• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.5
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• pp.676-682
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• 2014

In this paper, precipitation echo(PRE) and non-precipitaion echo(N-PRE)(including ground echo and clear echo) through weather radar data are identified with the aid of neuro-fuzzy algorithm. The accuracy of the radar information is lowered because meteorological radar data is mixed with the PRE and N-PRE. So this problem is resolved by using RBFNN and judgement module. Structure expression of weather radar data are analyzed in order to classify PRE and N-PRE. Input variables such as Standard deviation of reflectivity(SDZ), Vertical gradient of reflectivity(VGZ), Spin change(SPN), Frequency(FR), cumulation reflectivity during 1 hour(1hDZ), and cumulation reflectivity during 2 hour(2hDZ) are made by using weather radar data and then each characteristic of input variable is analyzed. Input data is built up from the selected input variables among these input variables, which have a critical effect on the classification between PRE and N-PRE. Echo judgment module is developed to do echo classification between PRE and N-PRE by using testing dataset. Polynomial-based radial basis function neural networks(RBFNNs) are used as neuro-fuzzy algorithm, and the proposed neuro-fuzzy echo pattern classifier is designed by combining RBFNN with echo judgement module. Finally, the results of the proposed classifier are compared with both CZ and DZ, as well as QC data, and analyzed from the view point of output performance.

• Atmosphere
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• v.28 no.4
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• pp.443-455
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• 2018

Nowcasting algorithms using weather radar data are mostly based on extrapolating the radar echoes. We estimate the echo motion vectors that are used to extrapolate the echo properly. Therefore, understanding the general characteristics of these motion vectors is important to improve the performance of nowcasting. General characteristics of radar-based motions are analyzed for warm season precipitation over Jeju region. Three-year summer season data (June~August, 2011~2013) from two radars (GSN, SSP) in Jeju are used to obtain echo motion vectors that are retrieved by Variational Echo Tracking (VET) method which is widely used in nowcasting. The highest frequency occurs in precipitation motion toward east-northeast with the speed of $15{\sim}16m\;s^{-1}$ during the warm season. Precipitation system moves faster and eastward in June-July while it moves slower and northeastward in August. The maximum frequency of speed appears in $10{\sim}20m\;s^{-1}$ and $5{\sim}10m\;s^{-1}$ in June~July and August respectively while average speed is about $14{\sim}15m\;s^{-1}$ in June~July and $8m\;s^{-1}$ in August. In addition, the direction of precipitation motion is highly variable in time in August. The speed of motion in Lee side of the island is smaller than that of the windward side.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.204-204
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• 2020

환경부는 기존 대형 강우레이더 관측망에 대한 동해안 지역 관측공백 해소와 집중호우에 의한 재해예방을 목적으로 2기의 전파강수관측소를 삼척과 울진 통고산에 구축 운영하고 있다. 본 연구에서는 삼척 및 울진 전파강수관측소 관측 자료 품질 향상을 위한 다양한 품질 분석 기법을 소개하고 그 결과를 제시한다. 설치된 전파강수관측소의 시스템 특성 중 하나인 Short/Long 펄스 신호에 따른 자료의 연속/불연속성 및 피뢰침에 의한 자료 품질, 그리고 강수에 의한 신호감쇠에 따른 유효관측거리 등을 분석하였다. 이러한 분석을 기반으로 신호보정옵셋 및 피뢰침 위치 등을 조정하여 자료 품질을 향상하였다. 또한 삼척과 울진 전파강수관측소를 대상으로 분포형 비차등위상차 산정 기술을 적용하고 그 결과를 분석하였다. 비차등위상차는 시스템 편차나 우박 등의 영향에서 자유로워 특히 전파강수관측소와 같은 X 밴드 정량강우 추정에서 중요하다. 일반적으로 비차등위상차는 차등위상차에 대한 필터링 기법으로 산출하는데, 이 방법은 약한 강수에 대해 변동성이 크며 지형에코 등에 의해 영향을 크게 받는다는 단점이 있다. 본 연구에서는 일반적인 필터링 기법에 의한 비차등위상차와 분포형 기법을 적용한 비차등위상차에 대해 비교 분석을 하였다. 전파강수관측소 강우 자료를 이용한 분포형 비차등위상차 시험적용 결과 기존 비차등위상차에 비해 정성적으로 우위를 보임을 알 수 있었다.

• Korean Journal of Remote Sensing
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• v.34 no.3
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• pp.465-480
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• 2018

On 20 January 2017, the fresh snow cover which is more than 20 cm, accompaning with lightning occurred over Yeongdong coastal region for the first 3-hour of the heavy snowfall event. This study analyzed sounding observations in the heavy snow period which were including the measurements of wind profiler, radiometer and rawinsonde. The features examined from the vertical wind and temperature data at the two adjacent stations, Bukgangneung and Gangneung-Wonju National University, are summarized as follows: 1) The strong (30-40 kts) north-east winds were observed in the level from 2 to 6 km. The Strong atmospheric instability was found from 4 to 6 km, in which the lapse rate of temperature was about $-18^{\circ}C\;km^{-1}$. These features indicate that the deep convective cloud develops up to the height of 6 km in the heavy snowfall period, which is shown in the satellite infrared images. 2) The cooling was observed in the level below 1 km. At this time, the surface air temperature at Bukgangneung station decreased by $4^{\circ}C$. The narrow cooling zone estimated from AWS and buoy data was located in east-west direction. These are the features observed in the cold front of extratropical cyclone. The distributions of radar echo and lightning also show the same shape in east-west direction. Therefore, the results indicate that the Yeongdong thundersnow event was the combined precipitation system of deep convective cloud and cold frontal precipitation.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.3
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• pp.171-177
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• 2013

The purpose of this study is to find a scheme to scale down the KMA (Korea Meteorological Administration) digital precipitation maps to the grid cell resolution comparable to the rural landscape scale in Korea. As a result, we suggest two steps procedure called RATER (Radar Assisted Topography and Elevation Revision) based on both radar echo data and a mountain precipitation model. In this scheme, the radar reflection intensity at the constant altitude of 1.5 km is applied first to the KMA local analysis and prediction system (KLAPS) 5 km grid cell to obtain 1 km resolution. For the second step the elevation and topography effect on the basis of 270 m digital elevation model (DEM) which represented by the Parameter-elevation Regressions on Independent Slopes Model (PRISM) is applied to the 1 km resolution data to produce the 270 m precipitation map. An experimental watershed with about $50km^2$ catchment area was selected for evaluating this scheme and automated rain gauges were deployed to 13 locations with the various elevations and slope aspects. 19 cases with 1 mm or more precipitation per day were collected from January to May in 2013 and the corresponding KLAPS daily precipitation data were treated with the second step procedure. For the first step, the 24-hour integrated radar echo data were applied to the KLAPS daily precipitation to produce the 1 km resolution data across the watershed. Estimated precipitation at each 1 km grid cell was then regarded as the real world precipitation observed at the center location of the grid cell in order to derive the elevation regressions in the PRISM step. We produced the digital precipitation maps for all the 19 cases by using RATER and extracted the grid cell values corresponding to 13 points from the maps to compare with the observed data. For the cases of 10 mm or more observed precipitation, significant improvement was found in the estimated precipitation at all 13 sites with RATER, compared with the untreated KLAPS 5 km data. Especially, reduction in RMSE was 35% on 30 mm or more observed precipitation.