• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.5
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• pp.977-984
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• 2000

Atmospheric boundary layer over equatorial maritime continent was analyzed with Doppler radar. An L-band (1357.5 MHz) boundary layer radar (BLR) has been in continuous successful operation in Selpong, Indonesia(6.45, 106.7E), since November 1992. The performance of the BLR with respect to the observation height range and the wind measurement reliability has been examined on the basis of simultaneous meteorological observations. In the dry season (10-12 October 1993), we have found two types of strong echo structures appearing systematically in the equatorial planetary boundary layer with diurnal variations on clear days. The first type is the striking appearance of a strong echo layer ascending from below 300 m (in the morning) to above 3-5 km (in the afternoon), which is identified with a diurnal variation of the top of the mixing planetary boundary layer. As expected, it is higher in the Indonesian equatorial region than in midlatitudes. Another type is a layered echo appearing at 2-3 km heights from nighttime to morning, which seem to be coincident with humidity gaps. In the rainy season (20-21 February 1994), the height of the atmospheric mining was lower than that in the dry season.

• Journal of Korea Society of Industrial Information Systems
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• v.5 no.3
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• pp.44-50
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• 2000

This paper presents the effective data processing system of a transportable meteorological radar(DWSR-200x). Transportable meteorological radar is useful as it can be moved to target area for special purpose. First of all, to use this radar effectively, it is desirable that the data transmitting should be taken place between the radar system and the data center located in a distance. From this raw data we can analyze the property of atmosphere, as well as sore and display the demanded shape of users. In this paper, we make use of wireless LAN that communicates the data between the radar system and the information center. And the display program of transportable radar is developed with transmitted data. It provides meteorologists with the echo searching function in real time and dictionary faculty using the graphic and multimedia data.

• Journal of the Korean Association of Geographic Information Studies
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• v.5 no.4
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• pp.1-8
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• 2002

Comparison between the Tropical Rainfall Measuring Mission(TRMM)/Precipitation Radar(PR) and the C-band doppler radar at Cheju, Kunsan and Pusan, operated by the Korean Meteorological Administration (KMA), is conducted for validation of the surface precipitation structure, and for calibration of KMA radar instrument. Data used in validation was selected for seven rain events in the south region of about $36^{\circ}N$ and at TRMM overflight in Korea, during the summer season of 2000. Quantitatively comparing radar reflectivities from two different platforms that have different view angles, bandwidths and frequencies is a challenging task. For the comparison, the precipitation patterns are projected on the same area for TRMM PR. Through the comparison, it is realized that the reflectivity from ground-based radar is under estimated, compared to TRMM/PR observations. We discuss that is underestimation may be produced by many factors(system performance, topography, etc).

• Journal of the Korean earth science society
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• v.36 no.1
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• pp.109-124
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• 2015

A novel approach, hybrid surface rainfall (KNU-HSR) technique developed by Kyungpook Natinal University, was utilized for improving the radar rainfall estimation. The KNU-HSR technique estimates radar rainfall at a 2D hybrid surface consistings of the lowest radar bins that is immune to ground clutter contaminations and significant beam blockage. Two HSR techniques, static and dynamic HSRs, were compared and evaluated in this study. Static HSR technique utilizes beam blockage map and ground clutter map to yield the hybrid surface whereas dynamic HSR technique additionally applies quality index map that are derived from the fuzzy logic algorithm for a quality control in real time. The performances of two HSRs were evaluated by correlation coefficient (CORR), total ratio (RATIO), mean bias (BIAS), normalized standard deviation (NSD), and mean relative error (MRE) for ten rain cases. Dynamic HSR (CORR=0.88, BIAS= $-0.24mm\;hr^{-1}$, NSD=0.41, MRE=37.6%) shows better performances than static HSR without correction of reflectivity calibration bias (CORR=0.87, BIAS= $-2.94mm\;hr^{-1}$, NSD=0.76, MRE=58.4%) for all skill scores. Dynamic HSR technique overestimates surface rainfall at near range whereas it underestimates rainfall at far ranges due to the effects of beam broadening and increasing the radar beam height. In terms of NSD and MRE, dynamic HSR shows the best results regardless of the distance from radar. Static HSR significantly overestimates a surface rainfall at weaker rainfall intensity. However, RATIO of dynamic HSR remains almost 1.0 for all ranges of rainfall intensity. After correcting system bias of reflectivity, NSD and MRE of dynamic HSR are improved by about 20 and 15%, respectively.

• Journal of Korea Water Resources Association
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• v.47 no.12
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• pp.1227-1237
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• 2014

Dual-polarization can distinguish precipitation type and dual-polarization is provide not only meteorological phenomena in the atmosphere but also non-precipitation echoes. Therefore dual-polarization radar can improve radar estimates of rainfall. However polarimetric measurements by transmitting vertically vibration waves and horizontally vibrating waves simultaneously is contain systematic bias of the radar itself. Thus the calibration bias is necessary to improve quantitative precipitation estimation. In this study, the calibration bias of reflectivity (Z) and differential reflectivity ($Z_{DR}$) from the Bislsan dual-polarization radar is calculated using the 2-Dimensional Video Disdrometer (2DVD) data. And an improvement in rainfall estimation is investigated by applying derived calibration bias. A total of 33 rainfall cases occurring in Daegu from 2011 to 2012 were selected. As a results, the calibration bias of Z is about -0.3 to 5.5 dB, and $Z_{DR}$ is about -0.1 dB to 0.6 dB. In most cases, the Bislsan radar generally observes Z and $Z_{DR}$ variables lower than the simulated variables. Before and after calibration bias, compared estimated rainfall from the dual-polarization radar with AWS rain gauge in Daegu found that the mean bias has fallen by 1.69 to 1.54 mm/hr, and the RMSE has decreased by 2.54 to 1.73 mm/hr. And estimated rainfall comparing to the surface rain gauge as ground truth, rainfall estimation is improved about 7-61%.

• Journal of the Korean earth science society
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• v.38 no.7
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• pp.496-510
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• 2017

This study presents a new nationwide quantitative precipitation estimation (QPE) based on the hybrid surface rainfall (HSR) technique using the weather radar network of Korea Meteorological Administration (KMA). This new nationwide HSR is characterized by the synthesis of reflectivity at the hybrid surface that is not affected by ground clutter, beam blockage, non-meteorological echoes, and bright band. The nationwide HSR is classified into static (STATIC) and dynamic HSR (DYNAMIC) mosaic depending on employing a quality control process, which is based on the fuzzy logic approach for single-polarization radar and the spatial texture technique for dual-polarization radar. The STATIC and DYNAMIC were evaluated by comparing with official and operational radar rainfall mosaic (MOSAIC) of KMA for 10 rainfall events from May to October 2014. The correlation coefficients within the block region of STATIC, DYNAMIC and MOSAIC are 0.52, 0.78, and 0.69, respectively, and their mean relative errors are 34.08, 30.08, and 40.71%.

• Journal of Korea Water Resources Association
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• v.48 no.5
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• pp.409-423
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• 2015

In this study the very short-term rainfall forecasting and storm water forecasting using the weather radar data were implemented in an urban stream basin. As forecasting time increasing, the very short-term rainfall forecasting results show that the correlation coefficient was decreased and the root mean square error was increased and then the forecasting model accuracy was decreased. However, as a result of the correlation coefficient up to 60-minute forecasting time is maintained 0.5 or higher was obtained. As a result of storm water forecasting in an urban area, the reduction in peak flow and outflow volume with increasing forecasting time occurs, the peak time was analyzed that relatively matched. In the application of storm water forecasting by radar rainfall forecast, the errors has occurred that we determined some of the external factors. In the future, we believed to be necessary to perform that the continuous algorithm improvement such as simulation of rapid generation and disappearance phenomenon by precipitation echo, the improvement of extreme rainfall forecasting in urban areas, and the rainfall-runoff model parameter optimizations. The results of this study, not only urban stream basin, but also we obtained the observed data, and expand the real-time flood alarm system over the ungaged basins. In addition, it is possible to take advantage of development of as multi-sensor based very short-term rainfall forecasting technology.

• Atmosphere
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• v.21 no.3
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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.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.985-985
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• 2012

최근 지구온난화 및 기후변화로 인해 단시간에 높은 강우강도를 가지고 발생하는 집중호우 홍수 등의 위험기상으로 인한 인명 및 재산피해가 빈번하게 발생하고 있어 초단기 및 단기 강수 예측에 대한 중요성이 부각되고 있다. 단기 강수예측모델은 다양한 관측자료의 사용과 자료동화기법의 개발로 예측능력이 크게 향상되었지만 수치모델의 고유특성인 스핀업(spin-up) 문제로 1~6시간까지 강수예측성능에 한계를 보인다. 반면 초단기 강수예측모델은 레이더기반으로 외삽법을 이용하여 1~3시간까지 높은 정확도의 강수예측을 하지만 강수에코의 생성 소멸의 물리과정을 포함하지 않아 3시간 이후의 정확도가 낮다. 이러한 단기 및 초단기 강수예측모델의 장점을 반영하여 최적 강수예측 자료 생산을 위한 연구를 수행하였다. 이를 위해 초단기 및 단기 강수예측모델의 예측성능을 평가하였으며 모델의 예측성능 기반의 최적 강수자료 병합기법을 개발하였다. 향후 최적 강수예측 자료 생산체계가 구축되면 수문관련 유관기관에서 하천관리에 사용하는 유량예측모델에 시 공간적 고해상도의 강수예측정보를 제공하여 수문분야의 유량예측 정확도 행상에 기여할 것으로 기대된다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.922-934
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• 2015

In this paper, The classification between precipitation echo(PRE) and non-precipitation echo(N-PRE) (including ground echo and clear echo) is carried out from weather radar data using neuro-fuzzy algorithm. In order to classify between PRE and N-PRE, Input variables are built up through characteristic analysis of radar data. First, the event classifier as the first classification step is designed to classify precipitation event and non-precipitation event using input variables of RBFNNs such as DZ, DZ of Frequency(DZ_FR), SDZ, SDZ of Frequency(SDZ_FR), VGZ, VGZ of Frequency(VGZ_FR). After the event classification, in the precipitation event including non-precipitation echo, the non-precipitation echo is completely removed by the echo classifier of the second classifier step that is built as Type-2 FCM based RBFNNs. Also, parameters of classification system are acquired for effective performance using PSO(Particle Swarm Optimization). The performance results of the proposed echo classifier are compared with CZ. In the sequel, the proposed model architectures which use event classifier as well as the echo classifier of Interval Type-2 FCM based RBFNN show the superiority of output performance when compared with the conventional echo classifier based on RBFNN.