Browse > Article
http://dx.doi.org/10.4217/OPR.2020.42.3.179

Comparison of Data Assimilation Methods in a Regional Ocean Circulation Model for the Yellow and East China Seas  

Lee, Joon-Ho (Basic Science Institute, Jeju National University)
Moon, Jae-Hong (Department of Earth and Marine Science, College of Ocean Sciences, Jeju National University)
Choi, Youngjin (GeoSystem Research Corporation)
Publication Information
Ocean and Polar Research / v.42, no.3, 2020 , pp. 179-194 More about this Journal
Abstract
The present study aims to evaluate the effects of satellite-based SST (OSTIA) assimilation on a regional ocean circulation model for the Yellow and East China Seas (YECS), using three different assimilation methods: the Ensemble Optimal Interpolation (EnOI), Ensemble Kalman Filter (EnKF), and 4-Dimensional Variational (4DVAR) techniques, which are widely used in the ocean modeling communities. The model experiments show that an improved initial condition by assimilating the SST affects the seasonal water temperature and water mass distributions of the YECS. In particular, the SST data assimilation influences the temperature structures horizontally and vertically in winter, thereby improving the behavior of the YS warm current water. This is due to the fact that during wintertime the water column is well mixed, which is directly updated by the SST assimilation. The model comparisons indicate that the SST assimilation can improve the model performance in resolving the subsurface structures in wintertime, but has a relatively small impact in summertime due to the strong stratification. The differences among the different assimilation experiments are obvious when the SST was sharply changed due to a typhoon passage. Overall, the EnKF and 4DVAR show better agreement with the observations than the EnOI. The relatively low performance of EnOI under storm conditions may be related with a limitation of EnOI method whereby an analysis is obtained from a number of climatological fields, and thus the typhoon-induced SST changes in short-time scales may not be adequately reflected in the data assimilation.
Keywords
4DVAR; EnKF; EnOI; data assimilation; Yellow and East China Seas;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Gopalakrishnan G, Hoteit I, Cornuelle BD, Rudnick DL (2019) Comparison of 4DVAR and EnKF state estimates and forecasts in the Gulf of Mexico. Q J R Meteorol Soc 145:1354-1376   DOI
2 Hoteit I, Luo X, Bocquet M, Kohl A, Ait-El-Fquih B (2018) New frontiers in operational oceanography, ch. 17: Data assimilation in oceanography: Current status and new directions. GODAE Ocean View 465-512
3 Hunt BR, Kostelich E, Szunyogh I (2007) Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D 230:112-126   DOI
4 Janekovic I, Powell BS, Matthews D, Mcmanus MA, Sevadjian J (2013) 4D-var data assimilation in a nested, coastal ocean model: A Hawaiian case study. J Geophys Res 118(10):5022-5035   DOI
5 Ji X, Kwon KM, Choi BJ, Liu G, Park KS, Wang H, Byun DS, Li Y, Ji Q, Zhu X (2017) Assimilating OSTIA SST into regional modeling systems for the Yellow Sea using ensemble methods. Acta Oceanol Sin 36:37-51
6 Kalnay E (2003) Atmospheric modeling, data assimilation and predictability. Cambridge University Press, Cambridge, 368 p
7 Kwon KM, Choi BJ, Kim SD, Lee SH, Park KA (2020) Assessment and Improvement of Global gridded sea surface temperature datasets in the Yellow Sea using in situ ocean buoy and research vessel observation. Remote Sens 12:759. doi:10.3390/rs12050759   DOI
8 Kwon KM, Choi BJ, Lee SH, Kim YH, S eo GH, C ho YK (2016). Effect of model error representation in the Yellow and East China Sea modeling system based on the ensemble Kalman filter. Ocean Dynam 66:263-283   DOI
9 Lee J H, K im T, Pang I C, M oon JH (2018) 4DVAR data assimilation with the Regional Ocean Modeling System (ROMS): Impact on the water mass distributions in the Yellow Sea. Ocean Sci J 53:165-178   DOI
10 Lee JH, Pang IC, Moon JH (2016) Contribution of the Yellow Sea bottom cold water to the abnormal cooling of sea surface temperature in the summer of 2011. J Geophys Res-Oceans 121:3777-3789   DOI
11 Li Y, He R, Chen K, McGillicuddy DJ (2015) Variational data assimilative modeling of the Gulf of Maine in spring and summer 2010. J Geophys Res-Oceans 120(5):3522-3541   DOI
12 Lyu G, Wang H, Zhu J (2014) Assimilating the along-track sea level anomaly into the regional ocean modeling system using the ensemble optimal interpolation. Acta Oceanol Sin 33(7):72-82
13 Mellor GL, Yamada T (1982) Development of a turbulence closure model for geophysical fluid problems. Rev Geophys 20:851-875   DOI
14 Miyoshi T, Sato Y, Kadowaki T (2010) Ensemble Kalman Filter and 4D-var intercomparison with the Japanese operational global analysis and prediction system. Mon Wea Rev 138(7):2846-2866   DOI
15 Moore AM, Arango HG, Broquet G, Powell BS, Zavala-Garay J, Weaver AT (2011a) The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems. I: System overview and formulation. Prog Oceanogr 91:34-49   DOI
16 Moore AM, Arango HG, Broquet G, Edwards CA, Veneziani M, Powell BS, Foley D, Doyle, Costa D, Robinson P (2011b) The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems: Part III - Observation impact and observation sensitivity in the California Current System. Prog Oceanogr 91:74-94   DOI
17 Ngodock H, Carrier M (2014) A 4DVAR system for the navy coastal ocean model. Part I: System description and assimilation of synthetic observations in Monterey Bay. Mon Wea Rev 142:2085-2107   DOI
18 Shen H, Zhang C, Xiao C, Zhu J (1998) Hange of the discharge and sediment flux to 561 estuary in Changjiang River. In: Hong GH, Zhang J, Park BK (eds) Health of the Yellow Sea. The Earth Love Publisher, Seoul, pp 129-148
19 Powell BS, Arango H, Moore AM, Lorzenzo ED, Milliff DFRF (2008) 4DVAR data assimilation in the Intra- Americas Sea with the Regional Ocean Modeling System (ROMS). Ocean Model 25:173-188   DOI
20 Shchepetkin AF, McWilliams JC (2005) The regional oceanic modeling system (ROMS): A split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean Model 9:347-404   DOI
21 Seo SN (2008) Digital 30sec gridded bathymetric data of Korea marginal seas - KorBathy30s. J Korean Soc Coast Ocean Eng 20:110-120
22 Warner JC, Sherwood CR, Butman B, Arango HG, Signell RP (2005) Performance of four turbulence closure models implemented using a generic length scale method. Ocean Model 8:81-113   DOI
23 Wunsch C (1996) The ocean circulation inverse problem. Cambridge University Press, Cambridge, 442 p
24 Yang SC, Corazza M, Carrassi A, Kalnay E, Miyoshi T (2009) Comparison of local ensemble transform Kalman filter, 3DVAR, and 4DVAR in a quasigeostrophic model. Mon Wea Rev 137(2):693-709   DOI
25 Bae HS (2017) Acceleration of optimal interpolation data assimilation for air quality forecasting. Ph.D. Thesis, Anyang University, 77 p
26 Kim YH, Lyu SJ, Choi BJ, Cho YK, Kim YG (2008) Implementation of the ensemble Kalman filter to a double Gyre ocean and sensitivity test using Twin Experiments. Ocean Polar Res 30(2):129-140   DOI
27 Kim JH, Eom HM, Choi JK, Lee SM, Kim Y-H, Chang PH (2015) Impacts of OSTIA sea surface temperature in regional ocean data assimilation system. J Korean Soc Oceanogr 20:1-15
28 Baek YH, Moon IJ (2019) The accuracy of satellitecomposite and model-reanalysis sea surface termperature data at the seas adjacent to the Korean Peninsula. Ocean Polar Res 41(4):213-232
29 Bennett AF (2005) Inverse modeling of the ocean and atmosphere. Cambridge University Press, Cambridge, 260 p
30 Blumberg AF, Galperin B, O’Connor DJ (1992) Modeling vertical structure of open-channel flows. J Hydraul Eng 118:1119-1134   DOI
31 Broquet G, Edwards CA, Moore AM, Powell BS, Veneziani M, Doyle JD (2009) Application of 4D-variational data assimilation to the California current system. Dynam Atmos Oceans 48:69-91   DOI
32 Buehner M, Houtekamer P, Charette C, Mitchell HL, He B (2010) Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part II: One-month experiments with real observations. Mon Wea Rev 138(5):1567-1586   DOI
33 Burchard H (2001) On the q2l equation by Mellor and Yamada (1982). Notes and correspondence. J Phys Oceanogr 31:1377-1387   DOI
34 Courtier P, Thepaut JN, Hollingsworth A (1994) A strategy for operational implementation of 4D-var using an incremental approach. Q J Roy Meteor Soc 120:1367-1388   DOI
35 Egbert GD, Erofeeva SY (2002) Efficient inverse modeling of barotropic ocean tides. J Atmo Ocean Technol 19:183-204   DOI
36 Di Lorenzo E, Moore AM, Arango HG, Cornuelle BD, Miller AJ, Powell BS, Chua BS, Bennett AF (2007) Weak and strong constraint data assimilation in the inverse Regional Ocean Modeling System (ROMS): Development and application for a baroclinic coastal upwelling system. Ocean Model 16:160-187   DOI
37 Donlon CJ, Martin M, Stark JD, Robert-Jones J, Fiedler E, Wimmer W (2012) The operational sea surface temperature and sea ice analysis (OSTIA) system. Remote Sens Environ 116:140-158   DOI
38 Edwards CA, Moore AM, Hoteit I, Cornuelle BD (2015) Regional ocean data assimilation. Ann Rev Mar Sci 7:21-42   DOI
39 Evensen G (2003) The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn 53:343-367   DOI
40 Fairall CW, Bradley EF, Rogers DP, Edson JB, Young GS (1996) Bulk parameterization of air-sea fluxes in TOGA COARE. J Geophys Res 101:3747-3767   DOI
41 Fisher M (1998) Minimization algorithms for variational data assimilation. In: Proceedings of the ECMWF Seminar on Recent Developments in Numerical Methods for Atmospheric Modelling, Reading, England, 7-11 September 1998, pp 364-385
42 Gentemann CL, Wentz FJ, DeMaria M (2006) Near real t ime global optimum interpolated microwave SSTs: Applications to hurricane intensity forecasting. In: Proceedings of the 27th Conference on Hurricanes and Tropical Meteorology, Monterey, 23-28 April 2006
43 Ghil M, Malanotte-Rizzoli P (1991) Data assimilation in meteorology and oceanography. Adv Geophys 33:141-266   DOI