• Journal of Korea Water Resources Association
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• v.51 no.9
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• pp.813-826
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• 2018

Recently, weather radar system has been widely used for effectively monitoring near real-time weather conditions. The radar rainfall estimates are generally relies on the Z-R equation that is an indirect approximation of the empirical relationship. In this regards, the bias in the radar rainfall estimates can be affected by spatial-temporal variations in the radar profile. This study evaluates the uncertainty of the Z-R relationship while considering the rainfall types in the process of estimating the parameters of the Z-R equation in the context of stochastic approach. The radar rainfall estimates based on the Bayesian inference technique appears to be effective in terms of reduction in bias for a given season. The derived Z-R equation using Bayesian model enables us to better represent the hydrological process in the rainfall-runoff model and provide a more reliable forecast.

• Journal of Korea Water Resources Association
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• v.52 no.9
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• pp.589-601
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• 2019

Various applications of radar rainfall data have been actively employed in the field of hydro-meteorology. Since radar rainfall is estimated by using predefined reflectivity-rainfall intensity relationships, they may not have sufficient reproducibility of observations. In this study, a generalized linear model is introduced to better capture the Z-R relationship in the context of bias correction within a Bayesian regression framework. The bias-corrected radar rainfall with the generalized linear model is more accurate than the widely used mean field bias correction method. In addition, we analyzed variability of the bias correction parameters under various geomorphological conditions such as the height of the weather station and the separation distance from the radar. The identified relationship is finally used to derive a regionalized formula which can provide bias correction factors over the entire watershed. It can be concluded that the bias correction parameters and regionalized method obtained from this study could be useful in the field of radar hydrology.

• Journal of Korea Water Resources Association
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• v.53 no.11
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• pp.999-1013
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• 2020

Weather modification research has been actively performed worldwide, but a technology that can more quantitatively prove the research effects are needed. In this study, the seeding effect, the efficiency of precipitation enhancement in weather modification experiment, was verified using the radar data. Also, the effects of seeding material on hydrometeor change was analyzed. For this, radar data, weather conditions, and numerical simulation data for diffusion were applied. First, a method to analyze the seeding effect in three steps was proposed: before seeding, during seeding, and after seeding. The proposed method was applied to three cases of weather modification experiments conducted in Gangwon-do and the West Sea regions. As a result, when there is no natural precipitation, the radar reflectivity detected in the area where precipitation change is expected was determined as the seeding effect. When natural precipitation occurs, the seeding effect was determined by excluding the effect of natural precipitation from the maximum reflectivity detected. For the application results, it was found that the precipitation intensity increased by 0.1 mm/h through the seeding effect. In addition, it was confirmed that ice crystals, supercooled water droplets, and mixed-phase precipitation were distributed in the seeding cloud. The results of these weather modification research can be used to secure water resources as well as for future study of cloud physics.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.89-90
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• 2016

Anomalous propagation echo is a kind of abnormal radar signal occurred by irregularly refracted radar beam caused by temperature or humidity. The echo frequently appears in ground-based weather radar. In order to improve accuracy of weather forecasting, it is important to analyze radar data precisely. Therefore, there are several ongoing researches about identifying the anomalous propagation echo all over the world. This paper conducts researches about a classification method which can distinguish anomalous propagation echo in the radar data using naive Bayes classifier and unique attributes of the echo such as reflectivity, altitude, and so on. It is confirmed that the fine classification results are derived by verifying the suggested naive Bayes classifier using actual appearance cases of the echo.

• 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.

• Journal of Korea Water Resources Association
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• v.38 no.12 s.161
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• pp.1039-1049
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• 2005

The objective of this study is to produce optimal radar-derived rainfall for hydrologic utilization. The ground clutter and beam blockage effects from Mt. Kwanak station (E.L 608m) are removed from radar reflectivities by POD analysis. The reflectivities are used to produce radar rainfall data in the form of rain rates (mm/h) by the application of the Marshall-Palmer reflectivity versus rainfall relationship. However, these radar-derived rainfall are underestimated in temporal and spatial scale compared with observed one, so it is necessary to hire a correction scheme based on the gauge-to-radar (G/R) statistical adjustment technique. The selected watershed for studying the real-time correction of radar-rainfall estimation is the Soyang dam site, which is located approximately 100km east of Kwanak radar station. The results indicate that adjusted radar rainfall with the gauge measurement have reasonal G/R ratio ranged on 0.95-1.32 and less uncertainty with that mean standard deviation of G/R ratio are decreased by $9-28\%$. Mean areal precipitation from adjusted radar rainfall are well agreed to the observed one on the Soyang River watershed. It is concluded that the real-time bias adjustment scheme is useful to estimate accurate basin-based radar rainfall for hydrologic application.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.628-631
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• 2008

For the remote sensing purpose, radar systems extract the target information, such as the magnitude of reflectivity and the velocity from the spectrum analysis of return echoes through the Doppler filter bank. This conventional spectrum estimation method, FFT(Fast fourier Transform) is widely used in most radar systems. However, the frequency resolution of return echoes can be seriously degraded in fast moving targets because of the short acquisition time. Since the high Doppler frequency resolution is important in the detection and tracking of fast moving targets, it can cause very unsatisfactory results. Therefore, in this paper, the parameter spectrum estimation method called AR(Autoregressive) spectrum estimation, is investigated to overcome these problems.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.665-670
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• 2014

A SVM is a kind of binary classifier in order to find optimal hyperplane which separates training data into two groups. Due to its remarkable performance, the SVM is applied in various fields such as inductive inference, binary classification or making predictions. Also it is a representative black box model; there are plenty of actively discussed researches about analyzing trained SVM classifier. This paper conducts a study on a method that is automatically detecting the line-shaped echoes, sun strobe echo and radial interference echo, using the SVM algorithm because the line-shaped echoes appear relatively often and disturb weather forecasting process. Using a spatial clustering method and corrected reflectivity data in the weather radar, the training data is made up with mean reflectivity, size, appearance, centroid altitude and so forth. With actual occurrence cases of the line-shaped echoes, the trained SVM classifier is verified, and analyzed its characteristics using the decision tree method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.219-222
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• 2011

There are number of methods that track the movement of an object or the change of state, such as Kalman filter, particle filter, dynamic clustering, and so on. Amongst these method, dynamic clustering method is an useful way to track cluster across multiple data frames and analyze their trend. In this paper we suggest the similarity-based dynamic clustering method, and verifies it's performance by simulation. Proposed dynamic clustering method is how to determine the same clusters for each continuative frame. The same clusters have similar characteristics across adjacent frames. The change pattern of cluster's characteristics in each time frame is throughly studied. Clusters in each time frames are matched against each others to see their similarity. Mamdani fuzzy model is used to determine similarity based matching algorithm. The proposed algorithm is applied to radar reflectivity data over time domain. We were able to observe time dependent characteristic of the clusters.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1405-1423
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• 2021

Precipitation is one of the main factors that affect water and energy cycles, and its estimation plays a very important role in securing water resources and timely responding to water disasters. Satellite-based quantitative precipitation estimation (QPE) has the advantage of covering large areas at high spatiotemporal resolution. In this study, machine learning-based rainfall intensity models were developed using Himawari-8 Advanced Himawari Imager (AHI) water vapor channel (6.7 ㎛), infrared channel (10.8 ㎛), and weather radar Column Max (CMAX) composite data based on random forest (RF). The target variables were weather radar reflectivity (dBZ) and rainfall intensity (mm/hr) converted by the Z-R relationship. The results showed that the model which learned CMAX reflectivity produced the Critical Success Index (CSI) of 0.34 and the Mean-Absolute-Error (MAE) of 4.82 mm/hr. When compared to the GeoKompsat-2 and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) rainfall intensity products, the accuracies improved by 21.73% and 10.81% for CSI, and 31.33% and 23.49% for MAE, respectively. The spatial distribution of the estimated rainfall intensity was much more similar to the radar data than the existing products.