본 연구에서는 유의미 학습을 촉진시키고 학습자들이 나름대로 개념을 형성하는 과정을 확인할 수 있는 방법인 개념도를 이용하여 중학생들의 ‘해수‘ 개념 형성에 미치는 영향에 대해 알아보았다. 이를 위하여 부산광역시의 중학교 1학년 4개 학급 128명을 대상으로 개념도를 활용한 수업 전-후에서 과학 학업 성취도와 자기 주도적 학습특성 및 개념도에 대한 인식을 조사해 본 결과는 다음과 같다. 첫째, 과학 학업 성취도에 미치는 효과는 유의수준 .05에서 유의미한 차이가 있는 것으로 나타났다. 둘째, 개념도를 활용한 수업은 전통적 수업 집단에 비해 학습자의 자기 주도적 학습특성을 향상시키는데 효과적인 것으로 나타났다. 셋째, 개념도를 활용한 수업이 개념 형성에 궁극적인 반응을 하는 것으로 나타났다.

본 연구에서는 현행 제7차 교육과정의 중학교 과학교과에서 제시된 대기압 개념을 분석하여 무게의 측면으로 대기압을 설명하는 교과서를 이용한 수업(수업방안 A)과 기체 분자운동과 무게의 양쪽 측면에서 설명하는 교과서를 이용한 수업(수업방안 B)의 두 가지 수업방안을 선정한 후 수업방안이 중학생들의 대기압 개념변화에 미치는 영향에 대하여 알아보았다. 이를 위하여 중학교 3학년 4개 학급을 대상으로 2개 학급씩 나누어 각각 수업을 실시하였다. 이 때 수업 전-후에 나타나는 학업 성취도와 대기압의 개념변화를 조사해본 결과는 다음과 같다. 첫째, 학업 성취도에 미치는 효과는 '수업방안 B'가 '수업방안 A'에 비해서 효과가 있는 것으로 나타났다. 둘째, 개념변화에 미치는 효과는 개념 검사의 사후 점수를 각 하위척도별로 효과를 검증한 결과, 개념의 요소 4개 중 3개의 요소인 '대기압이 작용하는 원리'와·대기압이 작용하는 방향과 이유', '지표면의 온도 상승에 따른 기압 변화와 그 이유'에서는 개념 변화에 유의미한 향상이 있는 것으로 나타났으나 '고도에 따른 대기압의 분포와 그 이유'에서는 개념 변화에 있어 유사한 것으로 나타났다. 셋째, 대기압 개념의 올바른 수업방안으로는 대기압을 기체 분자 운동론의 입장에서 정의하고 높이에 따른 대기업의 크기 분포는 공기의 무게로 정의한 것과 결과가 같게 나타남을 강조할 필요가 있다.

Solar radiation forecasts are important for predicting the amount of ice on road and the potential solar energy. In an attempt to improve solar radiation predictability in Jeju, we conducted machine learning with various data mining techniques such as tree models, conditional inference tree, random forest, support vector machines and logistic regression. To validate machine learning models, the results from the simulation was compared with the solar radiation data observed over Jeju observation site. According to the model assesment, it can be seen that the solar radiation prediction using random forest is the most effective method. The error rate proposed by random forest data mining is 17%.

Fog may have a significant impact on road conditions. In an attempt to improve fog predictability in Jeju, we conducted machine learning with various data mining techniques such as tree models, conditional inference tree, random forest, multinomial logistic regression, neural network and support vector machine. To validate machine learning models, the results from the simulation was compared with the fog data observed over Jeju(184 ASOS site) and Gosan(185 ASOS site). Predictive rates proposed by six data mining methods are all above 92% at two regions. Additionally, we validated the performance of machine learning models with WRF (weather research and forecasting) model meteorological outputs. We found that it is still not good enough for operational fog forecast. According to the model assesment by metrics from confusion matrix, it can be seen that the fog prediction using neural network is the most effective method.

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 initial 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 F1 using surface observations and EXP F2 with surface and upper-air observations employed MM5 FDDA. EXP L1 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 F1 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 L1 showed comparably lesser effects of data assimilation of surface observations than EXP F1. 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 F1), LAPS played a key role in simulating wind fields accurately and it could contribute to alleviate the overestimated winds in EXP L1 simulations.

We focus on the improvement of accuracy of sea surface wind over complex coastal area during 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 AMEDAS. 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.

We focused on improvement in simulation of wind fields for the complex coastal area. Local Analysis and Prediction System(LAPS) was used as a data assimilation method to improve initial conditions. Case studies of different LAPS inputs were performed to compare improvement of wind fields. Five cases have been employed : Ⅰ) non data assimilation, Ⅱ) all available data, Ⅲ) AWS, buoy, QuikSCAT, Ⅳ) AWS, buoy, wind profiler, Ⅴ) AWS, buoy, AMEDAS. Data assimilation can supplement insufficiency of the mesoscale model which does not represent detailed terrain effect and small scale atmospheric flow fields. Result assimilated all available data showed a good agreement to the observations rather than other cases and estimated well the local meteorological characteristics including sea breeze and up-slope winds. Result using wind profiler data was the next best thing. This implies that data assimilation with many high-resolution sounding data could contribute to the improvements of good initial condition in the complex coastal area. As a result, these indicated that effective data assimilation process and application of the selective LAPS inputs played an important role in simulating wind fields accurately in a complex area.

The southeastern coastal area of the Korean peninsula has a complex terrain including an irregular coastline and moderately high mountains. This implies that mesoscale circulations such as mountain-valley breeze and land-sea breeze can play an important role in wind field and ocean forcing. In this study, to improve the accuracy of complex coastal wind field(surface wind and sea surface wind), we carried out the sensitivity experiments based on PBL schemes in PSU/NCAR Mesoscale Model (MM5), which is being used in the operational system at Korea Meteorological Administration. Four widely used PBL parameterization schemes in sensitivity experiments were chosen: Medium-Range Forecast (MRF), High-resolution Blackadar, Eta, and Gayno-Seaman scheme. Thereafter, case(2004. 8. 26 - 8. 27) of weak-gradient flows was simulated, and the time series and the vertical profiles of the simulated wind speed and wind direction were compared with those of hourly surface observations (AWS, BUOY) and QuikSCAT data. In the simulated results, the strength of wind speed of all schemes was overestimated in complex coastal regions, while that of about four different schemes was underestimated in islands and over the sea. Sea surface wind using the Eta scheme showed the highest wind speed over the sea and its distribution was similar to the observational data. Horizontal distribution of the simulated wind direction was very similar to that of real observational data in case of all schemes. Simulated and observed vertical distribution of wind field was also similar under boundary layer(about 1 km), however the simulated wind speed was underestimated in upper layer.

In this study, we focused on the improvements in the simulation of sea surface wind over the complex coastal area. MM5 model being currently used to predict sea surface wind at Korea Meteorological Administration, was used to verify the accuracy to estimate the local wind field. A case study was performed on clear days with weak wind speed(4 m/s), chosen by the analysis of observations. The model simulations were conducted in the southeastern area of Korea during the selected periods, and observational data such as AWS, buoy and QuikSCAT were used to compare with the calculated wind components to investigate if simulated wind field could follow the tendency of the real atmospheric wind field. Results showed that current operational model, MM5, does not estimate accurately sea surface wind and the wind over the coastal area. The calculated wind speed was overestimated along the complex coastal regions but it was underestimated in islands and over the sea. The calculated diurnal changes of wind direction could not follow well the tendency of the observed wind, especially at nighttime. In order to exceed the limitations, data assimilation with high resolution data and more specificated geographical information is expected as a next best policy to estimate accurately the environment of local marine wind field.

Visibility reduction is a barometer of air pollution, which people can notice easily. First of all, we need to measure quantified visibility continuously in order to examine visibility reduction. Prevailing visibility is not practical to measure visibility depending on observer's expertness. Scattering visibility using Forward Scattering Meter (Belfort Visibility Sensor 6230) has been measured at Kwangan-Dong in Pusan and analysed since July, 1998. According to the analysis, the correlation coefficient(R) between prevailing visibility and scattering visibility was 0.7235. The visibility appeared that each frequency of poor visibility (under 6㎞) and good visibility(over 25㎞) was 10.6%, 9.7% on summertime in Pusan and the visibility range from 10km to 20km ranked high frequency as a half of whole ranges. The order of correlation coefficients between visibility and air pollutants are ranking CO, PM10 and NO2, that values are 0.5878, 0.5369, 0.5284 respectively. In meteorological factor, the case of poor visibility presented more weakly wind speed and higher relative humidity than the case of good visibility. The correlation coefficient between calculated visibility of multiple linear regression model and observed visibility was 0.7215. But the trend of calculated and observed visibility variation was similar with the exception of several good visibility cases.

After analyzing the correlation between air pollution and visibility, TSP and NO2 is responsible for poor visibility in Pusan. After analyzing the correlation between meteorological factors and visibility, general pattern of humidity has clear negative correlation. The variation of wind speed has a positive correlation. In order to investigate the cause of poor visibility in Pusan area, the Andersen sampler and PM-2.5 are used to collect and analyze aerosol. This study was carried out to monitor the visibility using Forward scattering meter and to find out the characteristics and the cause of good visibility case and poor visibility case by measuring and analyzing a variety of parameters, such as particle size distributions, chemical compositions, and meteorological conditions in Pusan. According to the analysis of intensive sampling, NO3-, NH4+ ion concentration increased together with the mass concentration around 0.5∼2.5㎛ approximately during the case of poor visibility. NH4NO3, NH4Cl, and NaCl were thought to be the major components of fine particles.