• Korean Journal of Remote Sensing
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• v.8 no.2
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• pp.79-91
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• 1992

With recent development in spaceborne imaging radar system, there are growing interests using satellite synthetic aperture radar(SAR) data in various applications. This study attempted to identify the relationships between several forest stand characteristics and radar backscatter, measured from space altitude altitude at three incidence angles. Shuttle Imaging Radar-B(SIR-B) data were collected over a forested area in northern Florida in October, 1984. By using various sources of reference data (forest type maps, inventory records, aerial photographs, and Landsat Thematic Mapper data), about 400 forest stands of known characteristics were carefully located in the radar data. Relative radar backscatter for the three incidence angles of SIR-B data were compared with known forest stand parameters such as mean tree height, diameter at breast height(DBH), stand density, biomass, and relative amount of understory vegetation. The results show that these stand parameters have statistically significant correlations with the radar backscatter. In addition, the SIR-B radar backscatter from a certain stand parameter turned out differently at the three different incidence angles. Finally, the types and characteristics of currently available satellite SAR data are discussed.

• Journal of Korea Multimedia Society
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• v.18 no.4
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• pp.473-482
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• 2015

Doppler weather radar is an important tool for meteorological research. Through several decades of development, Doppler weather radar has enormous progress in understanding, detection and warning of meso and micro scale weather system. It makes a significant contribution to weather forecast and weather disaster warning. But the large amount of data process limits the application of Doppler weather radar. This paper proposed for fast weather radar data processing based on CUDA. CDUA is a powerful platform for highly parallel programming developed by NVIDIA. Through running plenty of threads, radar data can be calculated at same time. In experiment, CUDA parallel program can significantly improve weather data processing time.

• Journal of Korea Multimedia Society
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• v.20 no.12
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• pp.1874-1889
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• 2017

The paper aims to analyze structure of I/Q data observed from radar and reliably estimate rainfall through quality control of I/Q data that can quantify uncertainty of I/Q data occurring due to resultant errors. Radar rainfall data have strong uncertainty due to various factors influencing quality. In order to reduce this uncertainty, previously enumerated errors in quality need to be eliminated. However, errors cannot be completely eliminated in some cases as seen in random errors, so uncertainty is necessarily involved in radar rainfall data. Multi-Lag Method, one of I/Q data quality control methods, was applied to estimate precipitation with regard to I/Q data of rainfall radar in Mt. Sobaek.

• Journal of Korea Society of Industrial Information Systems
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• v.5 no.3
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• pp.44-50
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• 2000

This paper presents the effective data processing system of a transportable meteorological radar(DWSR-200x). Transportable meteorological radar is useful as it can be moved to target area for special purpose. First of all, to use this radar effectively, it is desirable that the data transmitting should be taken place between the radar system and the data center located in a distance. From this raw data we can analyze the property of atmosphere, as well as sore and display the demanded shape of users. In this paper, we make use of wireless LAN that communicates the data between the radar system and the information center. And the display program of transportable radar is developed with transmitted data. It provides meteorologists with the echo searching function in real time and dictionary faculty using the graphic and multimedia data.

• Journal of Korea Water Resources Association
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• v.52 no.1
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• pp.21-33
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• 2019

This study performed to simulate the watershed storm runoff using data of S-band dual-polarization radar rain, GPM (Global Precipitation Mission) satellite rain, and observed rainfall at 21 ground stations operated by KMA (Korea Meteorological Administration) respectively. For the 3 water level gauge stations (Sancheong, Changchon, and Namgang) of NamgangDam watershed ($2,293km^2$), the KIMSTORM2 (KIneMatic wave STOrm Runoff Model2) was applied and calibrated with parameters of initial soil moisture contents, Manning's roughness of overland and stream to the event of typhoon CHABA (82 mm in watershed aveprage) in $5^{th}$ October 2016. The radar and GPM data was corrected with CM (Conditional Merging) method such as CM-corrected Radar and CM-corrected GPM. The CM has been used for accurate rainfall estimation in water resources and meteorological field and the method combined measured ground rainfall and spatial data such as radar and satellite images by the kriging interpolation technique. For the CM-corrected Radar and CM-corrected GPM data application, the determination coefficient ($R^2$) was 0.96 respectively. The Nash-Sutcliffe efficiency (NSE) was 0.96 and the Volume Conservation Index (VCI) was 1.03 respectively. The CM-corrected data of Radar and GPM showed good results for the CHABA peak runoff and runoff volume simulation and improved all of $R^2$, NSE, and VCI comparing with the original data application. Thus, we need to use and apply the radar and satellite data to monitor the flood within the watershed.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.522-522
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• 2015

In this study, the effect of threshold applied to the radar rain rate on the comparison of the radar and rain gauge rain rate was theoretically examined. The result derived was also evaluated theoretically, using the Bernoulli random field, and empirically, using Mt. Kwanak weather radar data. The results are summarized as follows. (1) In the application to the Bernoulli random field, it was found that the comparison of the radar and rain gauge rain rate with threshold does not introduce any systematic bias. (2) The same results could also be derived in the application to Mt Kwanak weather radar data. In all cases with several radar bin sizes and thresholds considered, the bias was estimated to be far less than 10% of the mean of the rain gauge rain rate. (3) However, in the comparison with threshold applied to both the radar and rain gauge rain rate, the bias was estimated to be higher than 20%. That is, the systematic bias was introduced. This result indicates that the comparison with threshold applied to both the radar and rain gauge rain rate should not be used.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.4
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• pp.169-178
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• 2018

Currently, various sensors have been used for advanced driver assistance systems. In order to overcome the limitations of individual sensors, sensor fusion has recently attracted the attention in the field of intelligence vehicles. Thus, vision and radar based sensor fusion has become a popular concept. The typical method of sensor fusion involves vision sensor that recognizes targets based on ROIs (Regions Of Interest) generated by radar sensors. Especially, because AVM (Around View Monitor) cameras due to their wide-angle lenses have limitations of detection performance over near distance and around the edges of the angle of view, for high performance of sensor fusion using AVM cameras and radar sensors the exact ROI extraction of the radar sensor is very important. In order to resolve this problem, we proposed a sensor fusion scheme based on commercial radar modules of the vendor Delphi. First, we configured multiple radar data logging systems together with AVM cameras. We also designed radar post-processing algorithms to extract the exact ROIs. Finally, using the developed hardware and software platforms, we verified the post-data processing algorithm under indoor and outdoor environments.

• 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.

• International Journal of Contents
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• v.3 no.4
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• pp.22-25
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• 2007

This paper presents a technology of information data fusion between radar and ELINT electronic intelligence system. adar get the information of the range, direction and velocity of targets, and ELINT system get the information of the direction and angular velocity of the same targets at the same place and at the same time. Since we have some common information data of targets from radar and ELINT system, we can find the target on radar is same or not on ELINT system using the information data fusions. If the target on the radar is verified with the same target on ELINT system, we get more information of the target. e can analysis and identify the target exactly and reduce an ambiguity error of unknown targets.

• 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.