• 한국지구과학회지
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• 제34권1호
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• pp.1-12
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• 2013

채프에코는 기상레이더 영상에서 강수에코로 자주 오인된다. 본 연구에서는 채프에코를 효과적으로 제거하기 위하여 국립기상연구소 X-밴드 이중편파레이더에서 관측한 자료를 활용하여 채프에코의 반사도와 각각의 이중편파변수의 관계성을 분석하였다. 특히 본 연구는 채프에코 사례의 입체관측자료 영상에서 채프에코가 발생한 영역만을 선별하여 이중편파변수 자료들을 수집하였다. 전반적으로 이중편파변수(반사도, 차등반사도, 교차상관계수, 차등반사도 표준편차와 비차등위상차)에 대해 채프에코의 변수 값이 강수에코보다 넓은 범위를 차지하며, 채프 섬유의 공간적 배치는 레이더빔에 대해 수평적으로 놓여있는 경향이 나타났다. 일부 강수에코의 교차상관계수는 0.8 이하의 값을 갖고 있기 때문에 채프에코 값과 상당한 중첩이 나타났다. 일반적으로 교차상관계수는 강수에코와 채프에코를 효과적으로 분리하는데 좋은 변수로 고려되고 있지만, 이 연구는 교차상관계수만을 이용하여 채프에코를 제거할 경우에 일부 강수에코가 제거될 가능성이 있음을 시사한다.

• 대기
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• 제21권3호
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• pp.285-300
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• 2011

To discriminate and eliminate chaff echoes in radar measurements, a new removal algorithm in two-dimensional reflectivity composite at the height of 1.5 km has been developed by using the brightness temperature($T_B$) obtained from MTSAT-1R. This algorithm utilizes the fact that chaffs are not appeared in infrared satellite data of MTSAT-1R, but detected in radar measurements due to their significant backscattering in the given radar wavelength. The algorithm is evaluated for three different situations: chaff only, chaff mixed with convective storms, and chaff covered with clouds. The algorithm shows excellent performance for the cases of chaff only and chaff mixed with convective storms. However, the performance of the algorithm significantly depends on the presence of clouds. Thus, the statistical analysis of $T_B$ is performed in order to optimize the monthly threshold.

• 제어로봇시스템학회논문지
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• 제19권10호
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• pp.901-906
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• 2013

Chaff is a kind of matter spreading atmosphere with the purpose of preventing aircraft from detecting by radar. The chaff is commonly composed of small aluminum pieces, metallized glass fiber, or other lightweight strips which consists of reflecting materials. The chaff usually appears on the radar images as narrow bands shape of highly reflective echoes. And the chaff echo has similar characteristics to precipitation echo, and it interrupts weather forecasting process and makes forecasting accuracy low. In this paper, the chaff echo recognizing and removing method is suggested using Bayesian network. After converting coordinates from spherical to Cartesian in UF (Universal Format) radar data file, the characteristics of echoes are extracted by spatial and temporal clustering. And using the data, as a result of spatial and temporal clustering, a classification process for analyzing is performed. Finally, the inference system using Bayesian network is applied. As a result of experiments with actual radar data in real chaff echo appearing case, it is confirmed that Bayesian network can distinguish between chaff echo and non-chaff echo.

• 대기
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• 제24권1호
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• pp.77-87
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• 2014

The radar reflectivity is significantly affected by ground clutter, beam blockage, anomalous propagation (AP), birds, insects, chaff, etc. The quality of radar reflectivity is very important in quantitative precipitation estimation. Therefore, Weather Radar Center (WRC) of Korea Meteorological Administration (KMA) employed two quality control algorithms: 1) Open Radar Product Generator (ORPG) and 2) fuzzy quality control algorithm to improve quality of radar reflectivity. In this study, an occurrence of AP echoes and the performance of both quality control algorithms are investigated. Consequently, AP echoes frequently occur during the spring and fall seasons. Moreover, while the ORPG QC algorithm has the merit of removing non-precipitation echoes, such as AP echoes, it also removes weak rain echoes and snow echoes. In contrast, the fuzzy QC algorithm has the advantage of preserving snow echoes and weak rain echoes, but it eliminates the partial area of the contaminated echo, including the AP echoes.