• Journal of Aerospace System Engineering
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• v.15 no.3
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• pp.11-19
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• 2021

Contrails are line-shaped clouds formed by the condensation of water vapor from the interaction of exhaust gas from aircraft engines and the high-altitude atmosphere. Contrails are known to aggravate global warming by creating a greenhouse effect by absorbing or reflecting radiation emitted from the Earth. In this study, development of a model that can quantitatively predict the contrail occurrence was conducted for the reduction of contrail, which is likely to form an aircraft certification category in the future. Based on prior research results, a model that can predict the occurrence of contrail between Tokyo and Qingdao was developed, in addition to proposing improved flight altitude that can minimize the occurrence of contrail.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.3
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• pp.483-488
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• 2019

The propagation prediction model of the earth-space communication link used as an international standard was used to calculate and analyze the total losses on the communication path. The standard definition and scope of ITU-R Rec. were analyzed for each parameter(rain, scintillation, atmospheric gas, clouds) used to calculate the total loss. The total losses were calculated using the standard model for each parameter and the statistical data provided by ITU-R, and the results were analyzed using the validation examples data. The rain losses were calculated using long-term local rainfall attenuation statistics data measured in the region, and compared with the calculation results using a rainfall map in the ITU-R Recommendation. The data of Cheollian satellites for the L-Band and Ka-Band were used to calculate the rainfall attenuation. In the range of 0.01% to 0.1%, it was found to have a greater attenuation slope when using local data than attenuation by the model of ITU-R.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.156-172
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• 2020

The purpose of this study was to predict road surface temperature using high-resolution solar radiation data. The road surface temperature prediction model (RSTPM) was applied to predict road surface temperature; this model was developed based on the heat-balance method. In addition, using SOLWEIG (SOlar and LongWave Environmental Irradiance Geometry-model), the shadow patterns caused by the terrain effects were analyzed, and high-resolution solar radiation data with 10 m spatial resolution were calculated. To increase the accuracy of the shadow patterns and solar radiation, the day that was modeled had minimal effects from fog, clouds, and precipitation. As a result, shadow areas lasted for a long time at the entrance and exit of a tunnel, and in a high-altitude area. Furthermore, solar radiation clearly decreased in areas affected by shadows, which was reflected in the predicted road surface temperatures. It was confirmed that the road surface temperature should be high at topographically open points and relatively low at higher altitude points. The results of this study could be used to forecast the freezing of sections of road surfaces in winter, and to inform decision making by road managers and drivers.

• Journal of KSNVE
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• v.10 no.3
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• pp.444-450
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• 2000

Wheel-rail noise is normally classified into three catagories : rolling impact and squeal noise. In this paper rolling noise caused by the irregularity between a wheel and a rail is analysed as follows: The irregularity between the wheel and the rail is assumed as linear superposition of sinusoidal profiles. Wheel-rail contact stiffness is linearized by using Hertzian contact theory and then contact force between the wheel and the rail is calculated. vibration of the rail and the wheel is calculated theoretically by receptance method or FEM depending on the geometry of the wheel or the rail for the frequency range of 100-500 Hz important for noise generation. The radiation noise caused by those vibration response is computed by BEM To verify this analysis tools rolling noise is calculated by proposed analysis steps using typical roughness data and these results are compared with experimental rolling noise data. This analysis tools show reasonable results and finally used for the prediction of the Korean high speed train rolling noise.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2007.05a
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• pp.99-101
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• 2007

비종관 관측자료인 기상위성 및 레이더 분석자료에서 위성영상에서 볼 수 있는 서해남부 해상의 스콜라인 형태의 강한 대류운을 무안-진도 레이더 반사도에 의한 수평 및 연직 구조로 살펴보았는데 특히 발달한 대류운 영역이 보다 상세한 반사도 차이로 나타나고, 40 dBZ 이상 강한 반사도 셀이 고도 6km, 20km 정도의 수평규모를 갖는 여러 대류운시스템이 합쳐진 다중세포시스템으로 구성되어 있음을 알 수 있었다. 또한 연구용 X-대역 무안 레이더 반사도 분포를 보면 사이트 주면에 강한 dBZ의 대류운이 자리하고 있어 발달한 강수입자에 의한 감쇄효과 때문에 북서방향과 남서방향의 강한 대류운들이 약하게 잘못 관측되고 있음을 볼 수 있었지만 S-대역 진도 레이더는 발달한 강수 입자에 크게 영향을 받지 않고 고유의 강한 반사도를 제대로 관측하고 있음을 알 수 있었다. 이와 같이 태풍에 의한 직${\cdot}$간접적인 호우, 장마전선의 활성화에 따른 집중호우를 동반하는 중규모대류운시스템에 대해 정확히 정량적으로 판단할 수는 없지만, 관측자료 분석을 통해 중규모대류운시스템을 발달${\cdot}$유지시킬 수 있는 기구의 존재 가능성을 체계적으로 이해하는데 많은 도움이 되었다. 본 연구 결과를 바탕으로 레이더 반사도와 3차원 바람장 그리고 마이크로강우레이더와 광학강우강계에 의한 연직 구름계와 순간적으로 발생하는 강수특성 등 섬세하고 다양한 비종관 관측자료를 이용하여 집중호우를 유발하는 중규모대류운시스템의 강수 구조와 특성 분석 및 예측에 활용될 수 있으리라 기대된다.

• Journal of IKEEE
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• v.24 no.2
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• pp.403-408
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• 2020

Photovoltaic (PV) has both intermittent and uncertainty in nature, so it is difficult to accurately predict. Thus anomaly detection technology is important to diagnose real time PV generation. This paper identifies a correlation between various parameters and classifies the PV data applying k-nearest neighbor and dynamic time warpping. Results for the two classifications showed that an outlier detection by a fault of some facilities, and a temporary power loss by partial shading and overall shading occurring during the short period. Based on 100kW plant data, machine learning analysis and test results verified actual outliers and candidates of outlier.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.310-310
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• 2023

기상레이더는 강우의 공간분포를 관측하고 강우장 이동특성을 예측하여 집중호우, 태풍 등에 대비할 수 있는 시간을 확보하기 위하여 운용되고 있다. 기상레이더는 전파를 송신하고 대기 중의물체(수상체, 건물 등)에 부딪혀 되돌아오는 신호를 수신하여 강우의 양, 분포, 이동방향 등을 산정할 수 있으며 세부적으로 입체관측(volume scan)을 반복하여 고도각 별로 거리와 방위각에 따라 다양한 합성영상을 산출할 수 있는 특성이 있다. 본 연구는 구름의 수평적 분포를 파악하는데 용이하여 기존에 널리 사용된 CAPPI 합성영상과 최근 현업에서 복잡한 지형으로 인한 오차를 해소하고자 광범위하게 사용되고 있는 다중 고도각 기반 레이더 강수량(hybrid surface rainfall, HSR) 합성영상을 취득하여 수문해석을 위한 유역단위 면적강수량의 추정오차를 검토하였다. HSR 합성영상은 우리나라와 같이 산악지형이 많이 존재하는 경우 지형의 영향을 받지 않아 지면에 가장 가까운 고도각의 관측자료를 사용하므로 지상관측소 강수량과 비교한 결과에서 기존의 CAPPI 합성영상 레이더 강수량과 통계적 효율 기준을 산정하여 레이더 강수의 품질이 개선되는 것을 확인하였다. 최근 환경부에서 추진하고 있는 인공지능(AI) 홍수예보 및 가상모형(Digital Twin)을 활용하여 홍수정보를 생산 및 전달하는 과정에서 유역의 지형적 특성을 현실적으로 고려한 레이더 강수량을 사용함으로 기후변화에 따라 국지적으로 발생하는 집중호우 대응 및 과학적 홍수관리를 실현할 수 있을 것으로 판단된다.

• Asian Journal of Turfgrass Science
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• v.20 no.1
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• pp.11-23
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• 2006

This study was carried out to propose low types of green speed expectation models for fast putting green management by changing mowing height($4.0{\sim}2.5$ mm) and timing of rolling, dew removal and dew removal+rolling. Ball roll distance data were taken from the creeping bentgrass(Agrostis palustris Huds. 'Penncross') practice green of east course at the Lakeside C.C. in October 18, 2001 and May 25, 2002. Data were subjected to multi-regression analysis using Statistical Package for the Social Science. Among four types of green speed expectation models, the best multiple-regression equation for fast green management was as follows; $Y_4=4.171-0.225{\cdot}X_1-0.038{\cdot}X_2$ (where, $Y_4$ : green speed(m) after single dew removal+single rolling, $X_1$ : mowing height($4.0{\sim}2.5,\;X_2$ : passage of time ($0{\sim}8$ h.)). The equation[single dew removal by using sponge roller $\rightarrow$ single mowing at 3.0 mm height or less $\rightarrow$ single rolling] explained to provide fast green over 3.2 m (Stimpmeter readings required for USGA championship play) until the end of first round. Therefore, this cultural practice system was believed to provide fast putting green condition for professional golf tournament

• Journal of the Korean earth science society
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• v.44 no.6
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• pp.557-577
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• 2023

This study defines non-precipitation information as areas with weak precipitation or cloud particles that radar cannot detect due to weak returned signals, and suggests methods for its utilization in data assimilation. Previous studies have demonstrated that assimilating radar data from precipitation echoes can produce precipitation in model analysis and improve subsequent precipitation forecast. However, this study also recognizes the non-precipitation information as valuable observation and seeks to assimilate it to suppress spurious precipitation in the model analysis and forecast. To incorporate non-precipitation information into data assimilation, we propose observation operators that convert radar non-precipitation information into hydrometeor mixing ratios and relative humidity for the Weather Research and Forecasting Data Assimilation system (WRFDA). We also suggest a preprocessing method for radar non-precipitation information. A single-observation experiment indicates that assimilating non-precipitation information fosters an environment conducive to inhibiting convection by lowering temperature and humidity. Subsequently, we investigate the impact of assimilating non-precipitation information to a real case on July 23, 2013, by performing a subsequent 9-hour forecast. The experiment that assimilates radar non-precipitation information improves the model's precipitation forecasts by showing an increase in the Fractional Skill Score (FSS) and a decrease in the False Alarm Ratio (FAR) compared to experiments in which do not assimilate non-precipitation information.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.