• 대기
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• 제17권4호
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• pp.435-453
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• 2007

KEOP (Korea Enhanced Observing Period)-2004 intensive summer observation was carried out from 20 June to 5 July 2004 over the Southwestern part of the Korean peninsula. In this study, the effects of KEOP-2004 intensive observation data on the simulation of precipitation system are investigated using KLAPS (Korea Local Analysis and Prediction System) and PSU/NCAR MM5. Three precipitation cases during the intensive observation are selected for detailed analysis. In addition to the control experiments using the traditional data for its initial and boundary conditions, two sensitivity experiments using KEOP data with and without Jindo radar are performed. Although it is hard to find a clear and consistent improvement in the verification score (threat score), it is found that the KEOP data play a role in improving the position and intensity of the simulated precipitation system. The experiments started at 00 and 12 UTC show more positive effect than those of 06 and 18 UTC. The effect of Jindo radar is dependent on the case. It plays a significant role in the heavy rain cases related to a mesoscale low over Changma front and the landing of a Typhoon. KEOP data produce more strong difference in the 06/18 UTC experiments than in 00/12 UTC, but give more positive effects in 00/12 UTC experiments. One of the possible explanations for this is that : KEOP data could properly correct the atmosphere around them when there are certain amounts of data, while gives excessive effect to the atmospheric field when there are few data. CRA analysis supports this reasoning. According to the CRA (Contiguous Rain Area) analysis, KEOP data in 00/12 UTC experiments improve only the surrounding area, resulting in essentially same precipitation system so the effects remain only in each convective cell rather than the system itself. On the other hand, KEOP data modify the precipitation system itself in 06/18 UTC experiments. Therefore the effects become amplified with time integration.

• 한국수소및신에너지학회논문집
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• 제24권1호
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• pp.1-11
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• 2013

Additives such as glycerol, methanol, acetone, and ethanol were used to prevent $NaBO_2$ from precipitation, and their effects on hydrogen generation properties of $NaBH_4$ hydrolysis were investigated. When the concentration of additives was 5 wt%, the additives such as methanol, acetone, and ethanol could not prevent $NaBO_2$ precipitation. Although glycerol prevented $NaBO_2$ precipitation, conversion efficiency decreased to 78.0% due to its viscosity. Based on test results, hydrogen generation tests were also performed at various concentration of glycerol and methanol to investigate the concentration effects on hydrogen generation properties. As the concentration of glycerol increased from 1 wt% to 3 wt%, conversion efficiency increased owing to additive effect. When its concentration increased to 5 wt%, conversion efficiency decreased due to its viscosity. As the concentration of methanol increased from 5 wt% to 10 wt%, conversion efficiency increased owing to additive effect. When its concentration increased to 15 wt%, conversion efficiency decreased due to $NaB(OCH_3)_4$ precipitate. Although conversion efficiency decreased about 1% when 3 wt% glycerol was added, $NaBO_2$ precipitation was prevented. Consequently, addition of 3 wt% glycerol to $NaBH_4$ solution improves stability of hydrogen generation system.

• 한국수자원학회논문집
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• 제30권3호
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• pp.247-256
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• 1997

1961년 이전에 관측이 시작되어 30년 이상의 관측자료가 있는 기상청의 15개 관측소의 강수량 자료를 이용하여 우리나라의 여름철 강수량 분포 특성을 조사하였다. 특히 이 연구에서는 우리나라 강수량 기후 평년값을 이용하여 기후적 특성을 조사하였으며, 지역별로 연 강수량, 여름철 강수량, 장마기간중 강수량의 연도별 변동을 비교 분석하고 그 상관을 조사하였다. 대체로 우리나라의 경우 연 강수량의 반 이상이 6, 7, 8월의 여름철에 집중되어 있고, 또 이 여름철 강수량은 장마에 크게 영향을 받는다. 또 지역별로 여름철 강수량 및 장마가 연 강수량에 미치는 기여도를 조사한 결과 서울을 비롯한 중서부 내륙지방이 장마의 영향을 가장 많이 받으며 동해안 중북부 지역과 제주도 지역은 상대적으로 장마의 영향이 적고, 국지적인 지형적 영향을 많이 받는 것으로 나타났다. 또한 우리나라의 경우 강수량의 연도별 변동이 심한 것으로 나타났으며, 특히 연 강수량보다 여름철 강수량과 장마기간중 강수량의 변화가 더 심한 것으로 나타났다. 따라서 국가 수자원 문제와 관련하여 연 강수량의 변동을 파악하기 위해서는 여름철 강수량의 변동에 대한 이해가 중요하며 아울러 장마의 특성 즉 몬순에 대한 파악이 함께 이루어져야 한다.

• 대기
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• 제19권2호
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• pp.155-168
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• 2009

Characteristics of seasonal mean diurnal temperature range (DTR) and their causes over South Korea are investigated using the 60 stations data of Korea Meteorological Administration from 1976 to 2005. In general, the seasonal mean DTR is greatest during spring (in inland area) and least during summer (urban and coastal area). The spatial and seasonal variations of DTR are closely linked with the land surface conditions (especially vegetation activity and soil moisture) and atmospheric conditions (cloud amount, precipitation, local circulation). The seasonal mean DTR shows a decreasing trend at the major urban areas and at the north-eastern part of South Korea. Whereas, it shows an increasing trend at the central area of the southern part. Decreasing and increasing trends of DTR are more significant during summer and fall, and during spring and winter. The decrease (increase) of DTR is mainly caused by the stronger increase of daily minimum (maximum) temperature than daily maximum (minimum) temperature. The negative effects of precipitation and cloud amount on the DTR are greater during spring and at the inland area than during winter and at the coastal area. And the effect of daytime precipitation on the DTR is greater than that of nighttime precipitation.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2005년도 학술발표회 논문집
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• pp.894-899
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• 2005

1970년대 이후, 우리나라는 산업화에 따른 급격한 도시화가 이루어졌다. 본 논문에서는 우리나라의 대표적인 도시인 서울특별시 및 6대 광역시의 1973년부터 2003년까지의 31개년의 강우량 자료를 이용하여 강우량의 변화에 대하여 분석하였다. 또한 강우량의 변화는 도시화 이외에도 엘니뇨 등의 기상 변화와 각종 기상 이변에도 큰 영향을 받으므로 도시 지역과 비교할 수 있는 비도시 지역을 선정하여 도시 지역의 강우량 변화와 비도시지역의 강우량 변화를 비교 분석하였다. 도시 지역과 비도시 지역의 연강우량, 여름 강우량, 지속 시간 1시간 및 24시간 연최대 강우량에 대해 통계적인 분석을 수행하였다. 그 결과, 도시 지역에서의 여름($6\~9$월) 강우량의 증가가 비도시 지역에 비해 두드러졌다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권5호
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• pp.1690-1707
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• 2021

Precipitation prediction during flood season has been a key task of climate prediction for a long time. This type of prediction is linked with the national economy and people's livelihood, and is also one of the difficult problems in climatology. At present, there are some precipitation forecast models for the flood season, but there are also some deviations from these models, which makes it difficult to forecast accurately. In this paper, based on the measured precipitation data from the flood season from 1993 to 2019 and the precipitation return data of CWRF, ANN cycle modeling and a weighted integration method is used to correct the CWRF used in today's operational systems. The MAE and TCC of the precipitation forecast in the flood season are used to check the prediction performance of the proposed algorithm model. The results demonstrate a good correction effect for the proposed algorithm. In particular, the MAE error of the new algorithm is reduced by about 50%, while the time correlation TCC is improved by about 40%. Therefore, both the generalization of the correction results and the prediction performance are improved.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.132-132
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• 2021

Access to accurate spatial precipitation in many hydrological studies is necessary. Existence of many mountains with diverse topography in South Korea causes different spatial distribution of precipitation. Rain gauge stations show accurate precipitation information in points, but due to the limited use of rain gauge stations and the difficulty of accessing them, there is not enough accurate information in the whole area. Weather radars can provide an integrated precipitation information spatially. Despite this, weather radar data have some errors that can not provide accurate data, especially in heavy rainfall. In this study, some location-based variable like aspect, elevation, plan curvature, profile curvature, slope and distance from the sea which has most effect on rainfall was considered. Then Automatic Weather Station data was used for spatial training of variables in each event. According to this, K-fold cross-validation method was combined with Adaptive Neuro-Fuzzy Inference System. Based on this, 80% of Automatic Weather Station data was used for training and validation of model and 20% was used for testing and evaluation of model. Finally, spatial distribution of precipitation for 1×1 km resolution in Gwangdeoksan radar station was estimates. The results showed a significant decrease in RMSE and an increase in correlation with the observed amount of precipitation.

• Geomechanics and Engineering
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• 제32권1호
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• pp.85-96
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• 2023

Microbially induced carbonate precipitation (MICP) is extensively discussed as a promising topic for ground stabilization. The practical effect of stabilizing the expansive soil is presented in this paper with a logical process from the bacterial activity to the treatment technology. Temperature, pH, shaking frequency, and inoculation amount are discussed to evaluate the bacterial activity. The physic-mechanic properties are also evaluated to discuss the effect of the MICP process on expansive soil. Results indicate that the MICP method achieves the mitigation of expansion. The treated soil has a low proportion of fine particles (< 5 ㎛), the plasticity index significantly decreases, and strength values improve much. MICP process has a significant cementation effect on the soil matrix. Moreover, the infiltration model test presents the coating effect on the topsoil. According to the relation between the CaCO₃ content and the treatment effect, the topsoil has better treatment than the deeper soil.

• 전기학회논문지
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• 제64권7호
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• pp.1114-1124
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• 2015

In this paper, precipitation / non-precipitation pattern classification of meteorological radar data is conducted by using neuro-fuzzy algorithm. Structure expression of meteorological radar data information is analyzed in order to effectively classify precipitation and non-precipitation. Also diverse input variables for designing pattern classifier could be considered by exploiting the quantitative as well as qualitative characteristic of meteorological radar data information and then each characteristic of input variables is analyzed. Preferred pattern classifier can be designed by essential input variables that give a decisive effect on output performance as well as model architecture. As the proposed model architecture, neuro-fuzzy algorithm is designed by using FCM-based radial basis function neural network(RBFNN). Two parts of classifiers such as instance classifier part and echo classifier part are designed and carried out serially in the entire system architecture. In the instance classifier part, the pattern classifier identifies between precipitation and non-precipitation data. In the echo classifier part, because precipitation data information identified by the instance classifier could partially involve non-precipitation data information, echo classifier is considered to classify between them. The performance of the proposed classifier is evaluated and analyzed when compared with existing QC method.

• 대기
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• 제18권4호
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• pp.387-395
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• 2008

The impact of horizontal resolution on a regional climate model was investigated by simulating precipitation over the Korean Peninsula. As a regional climate model, the SNURCM(Seoul National University Regional Climate Model) has 21 sigma layers and includes the NCAR CLM(National Center for Atmospheric Research Community Land Model) for land-surface model, the Grell scheme for cumulus convection, the Simple Ice scheme for explicit moisture, and the MRF(Medium-Range Forecast) scheme for PBL(Planetary Boundary Layer) processing. The SNURCM was performed with 20 km resolution for Korea and 60 km resolution for East Asia during a 20-year period (1980-1999). Although the SNURCM systematically underestimated precipitation over the Korean Peninsula, the increase of model resolution simulated more precipitation in the southern region of the Korean Peninsula, and a more accurate distribution of precipitation by reflecting the effect of topography. The increase of precipitation was produced by more detailed terrain data which has a 10 minute terrain in the 20 km resolution model compared to the 30 minute terrain in the 60 km resolution model. The increase in model resolution and more detailed terrain data played an important role in generating more precipitation over the Korean Peninsula. While the high resolution model with the same terrain data resulted in increasing of precipitation over the Korean Peninsula including the adjoining sea, the difference of the terrain data resolution only influenced the precipitation distribution of the mountainous area by increasing the amount of non-convective rain. In conclusion, the regional climate model (SNURCM) with higher resolution simulated more precipitation over the Korean Peninsula by reducing the systematic underestimation of precipitation over the Korean Peninsula.