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.

The real-time monitoring of surface vegetation is essential for the management of droughts, vegetation growth, and water resources. The availability of land cover maps based on remotely collected data makes the monitoring of surface vegetation easier. The vegetation index in an area is likely to be proportional to meteorological elements there such as air temperature and precipitation. This study investigated relationship between vegetation index based on Moderate Resolution Image Spectroradiometer (MODIS) and ground-measured meteorological elements at the Yongdam catchment station. To do this, 16-day averaged data were used. It was found that the vegetation index is well correlated to air temperature but poorly correlated to precipitation. The study provides some intuition and guidelines for the study of the droughts and ecologies in the future.

미국 9.11 사고 이후 테러는 과거에 비하여 다중이용시설 공격을 통한 불특정 다수의 공격이 증가하고 있다. 연이은 런던 폭탄테러, 파키스탄의 자폭 등은 사람들의 공포심 및 사회적 불안감을 증가시켰다. 최근 국내에서 다양한 국제행사가 개최되고 있어, 방사능테러 위협에 대비한 방사성물질의 국가 안보 의식이 증대되고 있다. 본 논문에서는 HotSpot Code를 사용하여 서로 상이한 기상조건에 따른 결과를 비교하였다. 국내에서 발생 가능한 테러 시나리오 작성 후, RDD(Radiological Dispersal Device) 및 더티밤에 사용될 가능성이 높은 선원을 조사하였다. 기상조건은 Pasquill-Gifford 안정도 등급에 따라 가장 안정된 조건의 F, 가장 불안정한 조건의 A를 선택하여 비교하였다. 시뮬레이션을 통한 A, F 등급 결괏값은 방사선학적 영향에 의해 시민들이 급성 영향으로 사망하는 경우는 없다고 판단하였다. 또한, 풍속 및 기상 안정도에 따라 방사능의 도달 정도가 서로 다르며, 기상 조건에 따라 방사능 희석정도가 서로 다름을 확인할 수 있다. 분석결과는 방사능테러 발생 시 초동 대응에 활용할 수 있을 것으로 예상된다.

본 연구에서는 ANFIS 기반 GloSea5 앙상블 기상전망 개선 기법을 개발하고 평가하였다. 대상유역은 국내 주요 다목적댐인 충주댐 유역을 선정하였으며, 개선 기법은 ANFIS 기반의 전·후처리기법으로 구성된다. 전처리 기법에서 GloSea5의 앙상블 멤버에 가중치를 부여하며(OWM), 후처리 과정에서는 전처리결과를 편의보정 한다(MOS). 평가결과 편의보정된 GloSea5에 비해 예측성능이 개선되었으며, CASE3, CASE1, CASE2 순으로 모의성능이 우수하였다. 전처리 기법은 강수의 변동성이 큰 계절에 개선효과가 우수하였으며, 후처리 기법은 전처리로 개선하지 못한 오차를 줄일 수 있는 것으로 나타났다. 따라서 본 연구에서 개발한 ANFIS 기반 GloSea5 앙상블 기상전망 개선 기법은 전·후처리 기법을 함께 사용하는 것이 가장 좋으며, 특히 여름철과 같이 강수의 변동성이 큰 계절에 활용성이 높을 것으로 판단된다.

본 연구의 목적은 기상자료(강수량, 최고기온, 최저기온, 평균기온, 평균풍속) 기반의 다중선형 회귀모형을 개발하여 농업용저수지 저수율을 예측 하는 것이다. 나이브 베이즈 분류를 활용하여 전국 1,559개의 저수지를 지리형태학적 제원(유효저수량, 수혜면적, 유역면적, 위도, 경도 및 한발빈도)을 기준으로 30개 군집으로 분류하였다. 각 군집별로, 기상청 기상자료와 한국농어촌공사 저수지 저수율의 13년(2002~2014) 자료를 활용하여 월별 회귀모형을 유도하였다. 저수율의 회귀모형은 결정계수(R2)가 0.76, Nash-Sutcliffe efficiency (NSE)가 0.73, 평균제곱근오차가 8.33%로 나타났다. 회귀모형은 2년(2015~2016) 기간의 기상청 3개월 기상전망자료인 GloSea5 (GS5)를 사용하여 평가되었다. 현재저수율과 평년저수율에 의해 산정되는 저수지 가뭄지수(Reservoir Drought Index, RDI)에 의한 ROC (Receiver Operating Characteristics) 분석의 적중률은 관측값을 이용한 회귀식에서 0.80과 GS5를 이용한 회귀식에서 0.73으로 나타났다. 본 연구의 결과를 이용해 미래 저수율을 전망하여 안정적인 미래 농업용수 공급에 대한 의사결정 자료로 사용할 수 있을 것이다.

This study tries to reveal abnormal trends in climate change from 60 stations in Korea during 1981-2010 by comparisons to the standard station, Chupungnyeong station. Trends in climate change from station with the abnormalities, and their implication and causes are also discussed. Although Wando, Wonju, Mungyeong and Mokpo stations show the most abnormalities, normal trends in climate change from some climate data are also found from Mokpo station. On the other hand, some climate data from Suwon, Jeonju, Jinju, Icheon and Geumsan stations indicate the most normalities. It should be noted that variabilities of climate data are largely different, indicating that clear trends in climate change may not be extracted. The fact that some stations with the abnormalities from some climate data also show the normalities should be also noted. This study suggests that most stations with the most abnormalities may be relevant to relocation of station.

In this study, to investigate an optimal configuration method for the modeling system, we performed an optimization experiment by controlling the types of compilers and libraries, and the number of CPU cores because it was important to provide reliable model data very quickly for the national air quality forecast. We were made up the optimization experiment of twelve according to compilers (PGI and Intel), MPIs (mvapich-2.0, mvapich-2.2, and mpich-3.2) and NetCDF (NetCDF-3.6.3 and NetCDF-4.1.3) and performed wall clock time measurement for the WRF and CMAQ models based on the built computing resources. In the result of the experiment according to the compiler and library type, the performance of the WRF (30 min 30 s) and CMAQ (47 min 22 s) was best when the combination of Intel complier, mavapich-2.0, and NetCDF-3.6.3 was applied. Additionally, in a result of optimization by the number of CPU cores, the WRF model was best performed with 140 cores (five calculation servers), and the CMAQ model with 120 cores ( five calculation servers). While the WRF model demonstrated obvious differences depending on the number of CPU cores rather than the types of compilers and libraries, CMAQ model demonstrated the biggest differences on the combination of compilers and libraries.

Weather is the most influential factor for crop cultivation. Weather information for cultivated areas is necessary for growth and production forecasting of agricultural crops. However, there are limitations in the meteorological observations in cultivated areas because weather equipment is not installed. This study tested methods of predicting the daily mean temperature in onion fields using geostatistical models. Three models were considered: inverse distance weight method, generalized additive model, and Bayesian spatial linear model. Data were collected from the AWS (automatic weather system), ASOS (automated synoptic observing system), and an agricultural weather station between 2013 and 2016. To evaluate the prediction performance, data from AWS and ASOS were used as the modeling data, and data from the agricultural weather station were used as the validation data. It was found that the Bayesian spatial linear regression performed better than other models. Consequently, high-resolution maps of the daily mean temperature of Jeonnam were generated using all observed weather information.

Agricultural meteorological information is an important resource that affects farmersʼ income, food security, and agricultural conditions. Thus, such data are used in various fields that are responsible for planning, enforcing, and evaluating agricultural policies. The meteorological information obtained from automatic weather observation systems operated by rural development agencies contains missing values owing to temporary mechanical or communication deficiencies. It is known that missing values lead to reduction in the reliability and validity of the model. In this study, the hierarchical Bayesian spatio–temporal model suggests replacements for missing values because the meteorological information includes spatio–temporal correlation. The prior distribution is very important in the Bayesian approach. However, we found a problem where the spatial decay parameter was not converged through the trace plot. A suitable spatial decay parameter, estimated on the bias of root–mean–square error (RMSE), which was determined to be the difference between the predicted and observed values. The latitude, longitude, and altitude were considered as covariates. The estimated spatial decay parameters were 0.041 and 0.039, for the spatio-temporal model with latitude and longitude and for latitude, longitude, and altitude, respectively. The posterior distributions were stable after the spatial decay parameter was fixed. root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and bias were calculated for model validation. Finally, the missing values were generated using the independent Gaussian process model.

본 연구는 기상학적 및 수문학적 가뭄지수를 이용하여 가뭄사상의 첨두 심도 발생시점과 가뭄발생 기간에 대한 연구를 수행하였다. 연구를 수행하기 위해 사용한 가뭄지수로 기상학적 가뭄지수는 표준강수지수(Standardized Precipitation Index, SPI)를 사용하였으며, 수문학적 가뭄지수로는 하천가뭄지수(Streamflow Drought Index, SDI)와 표준하천유량지수(Standardized Streamflow Index, SSI)를 이용하였다. 연구 대상지역은 농촌과 도시가 공존하는 청미천 유역을 선택하였으며, 평가기간은 1985년 1월부터 2017년 6월까지 32.5년을 평가하였다 하천유량은 SWAT 모형을 이용하여 산정하였다. 수집한 데이터를 이용하여 가뭄지수를 산정한 후에 시계열을 토대로 가뭄의 특성을 분석하였다. 그 결과 수문학적 가뭄은 기상학적 가뭄이 발생한 후에 발생하는 것을 확인할 수 있다. SDI가 SSI보다 SPI와의 첨두 발생시점, 가뭄 시작일의 차이와 평균 가뭄기간이 더 크게 나지만, 가뭄지수의 심도를 비교해보면 일반적으로 SSI가 SDI 보다 심각한 심도를 나타내고 있다. 그러므로 가뭄의 특성을 확인하기 위해서는 기상학적, 수문학적 가뭄지수 등 다양한 가뭄지수를 검토해야 한다.

Background : This study researches on the microclimate, photosynthesis and growth characteristics for the development of shading materials proper for the wide and inclined ginseng cultivation facility which can respond to climate change and save labor. Methods and Results : The wide shading facilities were installed on the area of 1,000 ㎡ in 2014 and 4 facilities were installed on the test ginseng cultivation area. On Mar. 29, 2017, 2 blue shading nets [with the sun blocking rate of 85% (200 g/㎡) and 90% (220 g/㎡)] were installed each for 4 facilities. On June 26, 2017, the aluminum screen and black shading net (with the sun blocking rate of 40% each) were installed during the period of high temperature (30℃ or higher) for each facility. The maximum light intensity under the shading facility was high with blue shading net 85% and PE black shading net 40%, or blue shading net 85% and aluminum screen 40%, higher than other treatments. They were higher by 4.8 - 7.3%, 5.3 - 7.8% each in July and August. Among the coating materials for reducing the high temperature, the aluminum screen coating had less water leakage in early July, late July, mid-August and late August when the precipitation was more than 100 ㎜. The death of the aerial part of ginseng occurred less until October. The growth of the aerial part of 4-year ginseng was better in blue shading net 85% and PE black shading net 40%, blue shading net 85% and aluminum screen 40% or blue shading net 90% and PE black shading net 40% than in blue shading net 90% and aluminum screen 40% The photosynthesis rate was the highest in June with 3.67 μmol CO2/㎡/s under the blue shading net 90% and aluminum screen 40% and with 3.55 μmol CO2/㎡/s under blue shading net 85% and PE black shading net 40% for K-1. As for the land races ginseng, it was the highest with 3.55 μmol CO2/㎡/s under the blue shading net 85% and PE black shading net 40%. For the growth of the underground part of the 4-year ginseng, the blue shading net 85% + PE black shading net 40% or the blue shading net 85% + aluminum screen 40% was the best with respect to the growth of the ground part of the ginseng such as the length of root, the length of main root, diameter of root and weight of root than other treatment. Conclusion : Best coating materials for the wide shading facilities are the blue shading net 85% and aluminum screen 40%.

Background : The continuous cropping of Cnidium officinale is a serious problem for the cultivation practices, which is an unelucidated subject. This study is concerned mainly with rhizosphere microbiome and meteological factors on the cause of physiological damage in the continuous cropping of Cnidium officinale. Methods and Results : Microbial population and community dynamics was evaluated with metagenomic DNA by IonTorrent PGM. Results of HPLC profiling revealed that metabolic components of symbiotic interaction with Cnidium officinale was not detected in cultivated soils. Proteobacteria groups such as nitrogen fixing bacteria, Pseudomonas and Burkholderia of rhizosphere soil in continuous cropped fields mainly decreased compared to the first cropped soil. Principle component analysis of bacterial community showed a significantly differentiated vector between first cropping field and continuous cropped fields. Although growth characteristics including height, leaf length, leaf diameter amd stem diameter etc., was not different with continuous cultivation year until mid-July, physiological damage was dramatically started from late July. Yield of Rhizoma in continuous cropped fields significantly decreased compared to first cropped field. Evapotranspiration of Cnidium officinale with lysimeter for summer season was evaluated. It showed high relationship between solar radiation and evapotranspiration with R2 = 0.7778 and 41% of solar radiation converted into evapotranspiration during 16 days. This result imply that evapotranspiration is mainly controlled by radiation energy in clear days. Water and heat cycle through evapotranspiration is suppose to be one of the important factors related with physiological disorder of Cnidium officinale. Conclusion : This result imply that physiological damage resulted from continuous cropping is involved in decrease of Proteobacteria at rhizosphere soils under stressed conditions.

수문 ․ 기상레이더는 강우량을 바로 추정하지 못하고 여러 단계의 정량적 강우량 추정과정을 거치게 되므로 많은 불확실성 발생요소가 존재한다. 불확실성 관련한 기존 연구들은 정량적 레이더기반 강우량 추정과정에서 보정방법을 이용하여 각 단계별 불확실성을 줄이는 연구들을 수행하였다. 하지만 기존 연구들은 전체 과정에 대한 포괄적인 불확실성을 나타내지 못하고 각 단계별 불확실성의 상대적인 비율도 제시하지 못하는 단점이 있다. 본 연구에서는 정량적 레이더강우량 추정과정의 각 단계별 불확실성을 정량화하고 불확실성 전파를 나타낼 수 있는 적합한 방법을 제시하였다. 첫 번째로 초기와 최종 불확실성, 각 단계별 불확실성의 변동과 상대적인 비율을 나타낼 수 있는 새로운 개념을 제안하였다. 두 번째로 레이더기반 추정과정의 불확실성 정량화와 전파과정을 분석하기 위해 Maximum Entropy Method (MEM)와 Uncertainty Delta Method (UMD)를 적용하였다. 세 번째로 레이더기반 강우량 추정과정의 불확실성 정량화를 위해 2개 품질관리 알고리즘, 2개 강우량 추정방법, 2개 후처리 강우량 보정방법을 2012년 여름철 18개 사례에 대하여 사용하였다. 적용결과, MEM에서 최종 불확실성(후처리 강우량 보정 불확실성: ME = 3.81)이 초기 불확실성(품질관리 불확실성: ME = 4.28)보다 작게 나타났으며, UMD에서도 최종 불확실성(UMD = 4.75)이 초기 불확실성(UMD = 5.33)보다 작게 나타나 불확실성이 감소하는 것으로 나타났다. 하지만 레이더강우량 추정단계의 불확실성은 증가하는 것으로 나타났다. 또한 레이더강우량 추정과정에서 각 단계별로 적합한 방법을 선정하는 것이 각 단계별로 불확실성이 감소시킬 수 있음을 확인하였다. 따라서 본 연구는 새로운 방법이 명확히 불확실성을 정량화할 수 있으며 정확한 정량적 레이더 강우추정에 기여할 것으로 판단한다.

가뭄은 여러 가지 요인에 의해 복합적으로 발생하는 현상으로 자연적 원인과 인위적 원인으로 구분할 수 있다. 우리나라는 기후학적 특성상 여름철에 태평양 고기압의 발달 시기가 빠르거나 평년보다 강하면 장마 기간이 짧아져 자연적인 원인에 의해 가뭄이 발생한다. 가뭄은 발생과정과 피해 영향에 따라 기상학적, 농업적, 수문학적, 사회경제적 가뭄으로 구분할 수 있으며 직 ․ 간접적으로 다른 가뭄에 영향을 미친다. 가뭄의 종류가 기상학적 가뭄에서 농업적 가뭄 혹은 기상학적 가뭄에서 수문학적 가뭄으로 변화되는 현상을 가뭄 전이라 한다. 본 연구에서는 국내의 가뭄 전이 발생여부와 발생 패턴을 검토하기 위해 수문기상 정보를 이용하여 기상학적 가뭄에서 농업적 가뭄으로의 전이 관계를 분석하였다. 가뭄 전이 발생 현황 및 특성 분석을 위해 가뭄 발생의 유형을 5가지로 구분하였으며, 유형에 따라 가뭄 전이가 발생하지 않거나 최대 3개월까지 지체되는 것을 확인하였다. 향후 더 많은 가뭄 지표들과의 분석을 통해 가뭄 전이 관계를 일반화한다면 기상학적 가뭄 발생 시 농업적 가뭄 예측을 위한 인자로 활용할 수 있을 것으로 기대된다.

Both the propagation velocity and the direction of atmospheric waves are important factors for analyzing and forecasting meteo-tsunami. In this study, a total of 14 events of meteo-tsunami over 11 years (2006-2016) are selected through analyzing sea-level data observed from tidal stations along the west coast of the Korean peninsula. The propagation velocity and direction are calculated by tracing the atmospheric disturbance of each meteo-tsunami event predicted by the WRF model. Then, the Froude number is calculated using the propagation velocity of atmospheric waves and oceanic long waves from bathymetry data. To derive the critical condition for the occurrence of meteo-tsunami, supervised learning using a logistic regression algorithm is conducted. It is concluded that the threshold distance of meteo-tsunami occurrence, from a propagation direction, can be calculated by the amplitude of air-pressure tendency and the resonance factor, which are found using the Froude number. According to the critical condition, the distance increases logarithmically with the ratio of the amplitude of air-pressure tendency and the square of the resonance factor, and meteo-tsunami do not occur when the ratio is less than 5.11 hPa/10 min.

Global climate change caused by industrialization has caused abnormal weather conditions such as urban temperatures and tropical nights, urban heat waves, heat waves, and heavy rains. Therefore, the study tried to analyze climate conditions and weather conditions in the streets and analyze climate factors and meteorological factors that lead to inconvenience to citizens. In the case of trees, the overall temperature, surface temperature, solar irradiance, and net radiation were measured low, and the temperature was lower in the Pedestrian road than in roads. The dry bulb temperature, the black bulb temperature, and the wet bulb temperature for the thermal evaluation showed the same tendency. In the case of thermal evaluation, there was a similar tendency to temperature in WBGT, MRT, and UTCI, and varied differences between types. Although the correlation between the meteorological environment and the thermal environment showed a statistically significant significance, the difference between the measured items was not significant. The study found that the trees were generally pleasant to weather and thermal climate in the form of trees, and the differences were mostly documented.

In this study, we investigated the impact of different initial data on atmospheric modeling results using the Weather Research and Forecast (WRF) model. Four WRF simulations were conducted with different initialization in March 2015, which showed the highest monthly mean PM10 concentration in the recent ten years (2006-2015). The results of WRF simulations using NCEP-FNL and ERA-Interim were compared with observed surface temperature and wind speed data, and the difference of grid nudging effect on WRF simulation between the two data were also analyzed. The FNL simulation showed better accuracy in the simulated temperature and wind speed than the Interim simulation, and the difference was clear in the coastal area. The grid nudging effect on the Interim simulation was larger than that of the FNL simulation. Despite of the higher spatial resolution of ERA-Interim data compared to NCEP-FNL data, the Interim simulation showed slightly worse accuracy than those of the FNL simulation. It was due to uncertainties associated with the Sea Surface Temperature (SST) field in the ERA-Interim data. The results from the Interim simulation with different SST data showed significantly improved accuracy than the standard Interim simulation. It means that the SST field in the ERA-Interim data need to be optimized for the better WRF simulation. In conclusion, although the WRF simulation with ERA-Interim data does not show reasonable accuracy compared to those with NCEP-FNL data, it would be able to be Improved by optimizing the SST variable.

In this study, a weighted ensemble method of numerical weather prediction by ensemble models is applied for PyeongChang area. The post-processing method takes into account combination and calibration of forecasts from different numerical models, assigning greater weight to ensemble models that exhibit the better performance. Three different numerical models, including European Center Medium-Range Weather Forecast, Ensemble Prediction System for Global, and Limited Area Ensemble Prediction System, were used to perform the post-processing method. We compared the model outputs from the weighed combination of ensembles with those from the Ensemble Model Output Statistics (EMOS) model for each raw ensemble model. The results showed that the weighted ensemble method can significantly improve the post-processing performance, compared to the raw ensemble method of the numerical models.

This study analyzes relationship between land cover change and local climate data of 46 weather stations in South Korea from the 1980s to 2000s. The used area shows proportional relationships to mean daily min. temperature and mean temperature, and a reverse relationship is found between the used area and relative humidity. However, the forest indicates reverse relationships to mean daily min. temperature and mean temperature. The agricultural land causes to increase in relative humidity and decrease in mean wind speed, while the water increases mean daily min. temperature, daily min. temperature and mean temperature as well as mean wind speed. The urbanization type (used area + barren) shows high correlations with temperature and humidity. The suburbanization type (agricultural land + forest + grass + wetland) has high correlations with temperature and wind speed. High correlations are also found between the waterfront type (wetland + water), and temperature and wind speed. It can be concluded that change in land cover around weather station obviously influences on climate data of the weather station and it is also expected that reliability and homogeneity of climate data from a weather station can be enhanced by this study.

최근 국지성 집중호우 및 급격한 기상변화로 인해 돌발홍수와 같은 기상재해의 발생빈도가 증가함에 따라 고해상도의 기상레이더 강수자료를 사용한 수공학 분야의 연구가 활발하게 진행되고 있다. 기상레이더 강수자료를 활용하는 목적은 기상레이더 강수자료가 제공하는 공간분포를 최대한 활용하는데 있다. 본 연구에서는 고해상도 기상레이더 강수자료의 공간적 특성을 유지하면서 지상 강수자료의 양적특성을 극대화할 수 있는 조건부 합성기법을 보간법에 따라 분석 하였다. 기상레이더 강수자료와 지상 강수자료를 조건부 합성하기 위하여 Kriging, 역거리 가중법 및 Spline 보간법을 적용하였다. 조건부 합성결과는 지상 강수패턴을 현실성 있게 재현하였으며 추가적으로 보간법에 적용되지 않은 강수자료와 모형검증을 수행한 결과 조건부 합성을 통하여 생산된 공간적 강수정보의 수문학적 활용이 가능할 것으로 판단된다.