• Journal of Environmental Science International
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• v.24 no.4
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• pp.525-540
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• 2015

Spatial distribution of precipitation has been estimated based on the local gauge correction (LGC) with a fixed inverse distance weighting (IDW), which is not optimized in taking effective radius into account depending on the radar error. We developed an algorithm, improved local gauge correction (ILGC) which eliminates outlier in radar rainrate errors and optimize distance power for IDW. ILGC was statistically examined the hourly cumulated precipitation from weather for the heavy rain events. Adjusted radar rainfall from ILGC is improved to 50% compared with unadjusted radar rainfall. The accuracy of ILGC is higher to 7% than that of LGC, which resulted from a positive effect of the optimal algorithm on the adjustment of quantitative precipitation estimation from weather radar.

• Journal of the Korean earth science society
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• v.35 no.2
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• pp.115-130
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• 2014

The growing possibility of the disaster due to severe weather calls for disaster prevention and water management measures in South Korea. In order to prevent a localized heavy rain from occurring, the rainfall must be observed and predicted quantitatively. In this study, we developed an adjustment algorithm to estimate the radar precipitation applying to the local gauge correction (LGC) method which uses geostatistical effective radius of errors of the radar precipitation. The effective radius was determined from the errors of radar rainfall using geostatistical method, and we adjusted radar precipitation for four heavy rainfall events based on the LGC method. Errors were decreased by about 40% and 60% in adjusted hourly rainfall accumulation and adjusted total rainfall accumulation for four heavy rainfall events, respectively. To estimate radar precipitation for localized heavy rain events in summer, therefore, we believe that it was appropriate for this study to use an adjustment algorithm, developed herein.

• Atmosphere
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• v.24 no.3
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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.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.23-23
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• 2017

기상레이더는 강우량을 바로 추정하지 못하는 특성으로 인해 정량적 강우산출 과정 중에 다양한 원인으로 인해 불확실성 발생 요소가 존재하나 이를 정량화하고 저감하는데 많은 어려움이 있다. 원인을 살펴보면, 첫째, 기상레이더의 관측에서부터 정량적 강우량 추정까지 일련의 과정에 대한 포괄적으로 불확실성 정량화와 분석이 이루어지지 못하며, 둘째, 전체 불확실성이 어느 정도 되는지 제시하지 못하므로 각 단계별 불확실성이 전체 불확실성 대비 어느 정도 비율이 되는지 제시하지 못한다. 마지막으로 기존 연구들은 불확실성을 줄이고자 여러 방법을 사용하고 있으나 어느 정도 효용성이 있는지 불확실성 측면에서 제시하지 못하고 있다. 따라서 본 연구에서는 Maximum Entropy(ME)와 Uncertainty Delta Method(UMD)를 이용한 접근방법을 제안하여 기상레이더를 활용하여 정량적 강우량을 추정하는 일련의 과정에서 단계별로 불확실성이 어떻게 전파되는지 추정하였다. 본 연구에서는 한반도 전역을 대상으로 2012년 여름철(6~8월)에 발생한 18개 강우사례를 이용하여 품질관리(Open Radar Product Generator 품질관리 알고리즘, fuzzy 알고리즘), 강우추정(Window Probability Matching Method, Marshall-Palmer 관계식), 후처리보정(Local Gauge Correction 기법, Gauge to Radar ratio 기법)단계만을 수행하였으며, 이 결과를 바탕으로 기상레이더 정량적 강우추정 단계별 불확실성을 정량화하였다. 정량화결과, 최종적으로 관측단계의 불확실성보다 최종 불확실성이 줄어들었으나, 강우추정 단계에서 불확실성이 증가하는 것으로 나타났다. 이는 어떤 강우추정식을 적용하느냐에 따라 레이더 강우추정결과가 매우 달라질 수 있음을 의미한다. 따라서 본 연구에서 제시한 불확실성 정량화 방법을 통하여 첫째, 전체 및 단계별 불확실성을 정량화할 수 있고, 둘째, 최종 불확실성 대비 각 단계별 불확실성을 비율을 제시할 수 있으며, 마지막으로 수행단계별로 불확실성 전파과정을 파악할 수 있다. 이는 향후 정량적 레이더 강우추정 과정에 있어서 불확실성을 발생시키는 주요 원인파악과 이에 대한 집중적인 투자를 가능하게 한다. 이러한 과정을 통하여 보다 정확한 정량적 레이더 강우추정이 가능할 것으로 판단된다.

• Journal of Veterinary Clinics
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• v.30 no.1
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• pp.80-85
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• 2013

Twenty one cows in Goyang and Paju cities were referred due to displacement of the abomasum and foreign body in the rumen. Omentopexy and rumenotomy through a right flank celiotomy were performed for treatment of abomasal displacement and the foreign body removal in the rumen. The right paralumbar fossa is clipped and prepared surgically. Local anesthesia is instituted by performing inverted L block. The abdomen was entered through 25 to 30 cm vertical incision in the right paralumbar fossa starting 4 to 5 cm ventral to the transverse processes of the lumbar vertebrae. A 14-gauge needle with rubber tubing attached is inserted to relieve the gaseous pressure and to facilitate further exploration and manipulation. The rumen was gently pulled out of the abdominal cavity and incision was made at the omentum. Rumenotomy was done and retrieved the foreign body. After the rumen was rinsed with sterile saline, the rumen wall was closed by a Lembert suture technique. The omentum was closed by a simple continuous suture. Right flank omentopexy was performed for the surgical correction of abomasal displacement. Recovery results among 21 cows included 9 excellent, 5 good, 2 fair and 5 bad. It was considered that operation of abomasal displacement and foreign body removal in the rumen through right flank celiotomy was a good surgical technique to reduce expenses, surgical pain, and surgery time.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.1
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• pp.23-32
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• 2008

According to recent frequent local flash flood due to climate change, the very short-term rainfall forecast using remotely sensed rainfall like radar is necessary to establish. This research is to evaluate the feasibility of GIS-based distributed model coupled with radar rainfall, which can express temporal and spatial distribution, for multipurpose dam operation during flood season. $Vflo^{TM}$ model was used as physically based distributed hydrologic model. The study area was Yongdam dam basin ($930\;km^2$) and the 3 storm events of local convective rainfall in August 2005, and the typhoon.Ewiniar.and.Bilis.collected from Jindo radar was adopted for runoff simulation. Distributed rainfall consistent with hydrologic model grid resolution was generated by using K-RainVieux, pre-processor program for radar rainfall. The local bias correction for original radar rainfall shows reasonable results of which the percent error from the gauge observation is less than 2% and the bias value is $0.886{\sim}0.908$. The parameters for the $Vflo^{TM}$ were estimated from basic GIS data such as DEM, land cover and soil map. As a result of the 3 events of multiple peak hydrographs, the bias of total accumulated runoff and peak flow is less than 20%, which can provide a reasonable base for building operational real-time short-term rainfall-runoff forecast system.