• Journal of the Korean earth science society
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• v.33 no.4
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• pp.309-319
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• 2012

In order to clarify the characteristics of satellite based sea wind data assimilations applied for the estimation of wind resources around the Korean peninsula, several numerical experiments were carried out using WRF. Satellite sea wind data used in this study are QuikSCAT from NASA and ASCAT from ESA. When the wind resources are estimated with data assimilation, its estimation accuracy is improved clearly. Since the band width is broad for QuikSCAT, statistical accuracy of the estimated wind resources with QuikSCAT assimilations is better than that with ASCAT assimilations. But the wind estimated around sub-satellite point matches better with of ASCAT compared to QuikSCAT assimilation. The impact of sea wind data assimilation on the prediction of wind resources lasts for 6 hours after data assimilation starts, therefore the data assimilation processes using both fine spatial and temporal resolutions of sea wind are needed to make a more useful wind resource map of the Korean Peninsula.

• 한국지구과학회:학술대회논문집
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• 2003.09a
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• pp.108-109
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• 2003

• Journal of Environmental Science International
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• v.27 no.11
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• pp.1141-1154
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• 2018

Numerical experiments were carried out to investigate the effect of data assimilation of observational data on weather and PM (particulate matter) prediction. Observational data applied to numerical experiment are aircraft observation, satellite observation, upper level observation, and AWS (automatic weather system) data. In the case of grid nudging, the prediction performance of the meteorological field is largely improved compared with the case without data assimilations because the overall pressure distribution can be changed. So grid nudging effect can be significant when synoptic weather pattern strongly affects Korean Peninsula. Predictability of meteorological factors can be expected to improve through a number of observational data assimilation, but data assimilation by single data often occurred to be less predictive than without data assimilation. Variation of air pressure due to observation nudging with high prediction efficiency can improve prediction accuracy of whole model domain. However, in areas with complex terrain such as the eastern part of the Korean peninsula, the improvement due to grid nudging were only limited. In such cases, it would be more effective to aggregate assimilated data.

• Journal of Environmental Impact Assessment
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• v.22 no.6
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• pp.609-626
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• 2013

Air quality models have been widely used to study and simulate many air quality issues. In the simulation, it is important to raise the accuracy of meteorological predicted data because the results of air quality modeling is deeply connected with meteorological fields. Therefore in this study, we analyzed the effects of meteorological fields on the air quality simulation. This study was designed to evaluate MM5 predictions by using different initial condition data and different observations utilized in the data assimilation. Among meteorological scenarios according to these input data, the results of meteorological simulation using National Centers for Environmental Prediction (Final) Operational Global Analysis data were in closer agreement with the observations and resulted in better prediction on ozone concentration. And in Seoul, observations from Regional Meteorological Office for data assimilations of MM5 were suitable to predict ozone concentration. In other areas, data assimilation using both observations from Regional Meteorological Office and Automatical Weather System provided valid method to simulate the trends of meteorological fields and ozone concentrations. However, it is necessary to vertify the accuracy of AWS data in advance because slightly overestimated wind speed used in the data assimilation with AWS data could result in underestimation of high ozone concentrations.

• Journal of the Korean earth science society
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• v.26 no.1
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• pp.66-77
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• 2005

Aerological observation at Heuksando located in south-western part of Koran Peninsula has been started at 1 June 2003. In order to clarify the improvement of meteorological prediction quality. it is necessary to compare between aerological data observed at Gawngju and Heuksando and to make clear the influence of Heuksando data assimilation. Therefore numerical simulations were carried out with High resolution meterological prediction system based on MM5(The 5th Generation Mesoscale Model). The pattern of wind and temperature field observed at Heuksando and Gwangju are different due to land surface friction End Sensible heat flux at surface and the wind field Simulated With Gwangju and Heuksando aerological data agree well with observation wind field. Although the amount of precipitation in these experiments is underestimated. the area and starting time of precipitation around Honam province in case with Heuksando data is more reliable that without the data.

• Ocean and Polar Research
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• v.41 no.4
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• pp.213-232
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• 2019

This study evaluates the accuracy of four satellite-composite (OSTIA, AVHRR, G1SST, FNMONC-S) and three model-reanalysis (HYCOM, JCOPE2, FNMOC-M) daily sea surface temperature (SST) data around the Korean Peninsula (KP) using ocean buoy data from 2011-2016. The results reveal that OSTIA has the lowest root mean square error (RMSE; 0.68℃) and FNMOC-S/M has the highest correction coefficients (r = 0.993) compared with observations, while G1SST, JCOPE2, and AVHRR have relatively larger RMSEs and smaller correlations. The large RMSEs were found in the western coastal regions of the KP where water depth is shallow and tides are strong, such as Chilbaldo and Deokjeokdo, while low RMSEs were found in the East Sea and open oceans where water depth is relatively deep such as Donghae, Ulleungdo, and Marado. We found that the main sources of the large RMSEs, sometimes reaching up to 5℃, in SST data around the KP, can be attributed to rapid SST changes during events of strong tidal mixing, upwelling, and typhoon-induced mixing. The errors in the background SST fields which are used in data assimilations and satellite composites and the missing in-situ observations are also potential sources of large SST errors. These results suggest that both satellite and reanalysis SST data, which are believed to be true observation-based data, sometimes, can have significant inherent errors in specific regions around the KP and thus the use of such SST products should proceed with caution particularly when the aforementioned events occur.

• Atmosphere
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• v.33 no.4
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• pp.423-440
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• 2023

The updated version of Global Ocean Data Assimilation and Prediction System (GODAPS) in the NIMS/KMA (National Institute of Meteorological Sciences/Korea Meteorological Administration), which has been in operation since December 2021, is being introduced. This technical note on GODAPS2 describes main progress and updates to the previous version of GODAPS, a software tool for the operating system, and its improvements. GODAPS2 is based on Forecasting Ocean Assimilation Model (FOAM) vn14.1, instead of previous version, FOAM vn13. The southern limit of the model domain has been extended from 77°S to 85°S, allowing the modelling of the circulation under ice shelves in Antarctica. The adoption of non-linear free surface and variable volume layers, the update of vertical mixing parameterization, and the adjustment of isopycnal diffusion coefficient for the ocean model decrease the model biases. For the sea-ice model, four vertical ice layers and an additional snow layer on top of the ice layers are being used instead of previous single ice and snow layers. The changes for data assimilation include the updated treatment for background error covariance, a newly added bias scheme combined with observation bias, the application of a new bias correction for sea level anomaly, an extension of the assimilation window from 1 day to 2 days, and separate assimilations for ocean and sea-ice. For comparison, we present the difference between GODAPS and GODAPS2. The verification results show that GODAPS2 yields an overall improved simulation compared to GODAPS.

• 한국해양학회지
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• v.3 no.1
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• pp.16-25
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• 1968

This study was undertaken to assess the annual cycle of primary production and plant pigments in a downstream of the Han River. Measurements were carried out at three week intervals during April 1966 and March 1967, and ancillary data include water temperature, transparency, pH, dissolved oxygen and phytoplankton cell number. The seasonal cycle in water temperature profile shows the hihgest in the end of August with 27$^{\circ}C$, lowest in the middle of February with 0.2$^{\circ}C$. The transparency with Secchi disk reading varied from a maximum 4.0m in fall and a minimum 0.5m or less in early spring and flood season of summer. The pH of the river water varied from 6.5 to 7.3, averaged 6.91 in the surface water and 6.98 in the bottom water, showed little seasonalvariability. The dissolved oxygen in the surface water ranged from 5.93-9.64ml/L, while in the bottom water it ranged from 5.54-9.72 ml/L, and the oxygen saturation never fall below 94%. None thermal, the distribution of pH and content of oxygen, stratifications occurred. An apparent seasonal cycle of primary productivity was observed with remarkably high levels in the spring and fall, the lowest level in the winter. The range of net carbon assimilations showed 3.1-112.6 mgC/㎥/day or 15-427 mgC/㎡/day in spring, 37.0-271.2 mgC/㎥/day or 115-329 mgC/㎡/day in summer, 27.2-168.0 mgC/㎥ /day or 139-415 mgC/㎡/day in fall and 0.5-10.9 mgC/㎥/day or 5-19 mg/㎡/day in winter, respectively. Amount of chlorophyll ${\alpha}$ ranged from a minimum concentration of 0.2-0.3 mg/㎥ in the middle of February and a maximum 4.1-6.7 mg/㎥ in the middle of June. A general increase trend in chlorophyll ${\alpha}$ concentration was noted with increase of the river water temperature.