Browse > Article
http://dx.doi.org/10.14191/Atmos.2018.28.1.099

A Comparison of Accuracy of the Ocean Thermal Environments Using the Daily Analysis Data of the KMA NEMO/NEMOVAR and the US Navy HYCOM/NCODA  

Ko, Eun Byeol (Typhoon Research Center/Graduate School of Interdisciplinary Program in Marine Meteorology, Jeju National University)
Moon, Il-Ju (Typhoon Research Center/Graduate School of Interdisciplinary Program in Marine Meteorology, Jeju National University)
Jeong, Yeong Yun (Typhoon Research Center/Graduate School of Interdisciplinary Program in Marine Meteorology, Jeju National University)
Chang, Pil-Hun (National Institute of Meteorological Sciences)
Publication Information
Atmosphere / v.28, no.1, 2018 , pp. 99-112 More about this Journal
Abstract
In this study, the accuracy of ocean analysis data, which are produced from the Korea Meteorological Administration (KMA) Nucleus for European Modelling of the Ocean/Variational Data Assimilation (NEMO/NEMOVAR, hereafter NEMO) system and the HYbrid Coordinate Ocean Model/Navy Coupled Ocean Data Assimilation (HYCOM/NCODA, hereafter HYCOM) system, was evaluated using various oceanic observation data from March 2015 to February 2016. The evaluation was made for oceanic thermal environments in the tropical Pacific, the western North Pacific, and the Korean peninsula. NEMO generally outperformed HYCOM in the three regions. Particularly, in the tropical Pacific, the RMSEs (Root Mean Square Errors) of NEMO for both the sea surface temperature and vertical water temperature profile were about 50% smaller than those of HYCOM. In the western North Pacific, in which the observational data were not used for data assimilation, the RMSE of NEMO profiles up to 1000 m ($0.49^{\circ}C$) was much lower than that of HYCOM ($0.73^{\circ}C$). Around the Korean peninsula, the difference in RMSE between the two models was small (NEMO, $0.61^{\circ}C$; HYCOM, $0.72^{\circ}C$), in which their errors show relatively big in the winter and small in the summer. The differences reported here in the accuracy between NEMO and HYCOM for the thermal environments may be attributed to horizontal and vertical resolutions of the models, vertical coordinate and mixing scheme, data quality control system, data used for data assimilation, and atmosphere forcing. The present results can be used as a basic data to evaluate the accuracy of NEMO, before it becomes the operational model of the KMA providing real-time ocean analysis and prediction data.
Keywords
NEMO; HYCOM; ocean analysis data; accuracy; thermal environment;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
연도 인용수 순위
1 An, S.-I., and J. Choi, 2012: On the development of 2012 El Nino. Atmosphere, 22, 465-472, doi:10.14191/Atmos.2012.22.4.465 (in Korean with English abstract).   DOI
2 Beak, Y.-H., 2016: A study on an accuracy of satellite observed and numerical model-calculated SST around the Korean peninsula. M.S. thesis, Jeju National University, 70 pp (in Korean with English abstract).
3 Bernie, D. J., S. J. Woolnough, J. M. Slingo, and E. Guilyardi, 2005: Modeling diurnal and intraseasonal variability of the ocean mixed layer. J. Climate, 18, 1190-1202.   DOI
4 Bleck, R., G. R. Halliwell Jr., A. J. Wallcraft, S. Carroll, K. Kelly, and K. Rushing, 2002: HYbrid Coordinate Ocean Model (HYCOM) User's Manual: Details of the Numerical Code. HYCOM, Version 2.0.01, 177 pp.
5 Chassignet, E. P., H. E. Hurlburt, O. M. Smedstad, G. R. Halliwell, P. J. Hogan, A. J. Wallcraft, R. Baraille, and R. Bleck, 2007: The HYCOM (Hybrid Coordinate Ocean Model) data assimilative system. J. Mar. Syst., 65, 60-83.   DOI
6 Chiodi, A. M., and D. E. Harrison, 2017: Simulating ENSO SSTAs from TAO/TRITON Winds: The impacts of 20 years of buoy observations in the waveguide and comparison with reanalysis products. J. Climate, 30, 1041-1059, doi:10.1175/JCLI-D-15-0865.1   DOI
7 Cho, C. H., and Y. H. Seung, 1989: An oceanographic survey of tidal front around Kyunggi Bay. Yellow Sea Res., 2, 51-61 (in Korean with English abstract).
8 Cummings, J. A., and O. M. Smedstad, 2013: Variational data assimilation for the global ocean. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, 2, 303-343, doi:10.1007/978-3-642-350887_13.
9 Cummings, J. A., and O. M. Smedstad, 2014: Ocean data impacts in global HYCOM. J. Atmos. Ocean. Technol., 31, 1771-1791, doi:10.1175/JTECH-D-14-00011.1.   DOI
10 Gaspar, P., Y. Gregoris, and J.-M. Lefevre, 1990: A simple eddy kinetic energy model for simulations of the oceanic vertical mixing: Tests at station Papa and longterm upper ocean study site. J. Geophys. Res., 95, 16179-16193.   DOI
11 Halliwell, G., 2004: Evaluation of vertical coordinate and vertical mixing algorithms in the HYbrid-Coordinate Ocean Model (HYCOM). Ocean Model., 7, 285-322.   DOI
12 Han, I.-S., Y.-S. Suh, and K.-T. Seong, 2013: Windinduced spatial and temporal variations in the thermohaline front in the Jeju Strait, Korea. Fish. Aquat. Sci., 16, 117-124, doi:10.5657/FAS.2013.0117.
13 Ji, M., and A. Leetmaa, 1997: Impact of data assimilation on ocean initialization and El Nino prediction. Mon. Wea. Rev., 125, 742-753.   DOI
14 Hickox, R., I. Belkin, P. Cornillon, and Z. Shan, 2000: Climatology and seasonal variability of ocean fronts in the East China, Yellow and Bohai Seas from satellite SST data. Geophys. Res. Lett., 27, 2945-2948.   DOI
15 Jeong, J.-H., T.-W. Park, J.-H. Choi, S.-W. Son, K. H. Song, J.-S. Kug, B.-M. Kim, H. K. Kim, and S.-Y. Yim, 2016a: Assessment of climate variability over East Asia-Korea for 2015/16 winter. Atmosphere, 26, 337-345, doi:10.14191/Atmos.2016.26.2.337 (in Korean with English abstract).   DOI
16 Jeong, Y. Y., I.-J. Moon, and P.-H. Chang, 2016b: Accuracy of short-term ocean prediction and the effect of atmosphere-ocean coupling on KMA global seasonal forecast system (GloSea5) during the development of ocean stratification. Atmosphere, 26, 599-615, doi:10.14191/Atmos.2016.26.4.599 (in Korean with English abstract).   DOI
17 Park, T., C. J. Jang, M.-H. Kwon, H. Na, and K.-Y. Kim, 2015: An effect of ENSO on summer surface salinity in the Yellow and East China Seas. J. Mar. Syst., 141, 122-127, doi:10.1016/j.jmarsys.2014.03.017.   DOI
18 Shuto, K., 1996: Interannual variations of water temperature and salinity along the $137^{\circ}E$ meridian. J. Oceanogr., 52, 575-595.   DOI
19 Weaver, A. T., C. Deltel, E. Machu, S. Ricci, and N. Daget, 2005: A multivariate balance operator for variational ocean data assimilation. Quart. J. Roy. Meteor. Soc., 131, 3605-3625.   DOI
20 Yasuda, I., 1997: The origin of the North Pacific intermediate water. J. Geophys. Res., 102, 893-909.   DOI
21 Madec, G., 2008: NEMO ocean engine. Note du Pole de modelisation, Institut Pierre-Simon Laplace (IPSL), France, No 27, ISSN No 1288-1619, 401 pp.
22 Kim, D.-H., N. Nakashiki, Y. Yoshida, K. Maruyama, and F. O. Bryan, 2005: Regional cooling in the South Pacific sector of the Southern Ocean due to global warming. Geophys. Res. Lett., 32, L19607, doi:10.1029/2005GL023708.
23 Kim, Y. H., B.-J. Choi, J.-S. Lee, D.-S. Byun, K. R. Kang, Y.-G. Kim, and Y.-K. Cho, 2013: Korean ocean forecasting system: present and future. The Sea, 18, 89-103, doi: 10.7850/jkso.2013.18.2.89 (in Korean with English abstract).   DOI
24 Kouketsu, S., I. Kaneko, T. Kawano, H. Uchida, T. Doi, and M. Fukasawa, 2007: Changes of North Pacific intermediate water properties in the subtropical gyre. Geophys. Res. Lett., 34, L02605, doi:10.1029/2006GL028499.
25 Large, W. G., J. C. McWilliams, and S. C. Doney, 1994: Oceanic vertical mixing: A review and a model with a nonlocal boundary layer parameterization. Rev. Geophys., 32, 363-403.   DOI
26 Large, W. G., G. Danabasoglu, S. C. Doney, and J. C. McWilliams, 1997: Sensitivity to surface forcing and boundary layer mixing in a global ocean model: Annualmean climatology. J. Phys. Oceanogr., 27, 2418-2447.   DOI
27 McPhaden, M. J., and Coauthors, 2010: The global tropical moored buoy array. Proc. OceanObs'09, 2, Venice, Italy, 668-682.
28 Min, H. S., and B. Y. Yim, 2015: Evaluation of North Pacific intermediate water simulated by HadGEM2-AO. Ocean and Polar Res., 37, 265-278, doi:10.4217/OPR.2015.37.4.265 (in Korean with English abstract).   DOI
29 Mogensen, K., M. A. Balmaseda, A. T. Weaver, M. Martin, and A. Vidard, 2009: NEMOVAR: A variational data assimilation system for the NEMO ocean model. ECMWF Newslett., 120, 17-21.