• 자원리싸이클링
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• 제29권5호
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• pp.28-37
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• 2020

본 연구에서는 알칼리 영역의 소듐바나데이트(NaVO₃) 수용액을 출발물질로 사용하여 침전온도, 염화암모늄 첨가량 및 첨가방법, 수용액의 바나듐 및 수산화나트륨 함량 등이 암모늄메타바나데이트 침전거동에 미치는 영향을 알아보았다. 수용액 pH가 감소할수록, 염화암모늄 첨가량 그리고 수용액의 바나듐 함량이 증가할수록 암모늄메타바나데이트 침전률이 증가하였다. 본 연구조건에서 90% 이상의 암모늄메타바나데이트 침전률을 얻기 위한 기본 조건은 수용액 바나듐 함량 10,000mg/L, 고체 염화암모늄 첨가량 2당량, 침전온도 상온, 침전시간 2시간 이었다. 암모늄메타바나데이트는 침전속도 증가에 따라 침전물 크기가 감소하였으며, 특히 염화암모늄을 액체로 투입할 때, 침전속도는 가장 느리며 침전물 크기는 가장 크게 나타났다. 염화암모늄을 고체로 첨가하여 1차 침전반응 후, 새로운 반응물을 첨가하여 2차 침전반응을 시킬 때, 고체 염화암모늄을 첨가한 침전반응은 수용액에 존재하는 침전물에 영향을 받지 않았다. 그러나 염화암모늄 수용액을 첨가하였을 때는 수용액에 존재하는 침전물 표면에 침적되어 그 크기를 증가시켰다. 수용액 온도에 따른 암모늄메타바나데이트 용해도 차이에 의하여, 바나듐 함량 10,000mg/L 수용액에서는 침전온도가 암모늄메타바나데이트 침전에 영향을 미치며, 바나듐 함량 30,000mg/L 이상의 고농도 수용액에서는 침전온도가 침전반응에 영향을 미치지는 못하였다.

• 한국환경과학회지
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• 제19권9호
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• pp.1109-1118
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• 2010

Characteristics of precipitation and temperature in Ulleung-do and Dok-do were analyzed with hourly accumulated precipitation and mean temperature data obtained from Automatic Weather System(AWS) for latest four years(2005~2008). In Ulleung-do, total annual mean precipitation for this period is 1,574.4 mm, which shows larger amount than 1434.2 mm of whole Korean peninsula for latest 10 years(1999~2008) and 1,236.2 mm at Ulleung-do on common years(1971~2000), shows that the trend of precipitation gradually increases during the recent years. This amount is also 1.4 times larger than the total annual mean precipitation of 660.1 mm in Dok-do. Mean precipitation intensity(mm $h^{-1}$) at each time of a day in each month at Ulleung-do represents that the maximum values larger than $3.0\;mm\;h^{-1}$ were shown in May and on 0200 LST, whereas these were found in August and 0700 LST with $3.1\;mm\;h^{-1}$ in Dok-do. The difference of the precipitation amount and its intensity between Uleung-do and Dok-do is explained by the topological effect came from each covering area, and this fact is also identified from similar comparison of the precipitation characteristics for the islands in West Sea. The annual mean temperature of $14.0^{\circ}C$ in Dok-do is $1.2^{\circ}C$ higher than that of $12.8^{\circ}C$ in Ulleung-do. Trends of monthly mean temperature in both islands are shown to increase for the observed period.

• 대기
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• 제24권3호
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• pp.433-444
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• 2014

Although the Radar-AWS Rainrate (RAR) calculation system operated by Korea Meteorological Administration estimated precipitation using 2-dimensional composite components of single polarization radars, this system has several limitations in estimating the precipitation accurately. To to overcome limitations of the RAR system, the Korea Meteorological Administration developed and operated the RMQ (Radar-based Multi-sensor Quantitative Precipitation Estimation) system, the improved version of NMQ (National Mosaic and Multi-sensor Quantitative Precipitation Estimation) system of NSSL (National Severe Storms Laboratory) for the Korean Peninsula. This study introduced the RMQ system domestically for the first time and verified the precipitation estimation performance of the RMQ system. The RMQ system consists of 4 main parts as the process of handling the single radar data, merging 3D reflectivity, QPE, and displaying result images. The first process (handling of the single radar data) has the pre-process of a radar data (transformation of data format and quality control), the production of a vertical profile of reflectivity and the correction of bright-band, and the conduction of hydrid scan reflectivity. The next process (merger of 3D reflectivity) produces the 3D composite reflectivity field after correcting the quality controlled single radar reflectivity. The QPE process classifies the precipitation types using multi-sensor information and estimates quantitative precipitation using several Z-R relationships which are proper for precipitation types. This process also corrects the precipitation using the AWS position with local gauge correction technique. The last process displays the final results transformed into images in the web-site. This study also estimated the accuracy of the RMQ system with five events in 2012 summer season and compared the results of the RAR (Radar-AWS Rainrate) and RMQ systems. The RMQ system ($2.36mm\;hr^{-1}$ in RMSE on average) is superior to the RAR system ($8.33mm\;hr^{-1}$ in RMSE) and improved by 73.25% in RMSE and 25.56% in correlation coefficient on average. The precipitation composite field images produced by the RMQ system are almost identical to the AWS (Automatic Weather Statioin) images. Therefore, the RMQ system has contributed to improve the accuracy of precipitation estimation using weather radars and operation of the RMQ system in the work field in future enables to cope with the extreme weather conditions actively.

• 한국지구과학회지
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• 제26권5호
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• pp.417-428
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• 2005

아시아의 동안에 위치한 한반도는 수리적, 지리적 요인에 의해 지역에 따라 강수현상 및 탁월 일기의 다소와 그 계절변화가 크다. 이러한 탁월한 날씨의 특징은 한국의 하계의 강수출현율과 그 순변화에 잘 반영되고 있다. 본 논문은 한국의 78개 관측지점의 하계강수량$(1991\~2003)$의 순별 평균값에 대해 주성분분석법을 응용하여 하계강수량의 순변화형을 추출하고, 그의 공간스케일과 강수량의 다소에 따라 강수지역구분을 한 것이다. 주성분 분석에 의해 추출된 주성분 벡터와 진폭계수(Rs)에 따라 하계 강수량 순변화의 전형적인 특징은 두 개의 순변화형으로 표현되며 그 누적기여율은 $64.3\%$이다. 또한 한국의 하계 강수량의 순변화형은 $A\~K$형까지 9개가 추출되었고, 강수지역은 17개형으로 분류되었다.

• 대기
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• 제30권4호
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• pp.335-346
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• 2020

Three free parameters included in a cumulus parameterization are optimized by using micro-genetic algorithm for three precipitation cases occurred in the Korea Peninsula during the summer season in order to reduce biases in a regional model associated with the uncertainties of the parameters and thus to improve the predictability of precipitation. The first parameter is the one that determines the threshold in convective trigger condition. The second parameter is the one that determines boundary layer forcing in convective closure. Finally, the third parameter is the one used in calculating conversion parameter determining the fraction of condensate converted to convective precipitation. Optimized parameters reduce the occurrence of convections by suppressing the trigger of convection. The reduced convection occurrence decreases light precipitation but increases heavy precipitation. The sensitivity experiments are conducted to examine the effects of the optimized parameters on the predictability of precipitation. The predictability of precipitation is the best when the three optimized parameters are applied to the parameterization at the same time. The first parameter most dominantly affects the predictability of precipitation. Short-range forecasts for July 2018 are also conducted to statistically assess the precipitation predictability. It is found that the predictability of precipitation is consistently improved with the optimized parameters.

• 대기
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• 제24권2호
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• pp.159-171
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• 2014

This study investigated the temporal and spatial characteristics of summertime (June-August) precipitation over Korean peninsula, using Korea Meteorological Administration (KMA)is Automated Synoptic Observing System (ASOS) data for the period of 1973-2010 and Automatic Weather System (AWS) data for the period of 1998-2010.The authors looked through climatological features of the summertime precipitation, then examined the degree of locality of the precipitation, and probable precipitation amount and its return period of 100 years (i.e., an extreme precipitation event). The amount of monthly total precipitation showed increasing trends for all the summer months during the investigated 38-year period. In particular, the increasing trends were more significant for the months of July and August. The increasing trend of July was seen to be more attributable to the increase of precipitation intensity than that of frequency, while the increasing trend of August was seen to be played more importantly by the increase of the precipitation frequency. The e-folding distance, which is calculated using the correlation of the precipitation at the reference station with those at all other stations, revealed that it is August that has the highest locality of hourly precipitation, indicating higher potential of localized heavy rainfall in August compared to other summer months. More localized precipitation was observed over the western parts of the Korean peninsula where terrain is relatively smooth. Using the 38-years long series of maximum daily and hourly precipitation as input for FARD2006 (Frequency Analysis of Rainfall Data Program 2006), it was revealed that precipitation events with either 360 mm $day^{-1}$ or 80 mm $h^{-1}$ can occur with the return period of 100 years over the Korean Peninsula.

• 대기
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• 제22권4호
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• pp.449-454
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• 2012

This study presents the long-term variability of spring precipitation over the Korean peninsula. It is found that the significant interdecadal change in the spring precipitation has occurred around year 1991. Over the Korean peninsula the precipitation for the post-1991 period increased by about 30 mm per year in CMAP and station-measured data compared to the precipitation prior to year 1991. Due to an increased baroclinicity during the later period, the low-level negative pressure anomaly has developed with its center over northern Japan. Korea is situated at the western end of the negative pressure anomaly, receiving moisture from westerly winds and producing more precipitation. Also, we estimate the change in the near future (years 2020~2040) spring precipitation using six best performing Coupled Model Intercomparison Project 3 (CMIP3) models. These best model ensemble mean shows that spring precipitation is anticipated to increase by about 4% due to the strengthened westerlies accompanied by the northwestern enhancement of the North Pacific subtropical high.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2020년도 학술발표회
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• pp.120-120
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• 2020

Spatial precipitation data is one of the essential components in modeling hydrological problems. The estimation of these data has achieved significant achievements own to the recent advances in remote sensing technology. However, there are still gaps between the satellite-derived rainfall data and observed data due to the significant dependence of rainfall on spatial and temporal characteristics. An effective approach based on the Convolutional Neural Network (CNN) model to correct the satellite-derived rainfall data is proposed in this study. The Mekong River basin, one of the largest river system in the world, was selected as a case study. The two gridded precipitation data sets with a spatial resolution of 0.25 degrees used in the CNN model are APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). In particular, PERSIANN-CDR data is exploited as satellite-based precipitation data and APHRODITE data is considered as observed rainfall data. In addition to developing a CNN model to correct the satellite-based rain data, another statistical method based on standard deviations for precipitation bias correction was also mentioned in this study. Estimated results indicate that the CNN model illustrates better performance both in spatial and temporal correlation when compared to the standard deviation method. The finding of this study indicated that the CNN model could produce reliable estimates for the gridded precipitation bias correction problem.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.143-143
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• 2022

Precipitation plays an essential role in water resources management and disaster prevention. Therefore, the understanding related to spatiotemporal characteristics of rainfall is necessary. Nowadays, highly accurate precipitation is mainly obtained from gauge observation systems. However, the density of gauge stations is a sparse and uneven distribution in mountainous areas. With the proliferation of technology, satellite-based precipitation sources are becoming increasingly common and can provide rainfall information in regions with complex topography. Nevertheless, satellite-based data is that it still remains uncertain. To overcome the above limitation, this study aims to take the strengthens of machine learning to generate a new reanalysis of precipitation data by fusion of multiple satellite precipitation products (SPPs) with gauge observation data. Several machine learning algorithms (i.e., Random Forest, Support Vector Regression, and Artificial Neural Network) have been adopted. To investigate the robustness of the new reanalysis product, observed data were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the machine learning model showed higher accuracy than original satellite rainfall products, and its spatiotemporal variability was better reflected than others. Thus, reanalysis of satellite precipitation product based on machine learning can be useful source input data for hydrological simulations in ungauged river basins.

• 한국지리정보학회지
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• 제16권3호
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• pp.147-163
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• 2013

본 연구의 목적은 3 가지 보간 방법으로 생산한 남한 강수 자료에서 강수 분포의 차이를 비교하는 것이다. 보간된 강수 자료를 생태환경모델링, 수문모델링, 기후변화 영향평가 등의 연구에서 이용할 때 보간 방법에 따른 강수량의 차이는 중요한 정보이기 때문이다. 연구에는 기후변화정보센터에서 PRISM(Precipitation-elevation Regressions on Independent Slopes Model)으로 작성한 강수 자료와 본 연구에서 공동크리깅과 역거리가중법으로 작성한 강수 자료가 사용되었다. 보간된 강수 자료의 공간해상도는 1km이다. 보간 방법 선택에 의해 발생하는 강수량의 차이는 대체로 산지 유역의 자료에서 크다. 특히 군사분계선 주변과 소백산, 월악산, 덕유산, 지리산, 태백산지의 강수 자료에서 보간 방법의 차이에 따라 발생하는 월강수량의 차이는 약 10~20%, 또는 그 이상이었다. 이는 이 지역의 연구에 보간된 강수 자료를 이용할 때 연구에 채택한 보간 방법에 따라 최종 결과가 큰 영향을 받을 수 있다는 것을 의미한다.