• Journal of Integrative Natural Science
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• v.6 no.1
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• pp.12-15
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• 2013

To combine the micro rain radar and microwave radiometer cloud liquid water, we estimate the cloud physical thickness from the difference between the MTSAT-1R cloud top height and cloud base height of visual observation of Daegwallyeong weather station, and the cloud liquid water path of micro rain radar is obtained by multiplying the liquid water content of micro rain radar and the estimated cloud physical thickness. The trend of microwave radiometer liquid water path agrees with that of the micro rain radar during small precipitation. We study these characteristics of micro rain radar and microwave radiometer for small precipitation to obtain the combined cloud water content of micro rain radar and microwave radiometer, constantly operated regardless to the rainfall.

• Atmosphere
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• v.24 no.1
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• pp.39-48
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• 2014

This study provides a comparative analysis of cloud top heights observed by a Ka-band cloud radar and the Communication, Ocean and Meteorological Satellite (COMS) at Boseong National Center for Intensive Observation of severe weather (NCIO) from May 25, 2013 (1600 UTC) to May 27. The rainfall duration is defined as the period of rainfall from start to finish, and the no rainfall duration is defined as the period other than the rainfall duration. As a result of the comparative analysis, the cloud top heights observed by the cloud radar have been estimated to be lower than that observed by the COMS for the rainfall duration due to the signal attenuation caused by raindrops. The stronger rainfall intensity gets, the more the difference grows. On the other hand, the cloud top heights observed by the cloud radar have been relatively similar to that observed by the COMS for the no rainfall duration. In this case, the cloud radar can effectively detect cloud top heights within the range of its observation. The COMS indicates the cloud top heights lower than the actual ones due to the upper thin clouds under the influence of ground surface temperature. As a result, the cloud radar can be useful in detecting cloud top heights when there are no precipitation events. The COMS data can be used to correct the cloud top heights when the radar gets beyond the valid range of observation or there are precipitation events.

• Journal of Environmental Science International
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• v.19 no.10
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• pp.1187-1201
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• 2010

The formation and development conditions of the cloud streets over the yellow sea by the Cold Surge of Siberian Anticyclone Expansion which produce the heavy snowfall events over the southwestern coast, Honam District of the Korean peninsula, has been investigated through analyses of the three dimensional snow cloud structures by using the CAPPI, RHI, VAD and VVP data of X-band Radar at Muan Weather Observatory and S-band Radar at Jindo Weather Station. The data to be used are obtained from January 04, 2003, when heavy snow storm hits on Gwangju and Honam District. The PPI Radar images show that the cloud bands distribute in perpendicular to the expansion direction of the high pressure and that the radius of cloud cells is about 5~8 km with 20~30 dBz and distance between each cell is about 10 km. And but the vertical Radar images show that the cloud street is a small scale convective type cloud within height of about 3 km where a stable layer exists. From the VVP images, the time period of the high pressure expansion, the moving direction and development stages of the system are delineated. Finally, the vertical distribution of wind direction is fairly constants, while the wind speed sheer increases with altitude to 3 km.

• Atmosphere
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• v.25 no.1
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• pp.19-30
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• 2015

The Ka-band cloud radar (KCR) has been operated by the National Institute of Meteorological Research (NIMR) of Korea Meteorological Administration (KMA) at Boseong National Center for Intensive Observation of severe weather since 2013. Evaluation of data quality is an essential process to further analyze cloud information. In this study, we estimate the measurement error and the sampling uncertainty to evaluate data quality. By using vertically pointing data, the statistical uncertainty is obtained by calculating the standard deviation of each radar parameter. The statistical uncertainties decrease as functions of sampling number. The statistical uncertainties of horizontal and vertical reflectivities are identical (0.28 dB). On the other hand, the statistical uncertainties of Doppler velocity (spectrum width) are 2.2 times (1.6 times) larger at the vertical channel. The reflectivity calibration of KCR is also performed using X-band vertically pointing radar (VertiX) and 2-dimensional video disdrometer (2DVD). Since the monitoring of calibration values is useful to evaluate radar condition, the variation of calibration is monitored for five rain events. The average of calibration bias is 10.77 dBZ and standard deviation is 3.69 dB. Finally, the statistical characteristics of cloud properties have been investigated during two months in autumn using calibrated reflectivity. The percentage of clouds is about 26% and 16% on September to October. However, further analyses are required to derive general characteristics of autumn cloud in Korea.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.568-570
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• 2012

The algorithm to distinguish cloud from aerosols in the measurements of Laser Radar is developed. This method use the difference of slope between return signals of cloud and aerosols. The parameters achieved from the algorithm are altitude of cloud top, cloud base, and boundary layer.

• Korean Journal of Remote Sensing
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• v.29 no.3
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• pp.293-306
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• 2013

Hydrometeor type and Drop Size Distribution (DSD) in cloud are the fundamental properties that may help explain the rain formation processes and determine the parameters of radar meteorology. This study presents a preliminary analysis of hydrometeor types and DSD data of cloud measured with a PARSIVEL (PARticle SIze and VELocity) optical disdrometer at the site of Cloud Physics Observation System (CPOS, $37^{\circ}41^{\prime}N$, $128^{\circ}45^{\prime}E$, 843 m from sea level) in Daegwallyeong mountainside of Korea. The method has been validated by comparing the observed rainfall rates with the computed ones from the fitted distribution, using the physical data such as DSD, terminal velocity, and rain intensity which were measured by a Micro-Rain Radar (MRR) and a PARSIVEL optical disdrometer. The analysis period started in three cases: on rainy days with light rain (15.5 mm), moderate rain (76 mm), and heavy rain (121 mm), from March to November 2007, respectively.

• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.277-288
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• 2011

Cloudsat satellite data is sensitive to snowfall and collected during each month beginning with Dec 2007 and ending Feb 2008. In this study, we attempt to develop a snowfall retrieval algorithm using a combination of radiometer and cloud radar data. We trained data from the relation between brightness temperature measurements from NOAA's Advanced Microwave Sounder Unit-B(AMSU-B) and the radar reflectivity of the 2B-GEOPROF product from W-band(94 GHz) cloud radar onboard Cloudsat and applied it to the Korea peninsula. We use a principal components analysis to quantify the variations that are the result of the radiometric signatures of snowfall from those of the surface. Finally, we quantify the correlation between the higher principal component (orthogonal to surface variability) of the microwave radiances and the precipitation-sensitive CloudSat radar reflectivities. This work summarizes the results of applying this approach to observations over the East Sea during Feb. 2008. The retrieved data show reasonable estimation for snowfall rate compared with Cloudsat vertical image.

• Atmosphere
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• v.29 no.1
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• pp.105-112
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• 2019

Measurements of vertical air motion and microphysics are essential for improving our understanding of convective clouds. In this paper, the author reviews the current research on the retrieval of vertical air motions using the cloud radar. At radar wavelengths of 3 mm (W-band radar; 94-GHz radar; cloud radar), the raindrop backscattering cross-section (${\sigma}b$) varies between successive maxima and minima as a function of the raindrop diameter (D) that are well described by Mie theory. The first Mie minimum in the backscattering cross-section occurs at D~1.68 mm, which translates to a raindrop terminal fall velocity of ${\sim}5.85m\;s^{-1}$ based on the Gunn and Kinzer relationship. Since raindrop diameters often exceed this size, the signal is captured in the radar Doppler spectrum, and thus, the location of the first Mie minimum can be used as a reference for retrieving the vertical air motion. The Mie technique is applied to radar Doppler spectra from the surface-based and airborne, upward pointing W-band radars. The contributions of aircraft motion to the vertical air motion are also described and further the first-order aircraft motion corrected equation is presented. The review also shows that the separate spectral peaks due to the cloud droplets can provide independent validation of the Mie technique retrieved vertical air motion using the cloud droplets as a tracer of vertical air motion.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.28-28
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• 2022

본 연구는 용담댐유역을 포함한 금강 유역 상류 지역을 대상으로 Sentinel-1 SAR (Synthetic Aperture Radar) 위성영상을 기반으로 한 토양수분 산정을 목적으로 하였다. Sentinel-1 영상은 2019년에 대해 12일 간격으로 수집하였고, 영상의 전처리는 SNAP (SentiNel Application Platform)을 활용하여 기하 보정, 방사 보정 및 Speckle 보정을 수행하여 VH (Vertical transmit-Horizontal receive) 및 VV (Vertical transmit-Vertical receive) 편파 후방산란계수로 변환하였다. 토양수분 산정에는 Water Cloud Model (WCM)이 활용되었으며, 모형의 식생 서술자(Vegetation descriptor)는 RVI (Radar Vegetation Index)와 NDVI (Normalized Difference Vegetation Index)를 활용하였다. RVI는 Sentinel-1 영상의 VH 및 VV 편파자료를 이용해 산정하였으며, NDVI는 동기간에 대해 10일 간격으로 수집된 Sentinel-2 MSI (MultiSpectral Instrument) 위성영상을 활용하여 산정하였다. WCM의 검정 및 보정은 한국수자원공사에서 제공하는 10 cm 깊이의 TDR (Time Domain Reflectometry) 센서에서 실측된 6개 지점의 토양수분 자료를 수집하여 수행하였으며, 매개변수의 최적화는 비선형 최소제곱(Non-linear least square) 및 PSO (Particle Swarm Optimization) 알고리즘을 활용하였다. WCM을 통해 산정된 토양수분은 피어슨 상관계수(Pearson's correlation coefficient)와 평균제곱근오차(Root mean square error)를 활용하여 검증을 수행할 예정이다.

• Atmosphere
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• v.33 no.5
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• pp.457-475
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• 2023

Accurately predicting localized heavy rainfall is challenging without high-resolution mesoscale cloud information in the numerical model's initial field, as precipitation intensity and amount vary significantly across regions. In the Korean Peninsula, the radar observation network covers the entire country, providing high-resolution data on hydrometeors which is suitable for data assimilation (DA). During the pre-processing stage, radar reflectivity is classified into hydrometeors (e.g., rain, snow, graupel) using the background temperature field. The mixing ratio of each hydrometeor is converted and inputted into a numerical model. Moreover, assimilating saturated water vapor mixing ratio and decomposing radar radial velocity into a three-dimensional wind vector improves the atmospheric dynamic field. This study presents radar DA experiments using a numerical prediction model to enhance the wind, water vapor, and hydrometeor mixing ratio information. The impact of radar DA on precipitation prediction is analyzed separately for each radar component. Assimilating radial velocity improves the dynamic field, while assimilating hydrometeor mixing ratio reduces the spin-up period in cloud microphysical processes, simulating initial precipitation growth. Assimilating water vapor mixing ratio further captures a moist atmospheric environment, maintaining continuous growth of hydrometeors, resulting in concentrated heavy rainfall. Overall, the radar DA experiment showed a 32.78% improvement in precipitation forecast accuracy compared to experiments without DA across four cases. Further research in related fields is necessary to improve predictions of mesoscale heavy rainfall in South Korea, mitigating its impact on human life and property.