[논문]Meteorological Field Generation Method for CALPUFF Model

Park, Ji-Hoon; Park, Geun-Yeong

doi:10.13160/ricns.2018.11.1.30

Meteorological Field Generation Method for CALPUFF Model 원문보기

Journal of the Chosun Natural Science = 조선자연과학논문집, v.11 no.1, 2018년, pp.30 - 38

Park, Ji-Hoon (National Institute of Environmental Research, Climate and Air Quality Research Department) , Park, Geun-Yeong (Climate change & Environment Research Institute)

Abstract ▼ AI-Helper

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.

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제안 방법

After simulating the CALPUFF model, by using the program to extract the result files in the form of a table or grid of data of hourly SO₂ concentration from the result of the CALPUFF model named CALPOST, the values of SO₂ concentration of 10 hybrid receptor stations were extracted and shown in Fig. 2. They were also compared with the actual SO₂concentration of each hybrid receptor corresponding to the simulation period.
In this research, for the input data of the CALMET model, we try to use 1) the surface and aerological observation data, 2) the result files of MM5 not using four dimension data assimilation, and 3) the result files of MM5 using four dimension data assimilation. By using the result files created in this process for SO₂simulation of the CALPUFF model, we examined SO₂ concentration distribution according to each weather input data so that when using CALPUFF model, we can know the difference of results according to meteorological realm, and generate the appropriate meteorological realm. Thus it can be helpful to carry out the CALPUFF model.
In addition, in this research, we simulated by setting a comparatively small simulation section of 30 km×40 km, but we think it is necessary for the researches on conducting larger scale simulation section than this with the CALPUFF model, or conducting simulations with other medium and small scale models to investigate the efficiency of improving meteorological realm.
Meteorological data of the CALMET model is classified into aerological observation data, surface observation data, and precipitation data. In this research, aerological observation data was received from Radiosonde Database provided by FSL (Forecast Systems Laboratory) and NCDC (National Climatic Data Center) and passed through the preprocess with READ62 program and then used as input data of CALMET. In this research, we used the data of WMO (World Metroorological Organization) station 47122, Osan Station which is the closest aerological observation data station to the simulation section.
In this research, data of 100 m interval provided by Korea land cover map was used in order to utilize altitudinal data with higher resolution than those introduced in Table 1. High efficiency of this has been studied^[4].
Table 2 shows a classification table of 37 types of topographical map data applied by CTGPROC which is the input data preprocessing program of the topographic map data among the sub programs of the CALMET model. In this research, using the Korea land cover map which has the classification shown in Table 3, data was generated by applying and transforming to the classification table of 37 types. MAKEGEO generated geophysical data, which is input data of CALMET model, by using each result files of altitudinal data and topographic map data.
And the concentration of Seoknam-dong rapidly increased from February 5, but other areas did not show such a rapid increase, so it seems that the temporary discharge of pollutants that could not be displayed in the atmospheric environmental policy system was limited to Seoknam-dong measurement station. In this research, when the emission source was input to the CALPUFF model through the sensitivity analysis, the ship emission source which is one of the non-road mobile sources of the line pollution sources was removed and selected as the emission quantity of the basic simulation condition of CALPUFF simulation. However, looking at Fig.

대상 데이터

For altitudinal data, data of 30 seconds was used. For meteorological data, NCEP/ NCAR reanalysis data were used, and they are classified into data on 17 layers and the surface data. This data was created in the form of netCDF.
For the surface observation data, data of Incheon Meteorological Observatory which is one meteorological observatory in the simulation section and the surface observation data of 12 AWS stations in the simulation section (UTMX: 285.0~ 315.0 km, UTMY: 4130~4170 km) were obtained and used by converting into the surface input data and the precipitation input data which were required from the CALMET model.
In this research, aerological observation data was received from Radiosonde Database provided by FSL (Forecast Systems Laboratory) and NCDC (National Climatic Data Center) and passed through the preprocess with READ62 program and then used as input data of CALMET. In this research, we used the data of WMO (World Metroorological Organization) station 47122, Osan Station which is the closest aerological observation data station to the simulation section. For the surface observation data, data of Incheon Meteorological Observatory which is one meteorological observatory in the simulation section and the surface observation data of 12 AWS stations in the simulation section (UTMX: 285.

이론/모형

In composing altitudinal data and topographic map data of CALMET model, it is first necessary to decide what kind of projection to use for the generation of input data. In this research, UTM (Universal Transverse Mercator measure: km) is used. In addition, TTM (Tangential Transverse Mercator), LCC (Lambert Conformal Conic), and EM (Equatorial Mercator) can be adopted in the CALMET model.
not only through CALMET model. In this research, we constructed a meteorological realm through CALMET model. The connection between the input data and the CALPUFF model was shown in Fig.
The meteorological realm used for the CALPUFF model is obtained by using the result files of the CALMET model. The weather input data of the CALMET model uses the simulation results of surface and aerological observation data or MM5 (Mesoscale Meteorological Model V).
The relaxation term calculated in this way will reduce this error. The method of four dimension data assimilation used in this research is the station or observational nudging method. Since there is no standard program for generating input values of this method, observation data of stations must be converted as shown in Table 6.
By using the result files created in this process for SO₂simulation of the CALPUFF model, we examined SO₂ concentration distribution according to each weather input data so that when using CALPUFF model, we can know the difference of results according to meteorological realm, and generate the appropriate meteorological realm. Thus it can be helpful to carry out the CALPUFF model.

성능/효과

1. The simulation of CALPUFF model which generated the meteorological realm by using MM5 and MM5 applied of FDDA tended to show more similarity and the concentration value as measured values in general than the simulation of CALPUFF model which generated the meteorological realm by using the surface and aerological observation data, In addition, we could see that the vibration in the results using the surface and aerological observation data was much softened in the results using MM5 and MM5 applied of FDDA. We thought that this was because the meteorological realm in the diffusion model determines the diffusion and transport of pollutants and the chemical reaction, etc
By using the surface and aerological observation data, CALMET model was simulated and the results of the CALPUFF model with these results as the input data were generally high. On the other hand, by using MM5 and MM5 (FDDA), CALMET model was simulated and in the concentration of the CALPUFF model with these results as the input data, neither were high.
And in the results using the surface and aerological observation data of most hybrid receptors, the concentration tended to show high from January 28 to January 29. In the result of using MM5, this became about 50% lower than the result using the surface and aerological observation data, and the result of using MM5 (FDDA) showed about 10% lower in Sinheungdong, Nonhyeon-dong and Seoknam-dong, and about 50% lower in the other areas than the result of using MM5. The measured value of the concentration of Geomdan showed 3~6 times higher than that of all three cases, this can be said that the emission quantity of Geomdan displayed in this atmospheric environmental policy system was evaluated lower than the actual.
Making this meteorological realm closer to the actual measured values helps to draw efficient results. In this research, we examined how efficient it was to simulate the CALPUFF model through MM5 model that could better simulate the meteorological realm and the application of MM5 model to four dimension data assimilation and we could see that it was more efficient. However, there are other methods to improve the meteorological realm in addition to four dimension data assimilation in MM5, and there are other models of the meteorological realm as well.

참고문헌 (5)

National Institute of Environmental Research, "A Study on Identification of Source and Contribution Rate Evaluation(I)," in Endocrine disruptor substance investigation and research result report, 2005.
M. Barna and B. Lamb, "Improving ozone modeling in regions of complex terrain using observational nudging in a progonostic meteorological model," Atmos. Environ., Vol. 34, pp. 4889-4906, 2000

상세보기
A. Chandrasekar, C. R. Philbrick, R. Clark, B. Doddridge, and P. Georgopoulos, "Evaluating the performance of a computationally efficient MM5/CALMET system for developing wind field inputs to air quality models," Atmos. Environ., Vol. 37, pp. 3267-3276, 2003.

상세보기
H.-W. Lee, H.-Y. Won, H.-J. Choi, K.-Y. Lee, and H.-G. Kim, "Numerical Simulation of Dispersion Fields of $SO_2$ according to Atmospheric Flow Field to Reflect Local characteristics in Complex Coastal Regions," Journal of the Environmental Sciences Society, Vol. 14, pp. 297-309, 2005.

원문보기 상세보기
J.-B. Lee and E.-Y.Song, "An analysis of the urban effect on MM5 simulation," Proceeding of the 43rd Meeting of Korean Society for Atmospheric Environment, Incheon May 13-15, 2006.

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