Deep Sea Res. Part II Top. Stud. Oceanogr., 77, 21–30.
https://doi.org/10.1016/j.dsr2.2012.04.013
McPherson, R. D., Bergman, K. H., Kistler, R. E., Rasch, G. E., and Gordon, D. S. 1979. The NMC operational global data assimilation system. Mon. Weather Rev., 107(11), 1445–1461.
https://doi.org/10.1175/1520-0493(1979)107<1445:TNOGDA>2.0.CO;2
Nardelli, B. B., Tronconi, C., Pisano, A., and Santoleri, R. 2013. High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project. Remote Sens. Environ., 129, 1–16.
https://doi.org/10.1016/j.rse.2012.10.012
Nowicki, A., Dzierzbicka-Głowacka, L., Janecki, M., and Kałas, M. 2015. Assimilation of the satellite SST data in the 3D CEMBS model. Oceanologia, 57(1), 17–24.
https://doi.org/10.1016/j.oceano.2014.07.001
She, J., Høyer, J. L., and Larsen, J. 2007. Assessment of sea surface temperature observational networks in the Baltic Sea and North Sea. J. Marine Syst., 65(1), 314–335.
https://doi.org/10.1016/j.jmarsys.2005.01.004
Siegel, H., Gerth, M., and Tschersich, G. 2006. Sea surface temperature development of the Baltic Sea in the period 1990–2004. Oceanologia, 48(S), 119–131.
Sokolov, A., Andrejev, O., Wulff, F., and Rodriguez Medina, M. 1997. The Data Assimilation System for Data Analysis in the Baltic Sea. Systems Ecology Contributions 3. Stockholm University.
Soosaar, E., Maljutenko, I., Raudsepp, U., and Elken, J. 2014. An investigation of anticyclonic circulation in the southern Gulf of Riga during the spring period. Cont. Shelf Res., 78, 75–84.
https://doi.org/10.1016/j.csr.2014.02.009
Sørensen, J. V. T. and Madsen, H. 2004. Efficient Kalman filter techniques for the assimilation of tide gauge data in three‐dimensional modeling of the North Sea and Baltic Sea system. J. Geophys. Res. Oceans, 109, CO3017.
Tang, Y., Kleeman, R., and Moore, A. M. 2004. SST assimilation experiments in a tropical Pacific Ocean model. J. Phys. Oceanogr., 34(3), 623–642.
https://doi.org/10.1175/3518.1
Taylor, K. E. 2001. Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res. Atmospheres, 106(D7), 7183–7192.
https://doi.org/10.1029/2000JD900719
Uiboupin, R. and Laanemets, J. 2009. Upwelling characteristics derived from satellite sea surface temperature data in the Gulf of Finland, Baltic Sea. Boreal Environ. Res., 14(2), 297–304.
Uiboupin, R. and Laanemets, J. 2015. Upwelling parameters from bias-corrected composite satellite SST maps in the Gulf of Finland (Baltic Sea). IEEE Geosci. Remote Sens. Lett., 12(3), 592–596.
https://doi.org/10.1109/LGRS.2014.2352397
Väli, G., Zhurbas, V., Lips, U., and Laanemets, J. 2017. Submesoscale structures related to upwelling events in the Gulf of Finland, Baltic Sea (numerical experiments). J. Marine Syst., 171, 31–42.
https://doi.org/10.1016/j.jmarsys.2016.06.010
https://doi.org/10.5194/os-7-771-2011