• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.360-365
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• 2014

There are many types of advanced devices for weather prediction process such as weather radar, satellite, radiosonde, and other weather observation devices. Among them, the weather radar is an essential device for weather forecasting because the radar has many advantages like wide observation area, high spatial and time resolution, and so on. In order to analyze the weather radar observation result, we should know the inside structure and data. Some non-precipitation echoes exist inside of the observed radar data. And these echoes affect decreased accuracy of weather forecasting. Therefore, this paper suggests a method that could remove line-shaped non-precipitation echo from raw radar data. The line-shaped echoes are distinguished from the raw radar data and extracted their own features. These extracted data pairs are used as learning data for naive bayesian classifier. After the learning process, the constructed naive bayesian classifier is applied to real case that includes not only line-shaped echo but also other precipitation echoes. From the experiments, we confirm that the conclusion that suggested naive bayesian classifier could distinguish line-shaped echo effectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.545-550
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• 2013

In pattern recognition field, data classification is an essential process for extracting meaningful information from data. Adaptive boosting algorithm, known as AdaBoost algorithm, is a kind of improved boosting algorithm for applying to real data analysis. It consists of weak classifiers, such as random guessing or random forest, which performance is slightly more than 50% and weights for combining the classifiers. And a strong classifier is created with the weak classifiers and the weights. In this paper, a research is performed using AdaBoost algorithm for detecting chaff echo which has similar characteristics to precipitation echo and interrupts weather forecasting. The entire process for implementing chaff echo classifier starts spatial and temporal clustering based on similarity with weather radar data. With them, learning data set is prepared that separated chaff echo and non-chaff echo, and the AdaBoost classifier is generated as a result. For verifying the classifier, actual chaff echo appearance case is applied, and it is confirmed that the classifier can distinguish chaff echo efficiently.

• Journal of the Korean earth science society
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• v.40 no.1
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• pp.9-23
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• 2019

Super-refraction of radar beams tends to occur primarily under a particular vertical structure of temperature and water vapor pressure profiles. A quality control process for the removal of anomalous propagation (AP) ehcoes are required because APs are easily misidentified as precipitation echoes. For this purpose, we collected X-band polarimetric radar parameters (differential reflectivity, cross-correlation coefficient, and differential phase) only including non-precipitation echoes (super-refraction and clear-sky ground echoes) and precipitation echoes, and compared the echo types regarding the relationships among radar reflectivities, polarimetric parameters, and the membership functions. We developed a removal algorithm for the non-precipitation echoes using the texture approach for the polarimetric parameters. The presented algorithm is qualitatively validated using the S-band Jindo radar in Jeollanam-do. Our algorithm shows the successful identification and removal of AP echoes.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.99-106
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• 2015

In this study, we propose Radial basis function Neural Network(RBFNN) using Recursive Weighted Least Square Estimation(RWLSE) to effectively deal with big data class meteorological radar data. In the condition part of the RBFNN, Fuzzy C-Means(FCM) clustering is used to obtain fitness values taking into account characteristics of input data, and connection weights are defined as linear polynomial function in the conclusion part. The coefficients of the polynomial function are estimated by using RWLSE in order to cope with big data. As recursive learning technique, RWLSE which is based on WLSE is carried out to efficiently process big data. This study is experimented with both widely used some Machine Learning (ML) dataset and big data obtained from meteorological radar to evaluate the performance of the proposed classifier. The meteorological radar data as big data consists of precipitation echo and non-precipitation echo, and the proposed classifier is used to efficiently classify these echoes.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.586-591
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• 2014

In this study, we introduce Radial Basis Function Neural Networks(RBFNNs) classifier using Artificial Bee Colony(ABC) algorithm in order to classify between precipitation event and non-precipitation event from given radar data. Input information data is rebuilt up through feature analysis of meteorological radar data used in Korea Meteorological Administration. In the condition phase of the proposed classifier, the values of fitness are obtained by using Fuzzy C-Mean clustering method, and the coefficients of polynomial function used in the conclusion phase are estimated by least square method. In the aggregation phase, the final output is obtained by using fuzzy inference method. The performance results of the proposed classifier are compared and analyzed by considering both QC(Quality control) data and CZ(corrected reflectivity) data being used in Korea Meteorological Administration.

• Atmosphere
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• v.24 no.1
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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.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.458-458
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• 2012

Climate extreme variability is a major cause of disaster such as flood and drought types occurred in Korea and its effects is also more severe damage in last decades which can be danger mature events in the future. The main aim of this study was to assess the effectives of climate change on drought for an agriculture as Nakdong basin in Korea using climate change data in the future from data of General Circulation Models (GCM) of ECHO-G, with the developing countries like Korea, the developed climate scenario of medium-high greenhouse gas emission was proposed of the SRES A2. The Standardized Precipitation Index (SPI) was applied for drought evaluation. The drought index (SPI) applied for sites in catchment and it is evaluated accordingly by current and future precipitation data, specific as determined for data from nine precipitation stations with data covering the period 1980-2009 for current and three periods 2010-2039, 2040-2069 and 2070-2099 for future; time scales of 3month were used for evaluating. The results determined drought duration, magnitude and spatial extent. The drought in catchment act intensively occurred in March, April, May and November and months of drought extreme often appeared annual in May and November; drought frequent is a non-uniform cyclic pattern in an irregular repetitive manner, but results showed drought intensity increasing in future periods. The results indicated also spatial point of view, the SPI analysis showed two of drought extents; local drought acting on one or more one of sites and entire drought as cover all of site in catchment. In addition, the meteorology drought simulation maps of spatial drought representation were carried out with GIS software to generate for some drought extreme years in study area. The method applied in this study are expected to be appropriately applicable to the evaluation of the effects of extreme hydrologic events, the results also provide useful for the drought warning and sustainable water resources management strategies and policy in agriculture basins.

• Journal of Integrative Natural Science
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• v.10 no.4
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• pp.216-224
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• 2017

In the terrain of the Korean peninsula, mountainous and flat lands are complexly distributed in small areas. Therefore, local severe weather develops and disappears in a short time due to the influence of the terrain. Particularly in the case of local severe weather with heavy wind that has the greatest influence on aviation meteorology, the scale is very small, and it occurs and disappears in a short time, so it is impossible to predict with fragmentary data alone. So, we use weather radar to detect and predict local severe weather. However, due to the development of wireless communication services and the rapid increase of wireless devices, radio wave jamming and interference problems occur. In this research, we confirmed through the cases that when the radio interference echo which is one of the non-precipitation echoes that occur during the operation of the weather radar is displayed in the image, its form and shape are shown in a long bar shape, and have a strong dBZ. We also found the cause of the interference through the radio tracking process, and solved through the frequency channel negotiation and AP output minimizing. The more wireless devices increase as information communication technology develops in the future, the more emphasized the problem of radio wave interference will be, and we must make the radio interference eliminated through the development of the radio interference cancellation algorithm.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.61-68
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• 2019

In addition to non-meteorological echoes, meteorological echoes with large scattering effects, such as precipitation, cause errors in wind data measured by wind profiler. In the rainfall situation, the Doppler spectrum of wind profiler shows both the rainfall signal and the atmospheric signal as two peaks. The vertical radial velocity is very large due to the falling rain drop. The radial velocity contaminated by rainfall decreases the accuracy of the horizontal wind vector and leads to inaccurate weather analysis. In this study, we developed an algorithm to process raw data of wind profiler and distinguished rainfall signal and wind signal by partitioning bimodal peak for Doppler spectrum in rainfall environment.