• Journal of the Korean earth science society
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• v.34 no.1
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• pp.13-27
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• 2013

Quantitative precipitation estimation (QPE) is one of the most important elements in meteorological and hydrological applications. In this study, we adjusted the QPE from an S-band weather radar based on co-kriging method using the geostatistical structure function of error distribution of radar rainrate. In order to estimate the accurate quantitative precipitation, the error of radar rainrate which is a primary variable of co-kriging was determined by the difference of rain rates from rain gauge and radar. Also, the gauge rainfield, a secondary variable of co-kriging is derived from the ordinary kriging based on raingauge network. The error distribution of radar rain rate was produced by co-kriging with the derived theoretical variogram determined by experimental variogram. The error of radar rain rate was then applied to the radar estimated precipitation field. Locally heavy rainfall case during 6-7 July 2009 is chosen to verify this study. Correlation between adjusted one-hour radar rainfall accumulation and rain gauge rainfall accumulation improved from 0.55 to 0.84 when compared to prior adjustment of radar error with the adjustment of root mean square error from 7.45 to 3.93 mm.

• 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%.

• Atmosphere
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• v.26 no.4
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• pp.509-522
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• 2016

Heavy snowfall events have occurred frequently in the Yeongdong region but understanding of these events have trouble in lack of snowfall observation in this region because it is composed of complex topography like the "Taebaek mountains" and the "East sea". These problems can be solved by quantitative precipitation estimation technique using remote sensing such as radar, satellite, etc. Two radars which are able to cover over Yeondong region were installed at Gangneung (GNG) and Gwangdeoksan (GDK). This study uses radar and water equivalent of snow cover to investigate the characteristics of radar echoes and the $Z_e-R$ relations associated with the 10 Yeongdong heavy snowfall events during the last 5 years (2010~2014). It was found that the heights which the probability of detection (POD) of snow detection by GNG radar is more than 80% are 3,000 m and 1,500 m in convective cloud and stratiform cloud, respectively. The vertical gradient of radar reflectivity is less decreased in convective cloud than stratiform cloud. However, POD by GDK radar are lower than 80% at all layers because the majority of Yeondong observational stations are more than 100 km away from GDK radar site. Furthermore, we examined $Z_e-R$ relation from the 10 events using GNG radar and compared the "a" and "b" obtained from these examinations at Sokcho (SC) and Daegwallyeong (DG). These "a" and "b" are estimated from radar echo at 500 m (SC) and 1,500 m (DG). The values of "a" differ in their stations such as SC and DG are 30~116 and 6~39, respectively. But "b" is 0.4~1.7 irrespective of stations. Moreover, the value of "a" increased with surface air temperature. Therefore, quantitative precipitation estimation in heavy snowfall events by radar echo using fixed "a" and "b" is difficult because these values changed according to those precipitation characteristics.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.4
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• pp.695-704
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• 2018

The rainfall cases were identified by rainfall estimation techniques which were developed by using X - band marine radar. A digital signal converter was used to convert the signal received from the marine radar into digital reflectivity information. The ground clutter signal was removed and the errors caused by beam attenuation and beam volume changes were corrected. The reflectivity showed a linear relationship with the rain gauge rainfall. Quantitative rainfall was estimated by converting the radar signal into an cartesian coordinate system. When the rainfall was recorded more than $5mm\;hr^{-1}$ at three automatic weather stations, the rain cell distribution on the marine radar was consistent with that of the weather radar operated by Korea meteorological Adminstration.

• Atmosphere
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• v.29 no.3
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• pp.269-282
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• 2019

While it is important to obtain the accurate information on snowfall data due to the increase in damage caused by the heavy snowfall in the winter season, it is not easy to observe the snowfall quantitatively. Recently, snow measurements using a weighing precipitation gauge have been carried out, but there is a problem that high snowfall intensity results in low accuracy. Also, the observed snowfall data are sensitive depending on wind speed, temperature, and humidity. In this study, a new process of quality control for snow water equivalent (SWE) data of the weighing precipitation gauge were proposed to cover the low accuracy of snow data and maximize the data utilization. Snowfall data (SWE) observed by Pluvio, Parsivel, snow-depth meter using laser or ultrasonic, and rainfall gauge in Cloud Physics Observation Site (CPOS) were compared and analyzed. Applying the QC algorithm including the use of number of hydrometeor particles as reference, the increased SWE per the unit time was determined and the data noise was removed and marked by flag. The SWE data converted by the number concentration of hydrometeor particles are tested as a method to restore the QC-removed data, and show good agreement with those of the weighing precipitation gauge, though requiring more case studies. The three events data for heavy snowfall disaster in Pyeongchang area was analyzed. The SWE data with improved quality was showed a good correlation with the eye-measured data ($R^2$ > 0.73).

• 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.

• KIEAE Journal
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• v.15 no.5
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• pp.5-12
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• 2015

Purpose: Retention and infiltration of small and frequently-occurring rainfall by LID facilities account for a large proportion of the annual precipitation volume. Based on 4 standard facilities such as Porous Pavement, Infiltration Trench, Cylindrical Infiltration Well, Rectangular Infiltration Well by Seoul Metropolitan Handbook of the Prior Consultation for LID. The total retention volume of each facility was calculated according to the type and size. The Purpose of this study is to find out the quantitative relationship between Percentile Rainfall Event and Design Volume of Infiltration Facilities. Methode: For the estimation of Percentile Rainfall Event, Daily Precipitation of Seoul from 2005 to 2014 was sorted ascending and the distribution of percentile was estimated by PERCENTILE spreadsheet function. The managed Rainfall Depth and Percentile of each facility was calculated at the several sizes. In response to the rainwater charge volume of 5.5mm/hr by the Category "Private large site", the 3 types of facilities were planned for example. The calculated Rainfall Depth and Percentile were 54.4mm and 90% by the use of developed Calculation-Module based on the Spreadsheet program. Result: With this Module the existing Designed Infiltration volume which was introduced from Japan was simply converted to the Percentile-Rainfall-Event used in USA.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.155-155
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• 2018

Uncertainty in flood forecasting using a coupled meteorological and hydrological model is arisen from various sources, especially the uncertainty comes from the inaccuracy of Quantitative Precipitation Forecasts (QPFs). In order to improve the capability of flood forecast, the uncertainty estimation and mitigation are required to perform. This study is conducted to investigate and reduce such uncertainty. First, ensemble QPFs are generated by using Monte - Carlo simulation, then each ensemble member is forced as input for a hydrological model to obtain ensemble streamflow prediction. Likelihood measures are evaluated to identify feasible member. These members are retained to define upper and lower limits of the uncertainty interval and assess the uncertainty. To mitigate the uncertainty for very short lead time, a blending method, which merges the ensemble QPFs with radar-based rainfall prediction considering both qualitative and quantitative skills, is proposed. Finally, blending bias ratios, which are estimated from previous time step, are used to update the members over total lead time. The proposed method is verified for the two flood events in 2013 and 2016 in the Yeonguol and Soyang watersheds that are located in the Han River basin, South Korea. The uncertainty in flood forecasting using a coupled Local Data Assimilation and Prediction System (LDAPS) and Sejong University Rainfall - Runoff (SURR) model is investigated and then mitigated by blending the generated ensemble LDAPS members with radar-based rainfall prediction that uses McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE). The results show that the uncertainty of flood forecasting using the coupled model increases when the lead time is longer. The mitigation method indicates its effectiveness for mitigating the uncertainty with the increases of the percentage of feasible member (POFM) and the ratio of the number of observations that fall into the uncertainty interval (p-factor).

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.2-2
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• 2015

최근 국지성 집중호우 및 돌발홍수와 같은 급격한 기상변화로 인한 기상재해의 발생빈도가 증가함에 따라 기존 지상 기상관측소로부터 얻어지는 직접탐측 자료보다는 기상레이더와 위성영상 등 원격탐측 자료를 사용한 수문분야의 연구가 활발하게 진행되고 있다. 기상레이더는 넓은 지역에 걸쳐 실시간으로 강수현상 감시가 가능하며 지상우량계로는 파악이 불가능한 미계측 유역을 통과하는 국지적인 호우현상이나 강우장의 이동 및 변화의 파악도 빠른 시간에 가능한 장점이 있다. 본 연구는 기상레이더 공간적 분포와 지상관측소(AWS 및 ASOS) 자료를 연계한 통계적 레이더 강수량 추정(Quantitative Precipitation Estimation, QPE)과 레이더 강수장을 직접 추적하는 강수장 예측(Quantitative Precipitation Forecast, QPF)를 연계한 해석방안을 수립하였으며, 모형 적용과정은 다음과 같다. 첫째, 강우장의 공간적인 이동을 고려하기 위해 강우장으로 부터 이류(advection)패턴을 추출하여 각 강우세포가 가지는 이동방향 및 이동속도를 고려한 강우장 추적기법을 통하여 2시간의 선행시간을 가지는 강우장을 예측하고자 한다. 둘째, 과거 기상레이더 이미지와 지상관측소의 강수 특성을 파악한 후 앞서 예측된 레이더강우장의 형태와 가장 유사한 과거 레이더강우장과 동일 시간대에 지상관측소 강수시계열을 시나리오 형태로 구축한다. 본 연구를 통하여 개발된 기상레이더 영상 이미지 상관분석 기법을 활용한 초단기강우예측은 집중호우시 홍수 예 경보를 위한 수문모형의 입력자료로 활용이 가능하다. 즉, 수문모형과 연계한 고해상도 단기홍수 예측기술 적용이 가능할 것으로 판단되며, 향후 실시간 재해 예 경보에 활용성을 평가하고자 한다.

• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.155-169
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• 2016

In this study, we developed a multi-sensor blending short-term rainfall forecasting technique using radar and satellite data during extreme rainfall occurrences in Busan and Gyeongnam region in August 2014. The Tropical Z-R relationship ($Z=32R^{1.65}$) has applied as a optimal radar Z-R relation, which is confirmed that the accuracy is improved during 20mm/h heavy rainfall. In addition, the multi-sensor blending technique has applied using radar and COMS (Communication, Ocean and Meteorological Satellite) data for quantitative precipitation estimation. The very-short-term rainfall forecasting performance was improved in 60 mm/h or more of the strong heavy rainfall events by multi-sensor blending. AWS (Automatic Weather System) and MAPLE data were used for verification of rainfall prediction accuracy. The results have ensured about 50% or more in accuracy of heavy rainfall prediction for 1-hour before rainfall prediction, which are correlations of 10-minute lead time have 0.80 to 0.53, and root mean square errors have 3.99 mm/h to 6.43 mm/h. Through this study, utilizing of multi-sensor blending techniques using radar and satellite data are possible to provide that would be more reliable very-short-term rainfall forecasting data. Further we need ongoing case studies and prediction and estimation of quantitative precipitation by multi-sensor blending is required as well as improving the satellite rainfall estimation algorithm.