• Atmosphere
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• v.27 no.4
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• pp.467-481
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• 2017

A correction method of reflectivity in partial beam blockage (PBB) area is suggested, which is based on the combination of digital terrain information and self-consistency principle between polarimetric observation. First, the reflectivity was corrected by adding the radar energy loss estimated from beam blockage simulation using digital elevation model (DEM) and beam propagation geometry in standard atmosphere. The additional energy loss by unexpected obstacles and non-standard beam propagation was estimated by using the coefficient between accumulated reflectivity ($Z_H$) and differences of differential phase shift (${\Phi}_{DP}$) along radial direction. The proposed method was applied to operational S-band dual-polarization radar at Jindo and its performance was compared with those of simulation method and self-consistency method for six rainfall cases. When the accumulated reflectivity and increment of ${\Phi}_{DP}$ along radial direction are too small, the self-consistency method has failed to correct the reflectivity while the combined method has corrected the reflectivity bias reasonably. The correction based on beam simulation showed the underestimation. For evaluation of rainfall estimation, the FBs (FRMSEs) of simulation method and self-consistency principle were -0.32 (0.59) and -0.30 (0.57), respectively. The proposed method showed the lowest FB (-0.24) and FRMSE (0.50). The FB and FMSE were improved by about 18% and by 19% in comparison to those before correction (-0.42 and 0.70). We can conclude that the proposed method can improve the accuracy of rainfall estimation in PBB area.

• Journal of Korea Water Resources Association
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• v.42 no.8
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• pp.607-617
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• 2009

Radar beam blocking which is partially or entirely interrupted by obstacles like a mountain causes underestimation of the rainfall. In this paper, partially blocked radar reflectivity is retrieved using the ARM(Accumulated Reflectivity Map). ARM is made by accumulation of the radar reflectivity and very useful product to analyze the beam blockage. The blockage correcting map could be obtained by assuming the spatially uniform reflectivity field in the ARM. This method is applied to the cases of typhoon and Changma, and we obtain the MFE(Mean Fractional Error) from two radar data, the one is objective radar data which is affected by blockage and the other is comparative radar data which is not affected by blockage. Before blocking correction, MFE is 20-35%. However, after correction, MFE decreases to 7-10%.

• 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 the Semiconductor & Display Technology
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• v.14 no.3
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• pp.1-5
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• 2015

In photolithography process, resolution enhancement techniques such as optical proximity correction (OPC) and phase shift mask (PSM) have been applied to improve resolution. Especially, sub-resolution assist feature (SRAF) is one of the most important OPC to enhance image quality including depth of focus (DOF). However, imaging performance of the mask could be varied with the diffraction order amplitude changed by inserting SRAF. Therefore, in this study, we investigated the imaging properties and process margin of attenuated PSM with SRAF. Reflectivities of attenuated PSMs at 13.5 nm were 3, 6, 9% and simulation was performed by $PROLITH^{TM}$. As a result, aerial image properties and DOF as well as diffraction efficiency were improved by increasing the reflectivity of attenuated PSM. Additionally, printed critical dimension variations depending on SRAF width and space error were also reduced for attenuated PSM with high reflectivity. However, SRAF could be printed when reflectivity of attenuated PSM is high enough. In conclusion, optimization of reflectivity of attenuated PSM and SRAF to prevent side-lobe from being printed is needed to be considered.

• Journal of Positioning, Navigation, and Timing
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• v.6 no.4
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• pp.159-166
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• 2017

The position of autonomous driving vehicle is basically acquired through the global positioning system (GPS). However, GPS signals cannot be received in tunnels. Due to this limitation, localization of autonomous driving vehicles can be made through sensors mounted on them. In particular, a 3D Light Detection and Ranging (LIDAR) system is used for longitudinal position error correction. Few feature points and structures that can be used for localization of vehicles are available in tunnels. Since lanes in the road are normally marked by solid line, it cannot be used to recognize a longitudinal position. In addition, only a small number of structures that are separated from the tunnel walls such as sign boards or jet fans are available. Thus, it is necessary to extract usable information from tunnels to recognize a longitudinal position. In this paper, fire hydrants and evacuation guide lights attached at both sides of tunnel walls were used to recognize a longitudinal position. These structures have highly distinctive reflectivity from the surrounding walls, which can be distinguished using LIDAR reflectivity data. Furthermore, reflectivity information of tunnel walls was fused with the road surface reflectivity map to generate a synthetic reflectivity map. When the synthetic reflectivity map was used, localization of vehicles was able through correlation matching with the local maps generated from the current LIDAR data. The experiments were conducted at an expressway including Maseong Tunnel (approximately 1.5 km long). The experiment results showed that the root mean square (RMS) position errors in lateral and longitudinal directions were 0.19 m and 0.35 m, respectively, exhibiting precise localization accuracy.

• Atmosphere
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• v.22 no.3
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• pp.309-319
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• 2012

It is important to characterize and quantify the inherent error in the radar rainfall to make full use of the radar rainfall. This study verified the error structure of the reflectivity and corrected the range dependent error in the CAPPI using a VPR (vertical profile of reflectivity) model. The error of the CAPPI to display the reflectivity data becomes bigger for the range longer than 100 km. This range dependent error, however, is significantly improved by corrected the CAPPI data using the VPR model.

• Journal of the Korean Vacuum Society
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• v.12 no.S1
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• pp.40-45
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• 2003

Ru interfacial layer was inserted into Mo-on-Si interface to enhance the extreme ultra-violet (EUV) reflective multilayer properties. The stacking status and optical properties are analyzed using cross-sectional transmission electron microscope (TEM), and reflectometer. About 1.5% of maximum reflectivity can be acquired as predicted in optical simulation, which is thought to be originated from the diffusion inhibition property. Phase defect correctability of the multilayer can be enhanced by the insertion of Ru barrier layer.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.3
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• pp.329-336
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• 2010

This study deals with the measurement of reflectivity as well as the distance accuracy with Terrestrial Laser Scanner(TLS) using time of flight methods and near infrared wave length, for a variety of user-made targets. Especially, point clouds' reflection to several targets was measured with Gretag Macbeth il spectrophotometer in the office. And the distance accuracy in comparison to reference distance for TLS performance evaluation, was tested after scanning the user-made targets and measuring the inter-pillars distances over the precise EDM calibration baseline. The results of test was shown that except white resin objects, with approx. 10m and 170m inter-pillar distances, other targets achieved the distance accuracy of several millimeters(mm) with respect to standard distances. Future work should be concentrate on a few parameters influencing on the distance accuracy such as atmospheric correction, instrument correction, the additive constant or zero/index correction, etc.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.1011-1030
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• 2019

Remote sensing sensors used in satellites or aircrafts measure electromagnetic waves passing through the earth's atmosphere, and thus the information on the surface of the earth is affected as it is absorbed or scattered by the earth's atmosphere. Although satellites have different wavelength ranges and resolutions depending on the purpose of onboard sensors, in general, atmospheric correction must be made to remove the influence of the atmosphere in order to accurately measure the spectral signal of an object on the earth's surface. The purpose of atmospheric correction is to remove the atmospheric effect from remote sensing images to determine surface reflectivity values and to derive physical parameters of the surface. Until recently, atmospheric correction algorithms have evolved from image-based empirical methods or indirect methods using in-situ observation data to direct methods that numerically interpret more complex radiative transfer processes. This study analyzes the research records of atmospheric correction algorithms developed over the past 40 years, systematically establishes the current state of atmospheric correction technology and the results of major atmospheric correction algorithms and presents the current status and research trends of related technologies.

• 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.