• Atmosphere
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• v.24 no.3
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• pp.433-444
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• 2014

Although the Radar-AWS Rainrate (RAR) calculation system operated by Korea Meteorological Administration estimated precipitation using 2-dimensional composite components of single polarization radars, this system has several limitations in estimating the precipitation accurately. To to overcome limitations of the RAR system, the Korea Meteorological Administration developed and operated the RMQ (Radar-based Multi-sensor Quantitative Precipitation Estimation) system, the improved version of NMQ (National Mosaic and Multi-sensor Quantitative Precipitation Estimation) system of NSSL (National Severe Storms Laboratory) for the Korean Peninsula. This study introduced the RMQ system domestically for the first time and verified the precipitation estimation performance of the RMQ system. The RMQ system consists of 4 main parts as the process of handling the single radar data, merging 3D reflectivity, QPE, and displaying result images. The first process (handling of the single radar data) has the pre-process of a radar data (transformation of data format and quality control), the production of a vertical profile of reflectivity and the correction of bright-band, and the conduction of hydrid scan reflectivity. The next process (merger of 3D reflectivity) produces the 3D composite reflectivity field after correcting the quality controlled single radar reflectivity. The QPE process classifies the precipitation types using multi-sensor information and estimates quantitative precipitation using several Z-R relationships which are proper for precipitation types. This process also corrects the precipitation using the AWS position with local gauge correction technique. The last process displays the final results transformed into images in the web-site. This study also estimated the accuracy of the RMQ system with five events in 2012 summer season and compared the results of the RAR (Radar-AWS Rainrate) and RMQ systems. The RMQ system ($2.36mm\;hr^{-1}$ in RMSE on average) is superior to the RAR system ($8.33mm\;hr^{-1}$ in RMSE) and improved by 73.25% in RMSE and 25.56% in correlation coefficient on average. The precipitation composite field images produced by the RMQ system are almost identical to the AWS (Automatic Weather Statioin) images. Therefore, the RMQ system has contributed to improve the accuracy of precipitation estimation using weather radars and operation of the RMQ system in the work field in future enables to cope with the extreme weather conditions actively.

• Journal of Multimedia Information System
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• v.5 no.4
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• pp.245-256
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• 2018

This study estimated rainfall information more effectively by image signals through the information system of weather radar. Based on this, we suggest the way to estimate quantitative precipitation utilizing overlapped observation area of radars. We used the overlapped observation range of ground hyetometer observation network and radar observation network which are dense in our country. We chose the southern coast where precipitation entered from seaside is quite frequent and used Sungsan radar installed in Jeju island and Gudoksan radar installed in the southern coast area. We used the rainy season data generated in 2010 as the precipitation data. As a result, we found a reflectivity bias between two radar located in different area and developed the new quantitative precipitation estimation method using the bias. Estimated radar rainfall from this method showed the apt radar rainfall estimate than the other results from conventional method at overall rainfall field.

• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.175-189
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• 2019

The problem of the estimation of quantitative sensitive variable using the item sum technique (IST) on successive occasions has been discussed. IST difference, IST regression, and IST general class of estimators have been proposed to estimate quantitative sensitive variable at the current occasion in two occasion successive sampling. The proposed new estimators have been elaborated under Trappmann et al. (Journal of Survey Statistics and Methodology, 2, 58-77, 2014) as well as Perri et al. (Biometrical Journal, 60, 155-173, 2018) allocation designs to allocate long list and short list samples of IST. The properties of all proposed estimators have been derived including optimum replacement policy. The proposed estimators have been mutually compared under the above mentioned allocation designs. The comparison has also been conducted with a direct method. Numerical applications through empirical as well as simplistic simulation has been used to show how the illustrated IST on successive occasions may venture in practical situations.

• Journal of Wetlands Research
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• v.19 no.1
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• pp.112-121
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• 2017

This study proposed the quantitative precipitation estimation method using overlapped area in radar network. For this purpose, the dense rain gauges and radar network are used. As a result, we found a reflectivity bias between two radar located in different area and developed the new quantitative precipitation estimation method using the bias. Estimated radar rainfall from this method showed the apt radar rainfall estimate than the other results from conventional method at overall rainfall field.

• Atmosphere
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• v.24 no.3
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• pp.365-378
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• 2014

The limits of S-band dual-polarization radars in Korea are not reflected on the recent weather forecasts of Korea Meteorological Administration and furthermore, they are only utilized for rainfall estimations and hydrometeor classification researches. Therefore, this study applied four merging methods [SA (Simple Average), WA (Weighted Average), SSE (Sum of Squared Error), TV (Time-varying mergence)] to the QPE (Quantitative Precipitation Estimation) model [called RAR (Radar-AWS Rainfall) calculation system] using single-polarization radars and S-band dual-polarization radar in order to improve the accuracy of the rainfall estimation of the RAR calculation system. As a result, the merging results of the WA and SSE methods, which are assigned different weights due to the accuracy of the individual model, performed better than the popular merging method, the SA (Simple Average) method. In particular, the results of TVWA (Time-Varying WA) and TVSSE (Time-Varying SSE), which were weighted differently due to the time-varying model error and standard deviation, were superior to the WA and SSE. Among of all the merging methods, the accuracy of the TVWA merging results showed the best performance. Therefore, merging the rainfalls from the RAR calculation system and S-band dual-polarization radar using the merging method proposed by this study enables to improve the accuracy of the quantitative rainfall estimation of the RAR calculation system. Moreover, this study is worthy of the fundamental research on the active utilization of dual-polarization radar for weather forecasts.

• The Transactions of the Korea Information Processing Society
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• v.2 no.1
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• pp.34-44
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• 1995

One of the most important problems faced by software developers and users is the prediction of the size of programming system and its development effort. This article define the identical characteristics for structured specification which is consisted of Data Flow Diagram, Data Dictionary and Mini Specification and apply quantitative estimation factor of structured specification to program code metrics, Moreover, concerning DFD which is made up of component element of structured specification executed quantitative estimation experiment. In the result, we propose man/month prediction model of lower progression with production on analysis phase of upper progression.

• Journal of the Korean Society of Safety
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• v.25 no.1
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• pp.44-49
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• 2010

This paper is estimation of structure damage caused by Explosion in LPG(Liquefied Petroleum Gas) filling station. As we estimate the influence of damage which occur at gas storage tank in filling station. We can utilize the elementary data of safety distance. In this study, the influence of over-pressure caused by VCE(Vapor Cloud Explosion) in filling station was calculated by using the Hopkinson's scaling law and the accident damage was estimated by applying the influence on the adjacent structure into the probit model. As a result of the damage estimation conducted by using the probit model, both the damage possibility of explosion overpressure to structures of max 265 meters away and to glass bursting of 1150 meters away was nearly zero in open space explosion.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.133-133
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• 2021

The performance of radar Quantitative Precipitation Estimation (QPE) using Long Short-Term Memory (LSTM) networks in hydrological applications depends on either the quality of data or the three-dimensional CAPPI structure from the weather radar. While radar data quality is controlled and enhanced by the more and more modern radar systems, the effect of CAPPI structure still has not yet fully investigated. In this study, three typical and important types of CAPPI structure including inverse-pyramid, cubic of grids 3x3, cubic of grids 4x4 are investigated to evaluate the effect of CAPPI structures on the performance of radar QPE using LSTM networks. The investigation results figure out that the cubic of grids 4x4 of CAPPI structure shows the best performance in rainfall estimation using the LSTM networks approach. This study give us the precious experiences in radar QPE works applying LSTM networks approach in particular and deep-learning approach in general.

• 한국정보디스플레이학회:학술대회논문집
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• 2008.10a
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• pp.737-740
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• 2008

Block-matching motion estimation plays an important role in video coding. In this paper, we propose an Efficient Search Algorithm for Fast Motion Estimation. The proposed algorithm detects motion variation for reducing computational complexity before determining motion vector. Experimental results show that the proposed algorithm has good performance than conventional algorithms through quantitative evaluation.

• Journal of the Korean Society of Tobacco Science
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• v.21 no.1
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• pp.36-48
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• 1999

Field test was conducted in Chungbuk province to evaluate the soil fertility using landscape and soil attributes by application of geographic information system(GIS) in 48 tobacco fields during 2 years(1996 ; 23 fields, 1997 ; 25 fields). The soil fertility factors and fertilizer effects were estimated by twenty five independent variables including 13 chemical properties and 12 GIS databases. Twenty five independent variables were classified by two groups, 15 quantitative indexes and 10 qualitative indexes and were analyzed by multiple linear regression (MLR) of SAS, REG and GLM models. The estimation model for evaluation of soil fertility and fertilizer effect was made by giving the estimate coefficient for each quantitative index and for each group of qualitative index significantly selected by MLR. Estimation for soil fertility factors and fertilizer effects by independent variables was better by MLR than single regression showing gradually improvement by adding chemical properties, quantitative indexes and qualitative indexes of GIS. Consequently, it is assumed that this approach by MLR with quantitative and qualitative indexes was available as an evaluation model of soil fertility and recommendation of optimum fertilization for tobacco field.