• Proceedings of the Korea Air Pollution Research Association Conference
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• 2008.10a
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• pp.386-388
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• 2008

• Journal of Korean Society for Atmospheric Environment
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• v.25 no.1
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• pp.1-14
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• 2009

Wind energy has been recognized as one of the most important and fastest growing energy resources without emission of air pollutant. Thus, it is necessary to predict wind speed and direction accurately both in time and space toward the efficient usage of wind energy. Numerical simulation experiments using the Fifth-Generation Mesoscale Model (MM5) are carried out to clarify the impact of surface observation data assimilation on the estimation of wind energy resources. The EXP_Radius run was designed with respect to the radius of influence in the Four-Dimensional Data Assimilation (FDDA), and the EXP_Impact run was made by changing the nudging coefficient that determines the relative magnitude of the nudging term. The simulation period covers a clear-sky event on 3 - 5 June 2007 and another is on 2 - 4 December 2006. It is found that the simulated results are very sensitive to the radius of influence and nudging parameters in the FDDA. The further analysis of the results shows that the impact of the radius of influence tends to be stronger in weak synoptic flow episode than that in strong synoptic flows episode. The nudging factor is also sensitive to the intensity of the synoptic flows.

• 한국신재생에너지학회:학술대회논문집
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• 2009.06a
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• pp.463-466
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• 2009

The numerical simulation optimized by Four Dimensional Data Assimilation (FDDA) with Quick Scatterometer (QuikSCAT) data is carried out to evaluate wind resource characteristics at various heights in the southeastern area of the Korean Peninsula, where wind farms are planned to be built on on- and off-shore as well as comparable diurnal wind variations are characterized at the surface. The temporal and spatial distributions of modeled wind speeds showed good agreement with the observations based on the temporal variation analysis. Model results indicate that the higher model is performed in resolution, the more precise results is at turbine hub height. Occasionally, wind speed variations for each numerical resolution has a different regional and seasonal variations. In the coast area, hub height wind speed of 9km-resolution is simillar to that of 3km-resolution. On the other hand, hub height wind speed of 3km-resolution is simillar to that of 1km-resolution in the Jiri mountainous area.

• 한국신재생에너지학회:학술대회논문집
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• 2008.10a
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• pp.274-277
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• 2008

To evaluate high-resolution wind resources for local and coastal area with complex terrain was attemped to combine the prognostic MM5 mesoscale model with CALMET diagnostic modeling this study. Firstly, MM5 was simulated for 1km resolution, nested fine domain, with FDDA using QuikSCAT seawinds data was employed to improve initial meteorological fields. Wind field and other meteorological variables from MM5 with all vertical levels used as initial guess field for CALMET. And 5 surface and 1 radio sonde observation data is performed objective analysis whole domain cells. Initial and boundary condition are given by 3 hourly RDAPS data of KMA in prognostic MM5 simulation. Geophysical data was used high-resolution terrain elevation and land cover(30 seconds) data from USGS with MM5 simulation. On the other hand SRTM 90m resolution and EGIS 30m landuse was adopted for CALMET diagnostic simulation. The simulation was performed on whole year for 2007. Vertical wind field a hour from CALMET and latest results of MM5 simulation was comparison with wind profiler(KEOP-2007 campaign) data at HAENAM site.

• Journal of Environmental Impact Assessment
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• v.11 no.2
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• pp.79-91
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• 2002

Four dimensional data assimilation (FDDA) technique was considered for 3 dimensional wind field in coastal area and a set of 3 numerical experiments including control experiments has been tested for the case of the synoptic weather pattern of the weak northerly geostrophic wind with the cloud amount of less than 5/10 in autumn. A three dimensional land and sea breeze model with the sea surface temperature (SST) of 290K was performed without nudging the observed wind field and surface temperature of AWS (Automatic Weather System) for the control experiment. The results of the control experiment showed that the horizontal temperature gradient across the coastline was weakly simulated so that the strength of the sea breeze in the model was much weaker than that of observed one. The experiment with only observed horizontal wind field showed that both the pattern of local change of wind direction and the times of starting and ending of the land-sea breeze were fairly well simulated. However, the horizontal wind speed and vertical motion in the convergence zone were weakly simulated. The experiment with nudgings of both the surface temperature and wind speed showed that both the pattern of local change of wind direction and the times of starting and ending of the land-sea breeze were fairly well simulated even though the ending time of the sea breeze was delayed due to oversimulated temperature gradient along the shoreline.

• Journal of Integrative Natural Science
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• v.11 no.1
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• pp.30-38
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• 2018

CALPUFF is one of the recommended air pollution models by EPA with AERMOD. It has been used to simulate the ambient concentration of critical air pollutants as well as non-critical pollutants such as persistent organic matters and the organic materials causing odor. In this model, the air pollutants go through dispersion, transportation, chemical reaction, and deposition process. These mechanisms are significantly influenced by meteorological condition. This study produces the meteorological field in three different methods for the simulation of $SO_2$ using CALPUFF: 1) CALMET model by using both ground-level and aerological observation, 2) CALMET model by using MM5 results with NCEP/NCAR reanalyzed data, 3) CALMET model by using MM5 results in which FDDA is applied with NCEP/NCAR reanalyzed data as well as the meteorological data of Korea Meteorological Administration. As a result of CALPUFF model, the resolved concentration of $SO_2$ showed different behaviors in three cases. For the first case, the fluctuation of SO2 concentration was frequently observed while the fluctuation is reduced in the second and third cases. In addition, the maximum concentration of $SO_2$ in the first case was about 2~3 times higher than the second case, and about 4~6 times higher than the third case. These results can be caused by the accuracy of the resolved meteorological field. It is inferred that the meteorological field of the first case could be less accurate than other two cases. These results show that the use of correct meteorological data can improve the result of dispersion model. Moreover, the contribution of various sources such as point, line, and area sources on the ambient concentration of air pollutant can be roughly estimated from the sensitivity analysis.

• Journal of the Korean earth science society
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• v.36 no.6
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• pp.567-579
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• 2015

The accurate simulation of micro-scale weather phenomena such as fog using the mesoscale meteorological models is a very complex task. Especially, the uncertainty arisen from initial input data of the numerical models has a decisive effect on the accuracy of numerical models. The data assimilation is required to reduce the uncertainty of initial input data. In this study, the limitation of the mesoscale meteorological model was verified by WRF (Weather Research and Forecasting) model for a summer fog event around the Nakdong river in Korea. The sensitivity analyses of simulation accuracy from the numerical model were conducted using two different initial and boundary conditions: KLAPS (Korea Local Analysis and Prediction System) and LDAPS (Local Data Assimilation and Prediction System) data. In addition, the improvement of numerical model performance by FDDA (Four-Dimensional Data Assimilation) using the observational data from AWS (Automatic Weather System) was investigated. The result of sensitivity analysis showed that the accuracy of simulated air temperature, dew point temperature, and relative humidity with LDAPS data was higher than those of KLAPS, but the accuracy of the wind speed of LDAPS was lower than that of KLAPS. Significant difference was found in case of relative humidity where RMSE (Root Mean Square Error) for LDAPS and KLAPS was 15.7 and 35.6%, respectively. The RMSE for air temperature, wind speed, and relative humidity was improved by approximately $0.3^{\circ}C$, $0.2m\;s^{-1}$, and 2.2%, respectively after incorporating the FDDA.