Jang-Won Seo

Annual and Seasonal Variations of the Sea Surface Heat Fluxes in the East Asian Marginal Seas

Based on the twice-daily marine atmospheric variables which were derived mostly from the weather maps for 18 years period from 1978 to 1995, the surface heat flux over the East Asian marginal seas was calculated at 0.5°×0.5° grid points twice a day. The annual mean distribution of the net heat flux shows that the maximum heat loss occurs in the central part of the Yellow Sea, along the Kuroshio axis and along the west coast of the northern Japanese islands. The area off Vladivostok turned out to be a heat-losing region, however, on the average, the amount of heat loss is minimum over the study area and the estuary of the Yangtze River also appears as a region of the minimum heat loss. The seasonal variations of heat flux show that the period of heat gain is longest in the Yellow Sea, and the maximum heat gain occurs in June. The maximum heat loss occurs in January over the study area, except the Yellow Sea where the heat loss is maximum in December. The annual mean value of the net heat flux in the East/Japan Sea is −108 W/m2 which is about twice the value of Hirose et al. (1996) or about 30% higher than Kato and Asai (1983). For the Yellow Sea, it is about −89 W/m2 and it becomes −75 W/m2 in the East China Sea. This increase in values of the net heat flux comes mostly from the turbulent fluxes which are strongly dependent on the wind speed, which fluctuates largely during the winter season.

Numerical Study of Storm Surges and Tide around Korea Using Operational Ocean Model

In this study, a two-dimensional storm surges/tide prediction model called the Storm surges/Tide Operational Model (STORM) was developed and applied as the operational forecast model of the Korea Meteorological Administration (KMA). STORM has good horizontal resolution (8 km) and accounts for the interaction between tides and storm surges. This model has been implemented for the northwestern Pacific Ocean including the area around the Korean Peninsula. To examine the model performance, a hindcasting experiment was carried out for Typhoon Maemi. The results showed good agreement between the simulation and observation. The operational model results were also verified for two years (June 2005–June 2007) using observed sea level data from tidal stations around the Korean Peninsula. Comparisons of modeled and observed sea level revealed larger differences at the western coast of Korea than at the southern and eastern coasts. The seasonal variations of bias and root mean square error (RMSE) between the modeled and observed sea levels generally showed small differences in summer and large differences in winter. The average bias, RMSE, and correlation coefficients for 12 total stations between modeled and observed values were −0.13 m (−0.14 m), 0.47 m (0.47 m), and 0.79 (0.78) for 24-hour (48-hour) forecasts.

Operational Wind Wave Prediction System at KMA

An overview of the current operational wind wave prediction system and of new developments at the Korea Meteorological Administration (KMA) is presented. KMA has operated a numerical ocean wave prediction system since 1992. The first major upgrade was done in 1999 with the adaptation of a third-generation wave model (WAM) for both regional (ReWAM-0.25 deg resolution, covering Northeast Asia) and global (GoWAM-1.25 deg resolution) domains. In 2005, the KMA replaced its NEC SX5 computer with a 1024-CPU Cray X1E system, a Parallel Vector Processor (PVP) machine with 128 node modules. A coastal ocean wave prediction system (CoWAM) has been designed and is currently under test mode. Six CoWAM domains, each of 3 deg longitude by 2 deg latitude in size, with a mesh size of 1 km, are nested inside the regional ocean wave prediction system. The directional wave spectra at the boundaries of the CoWAM are provided from a 1/12° resolution upgraded version of the operational ReWAM. The WAVEWATCH-III code (developed at NOAA) is used for the upgraded ReWAM and new CoWAM system. To ensure the required model performance in the Massively Parallel Processor (MPP) architecture of the new supercomputer, a Message Passing Interface (MPI) has been implemented in the model source code. The sea surface wind and significant wave height are verified routinely on a monthly basis. The global moored buoy data (including the coastal buoys operated by KMA) and remote sensing data from Topex/Poseidon, and Jason (retrieved wave heights) and QuikSCAT (retrieved wind data) are used for verification of the wave prediction system. This validation and a comparison of results from the new ReWAM and CoWAM against buoy data off the coast of Korea are discussed.

Validation of Ocean General Circulation Model (FMS-MOM4) in Relation with Climatological and Argo Data

Ocean general circulation model developed by GFDL on the basis of MOM4 of FMS are examined and evaluated in order to elucidate the global ocean status. The model employs a tripolar grid system to resolve the Arctic Ocean without polar filtering. The meridional resolution gradually increases from at the equator to at . Other horizontal grids have the constant and vertical grids with 50 levels. The ocean is also coupled to the GFDL sea ice model. It considers tidal effects along with fresh water and chlorophyll concentration. This model is integrated for a 100 year duration with 96 cpu forced by German OMIP and CORE dataset. Levitus, WOA01 climatology, serial CTD observations, WOCE and Argo data are all used for model validation. General features of the world ocean circulation are well simulated except for the western boundary and coastal region where strong advection or fresh water flux are dominant. However, we can find that information concerning chlorophyll and sea ice, newly applied to MOM4 as surface boundary condition, can be used to reduce a model bias near the equatorial and North Pacific ocean.

Improvements in the Simulation of Sea Surface Wind Over the Complex Coastal Area-II: Data Assimilation Using LAPS

We focus on the improvement of accuracy of sea surface wind over complex coastal area doling the warm season. Local Analysis Prediction System (LAPS) was used to improve the initial values in Mesoscale Meteorological model (MM5). During the clear summer days with weak wind speed, sea surface wind simulated with LAPS was compared with the case without LAPS. The results of modeling with LAPS has a good agreement mesoscale circulation such as mountain and valley winds on land and in case of modeling without LAPS, wind speed overestimated over the sea in the daytime. And the results of simulation with LAPS indicated similar wind speed values to observational data over the sea under influence of data assimilation using BUOY, QuikSCAT, and AMEBAS. The present study suggests that MM5 modelling with LAPS showed more improved results than that of without LAPS to simulate sea surface wind over the complex coastal area.

Meteorological Analysis of the Sea Fog in Winter Season on Gyeonggi Bay, Yellow Sea: A Case Study for the 106-vehicle Pileup on February 11, 2015

ABSTRACT Kim, Y.-K.; Kim, H.; Seo, J.-W.; An, H.-Y., and Choi, Y.-H., 2017. Meteorological analysis of the sea fog in winter season on Gyeonggi Bay, Yellow Sea: A case study for the 106-vehicle pileup on February 11, 2015. In: Lee, J.L.; Griffiths, T.; Lotan, A.; Suh, K.-S., and Lee, J. (eds.), The 2nd International Water Safety Symposium. Journal of Coastal Research, Special Issue No. 79, pp. 124–128. Coconut Creek (Florida), ISSN 0749-0208. The winter sea fog in the Yellow Sea is generally known as steam fog which is created when a cold and dry air mass (e.g., Siberian high) flows over warm sea surface. The status of the atmospheric boundary layer frequently changes by various interactions between atmosphere and ocean, because the west coast of the Korean peninsula has a complicated shoreline by numerous islands and rias coast. Therefore, the sea fog around this area is temporarily generated in unexpected place and it is reduced the visibility severely. The 132 casualties (3 dead, 129 injured) were occurred by the 106-vehicle pileup over the Yeongjong Bridge on February 11, 2015, because of the sea fog on Gyeonggi Bay in the Yellow sea. In this study, synoptic weather analysis and numerical weather modeling were performed to understand causes of sea fog. As a result, the atmospheric boundary layer of Gyeonggi Bay was generally stable because of a stagnant high pressure system when the accidents took place. In addition, low level air under the atmospheric boundary layer was colder than SST on Gyeonggi Bay. It means that the sea fog may be generated by the temperature difference between sea and air.

Delayed Mode Quality Control of Argo Data and Its Verification in the Pacific Ocean

Quality control of Argo(Array for Real-time Geostrophic Oceanography) data is crucial by reason that salinity measurements are liable to experience some drift and offset due to biofouling, contamination of sensor and wash-out of biocide. The automated Argo real-time quality control has a limit of sorting data quality, so that WJO program is adopted as standardized method of Argo delayed mode quality control (DMQc) in the world that is a precise quality control method. We conducted DMQC on pressure, temperature and salinity measured by Argo floats in the Pacific Ocean including expert evaluation. Particularly, salinity data were corrected using WJO program. 4 salinity profiles of Argo delayed mode were compared with nearby in situ CTD data and other Argo data in deep layer where oceanographic conditions are stable in time and space. The differences of both salinities were lower than target accuracy of Argo. As compared with the difference of salinities before DMQC, those after DMQC decreased by 60-80 percent. Quality of delayed mode salinity data seemed to be improved correcting salinity data suggested by WJO program.

The Effects of the Changed Initial Conditions on the Wind Fields Simulation According to the Objective Analysis Methods

We employed two data assimilation techniques including MM5 Four Dimensional Data Asssimilation (FDDA) and Local Analysis and Prediction System (LAPS) to find out the effects of the changed inetial conditions on the wind fields simulation according to the objective analysis methods. We designed 5 different modeling cases. EXP B used no data assimilation system. Both EXP Fl using surface observations and EXP F2 with surface and upper-air observations employed MM5 FDDA. EXP Ll using surface observations and EXP L2 with surface and upper-air observations used LAPS. As results of, simulated wind fields using MM5 FDDA showed locally characterized wind features due to objective analysis techniques in FDDA which is forcefully interpolating simulated results into observations. EXP Fl represented a large difference in comparison of wind speed with EXP B. In case of LAPS, simulated horizontal distribution of wind fields showed a good agreement with the patterns of initial condition and EXP Ll showed comparably lesser effects of data assimilation of surface observations than EXP Fl. When upper-air observations are applied to the simulations, while MM5 FDDA could hardly have important effects on the wind fields simulation and showed little differences with simulations with merely surface observations (EXP Fl), LAPS played a key role in simulating wind fields accurately and it could contribute to alleviate the over-estimated winds in EXP Ll simulations.