• Title/Summary/Keyword: meteorological observation

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Raw Spectrum Analysis of operated UHF-Wind Profiler Radar in South Korea (국내 운용 UHF-윈드프로파일러 레이더의 원시 스펙트럼 분석)

  • Lee, Kyung-Hun;Kwon, Byung-Hyuk;Kim, Yu-Jin;Lee, Geon-Myeong
    • The Journal of the Korea institute of electronic communication sciences
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    • v.17 no.5
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    • pp.767-774
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    • 2022
  • In this paper raw spectrum data were analyzed to suggest the moving forward of performance evaluation and quality control of wind profilers of four manufacturers operating in South Korea. For the analysis, the profile of the spectrum averaged by season and the profile of four statistical values (minimum, average, median, and maximum) calculated by Power Spectrum Density (PSD) were used. The quality of spectrum data was the best for LAP-3000, followed by YKJ3, PCL-1300, and CLC-11-H. In Cheorwon and Chupungnyeong, where PCL-1300 was installed, the variability of the spectrum due to ground clutter and non-meteorological signals was large, so ground clutter removal and signal processing such as moving average and multi-peak were required. In Gunsan and Paju, where CLC-11-H was installed, DC (Direct Current) bias and propagation folding were found, so it is necessary to remove the DC bias and limit the effective altitude for observation.

Construction of a Spatio-Temporal Dataset for Deep Learning-Based Precipitation Nowcasting

  • Kim, Wonsu;Jang, Dongmin;Park, Sung Won;Yang, MyungSeok
    • Journal of Information Science Theory and Practice
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    • v.10 no.spc
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    • pp.135-142
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    • 2022
  • Recently, with the development of data processing technology and the increase of computational power, methods to solving social problems using Artificial Intelligence (AI) are in the spotlight, and AI technologies are replacing and supplementing existing traditional methods in various fields. Meanwhile in Korea, heavy rain is one of the representative factors of natural disasters that cause enormous economic damage and casualties every year. Accurate prediction of heavy rainfall over the Korean peninsula is very difficult due to its geographical features, located between the Eurasian continent and the Pacific Ocean at mid-latitude, and the influence of the summer monsoon. In order to deal with such problems, the Korea Meteorological Administration operates various state-of-the-art observation equipment and a newly developed global atmospheric model system. Nevertheless, for precipitation nowcasting, the use of a separate system based on the extrapolation method is required due to the intrinsic characteristics associated with the operation of numerical weather prediction models. The predictability of existing precipitation nowcasting is reliable in the early stage of forecasting but decreases sharply as forecast lead time increases. At this point, AI technologies to deal with spatio-temporal features of data are expected to greatly contribute to overcoming the limitations of existing precipitation nowcasting systems. Thus, in this project the dataset required to develop, train, and verify deep learning-based precipitation nowcasting models has been constructed in a regularized form. The dataset not only provides various variables obtained from multiple sources, but also coincides with each other in spatio-temporal specifications.

Interface Conversion to Extend Communication Cable of Ultrasonic Sensor (초음파 센서 통신선 연장을 위한 인터페이스 변환)

  • Seo, Dae-Il;Kwon, Byung-Hyuk;Kim, Sang-Jin
    • The Journal of the Korea institute of electronic communication sciences
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    • v.17 no.3
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    • pp.467-472
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    • 2022
  • The 3D ultrasonic anemometer transmits measured data by connecting PC and RS232C interface. Depending on the observation location, it is often necessary to extend the cable connecting the PC and the sensor. When installing on the test bed of the Air Meteorological Agency, the original AWM2919 cable was required to be extended because the distance between the PC container and the equipment installation site was more than 30 m. The cable was extended through a process such as extending the AWM2919 cable, converting the interface with the PC from RS232C to RS485, and testing the RS485 communication. After the equipment was installed with an extended cable, data were remotely collected and analyzed to confirm successful cable extension.

A Study on the Application of Local-scale Air Mass Recirculation Factor to High-concentration PM2.5 Episode in Coastal Areas (연안 지역 고농도 PM2.5 사례에 대한 국지 규모 공기괴 재순환 지수 적용 연구)

  • Jung-woo Yoo;Ji Seon Kim;Eun Ji Kim;Soon-Hwan Lee
    • Journal of Environmental Science International
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    • v.32 no.8
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    • pp.521-531
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    • 2023
  • This study analyzed the impact of recirculation on high-concentration PM2.5 in the coastal area. Through the analysis of observational data, it was observed that the development of sea breeze led to an increase in PM2.5 and SO42- concentrations. Hysplit backward trajectory analysis confirmed the occurrence of air mass recirculation. Results from WRF and CMAQ numerical simulations indicated that pollutants transported from land to sea during the night were re-transported to the land by daytime sea breeze, leading to high-concentration PM2.5 in Busan. To quantitatively investigate the recirculation a recirculation factor (RF) was calculated, showing an increase in RF values during high-concentration PM2.5 episodes. However, the RF values varied slightly depending on the time resolution of meteorological data used for the calculations. This variation was attributed to the terrain characteristics at observation sites. Additionally, during long-range transported days leading to nationwide high-concentration PM2.5 events, synoptic-scale circulation dominated, resulting in weaker correlation between PM2.5 concentration and RF values. This study enhances the understanding of the influence of recirculation on air pollution. However, it is important to consider the impact of temporal resolution and terrain characteristics when using RF for evaluating recirculation during episodes of air pollution.

Short-Term Water Quality Prediction of the Paldang Reservoir Using Recurrent Neural Network Models (순환신경망 모델을 활용한 팔당호의 단기 수질 예측)

  • Jiwoo Han;Yong-Chul Cho;Soyoung Lee;Sanghun Kim;Taegu Kang
    • Journal of Korean Society on Water Environment
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    • v.39 no.1
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    • pp.46-60
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    • 2023
  • Climate change causes fluctuations in water quality in the aquatic environment, which can cause changes in water circulation patterns and severe adverse effects on aquatic ecosystems in the future. Therefore, research is needed to predict and respond to water quality changes caused by climate change in advance. In this study, we tried to predict the dissolved oxygen (DO), chlorophyll-a, and turbidity of the Paldang reservoir for about two weeks using long short-term memory (LSTM) and gated recurrent units (GRU), which are deep learning algorithms based on recurrent neural networks. The model was built based on real-time water quality data and meteorological data. The observation period was set from July to September in the summer of 2021 (Period 1) and from March to May in the spring of 2022 (Period 2). We tried to select an algorithm with optimal predictive power for each water quality parameter. In addition, to improve the predictive power of the model, an important variable extraction technique using random forest was used to select only the important variables as input variables. In both Periods 1 and 2, the predictive power after extracting important variables was further improved. Except for DO in Period 2, GRU was selected as the best model in all water quality parameters. This methodology can be useful for preventive water quality management by identifying the variability of water quality in advance and predicting water quality in a short period.

Estimation of the SARS-CoV-2 Virus Inactivation Time Using Spectral Ultraviolet Radiation (파장별 지표 자외선 복사량을 이용한 SARS-CoV-2 바이러스 비활성화 시간 추정 연구)

  • Park, Sun Ju;Lee, Yun Gon;Park, Sang Seo
    • Atmosphere
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    • v.32 no.1
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    • pp.51-60
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    • 2022
  • Corona Virus Disease 19 pandemic (COVID-19) causes many deaths worldwide, and has enormous impacts on society and economy. The COVID-19 was caused by a new type of coronavirus (Severe Acute Respiratory Syndrome Cornonavirus 2; SARS-CoV-2), which has been found that these viruses can be effectively inactivated by ultraviolet (UV) radiation of 290~315 nm. In this study, 90% inactivation time of the SARS-CoV-2 virus was analyzed using ground observation data from Brewer spectrophotometer at Yonsei University, Seoul and simulation data from UVSPEC for the period of 2015~2017 and 2020. Based on 12:00-13:00 noon time, the shortest virus inactivation time were estimated as 13.5 minutes in June and 4.8 minutes in July/August, respectively, under all sky and clear sky conditions. In the diurnal and seasonal variations, SARS-CoV-2 could be inactivated by 90% when exposed to UV radiation within 60 minutes from 10:00 to 14:00, for the period of spring to autumn. However, in winter season, the natural prevention effect was meaningless because the intensity of UV radiation weakened, and the time required for virus inactivation increased. The spread of infectious diseases such as COVID-19 is related to various and complex interactions of several variables, but the natural inactivation of viruses by UV radiation presented in this study, especially seasonal differences, need to be considered as major variables.

Evaluation of the snow simulations from CLM using satellite-based observations (위성 관측 자료를 활용한 지면모형(CLM)의 적설 모의 평가)

  • Seo, Jungho;Seo, Hocheol;Kim, Yeonjoo
    • Proceedings of the Korea Water Resources Association Conference
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    • 2022.05a
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    • pp.332-332
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    • 2022
  • 적설은 지구 기후시스템과 수문순환 과정에서 중요한 역할을 하고 있으며, 겨울철의 적설은 봄철에 녹으면서 식생과 수자원 제공에 큰 영향을 주는 인자로 알려져 있다. 동아시아가 위치한 북반구는 적설량의 90%가 관찰되고 토지의 약 42%가 긴 시간동안 눈으로 덮여 있어 지표 에너지와 물 균형에 영향을 주고, 특히 수자원 관리를 위한 유출이나 토양수분과 같은 수문 인자에 큰 영향을 미친다. 따라서 적설을 정확하게 예측하는 것은 수자원 관리에 있어 매우 중요한 일이다. 한편, 이러한 수문 순환을 정확히 예측하기 위해 수문 분야에서는 지면모형(Land Surface Model, LSM)을 많이 사용하고 있다. 지면모형은 지표면과 대기 사이의 상호작용을 모의하기 위해 개발되었고, 에너지, 수증기, 이산화탄소 등의 다양한 인자들의 교환에 대하여 해석하며, 토양수분, 유출량 등의 수자원 분야의 주요 인자들을 산출하여 수자원 관리에 적극적으로 활용되고 있다. 이에 본 연구에서는 National Center for Atmospheric Research(NCAR)에서 개발한 Community Land Model(CLM)을 사용하여 2001년부터 2016년까지 25km의 공간해상도로 동아시아 지역의 적설 모의를 평가하였다. CLM의 적설 모의 평가 인자는 Snow depth, Snow water equivalent의 2가지 인자를 대상으로 수행하였고, 모의 성능 평가를 위한 관측 자료로 NASA Aqua와 JAXA GCOM-W1 위성에 탑재된 Advanced Microwave Scanning Radiometer(AMSR) 센서에서 제공하는 위성 관측 자료와 Defense Meteorological Satellite Program(DMSP) 위성의 Special Sensor Microwave/Imager(SSM/I) 센서와 Nimbus-7 위성의 Scanning Multichannel Microwave Radiometer(SMMR) 센서에서 제공하는 위성 관측 자료를 기반으로 지상 기상 관측소 자료와 조합하여 재생성한 European Space Agency Global Snow Monitoring for Climate Research (ESA GlobSnow)의 자료를 사용하였다. 그 결과 CLM의 적설 모의는 과대 추정하는 것을 알 수 있었으며, 본 연구의 결과는 동아시아 적설 모의 개선을 위해 자료 동화를 사용하는 후속 연구의 기초자료로 사용할 수 있다.

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A Study on the Analysis of Jeju Island Precipitation Patterns using the Convolution Neural Network (합성곱신경망을 이용한 제주도 강수패턴 분석 연구)

  • Lee, Dong-Hoon;Lee, Bong-Kyu
    • Journal of Software Assessment and Valuation
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    • v.15 no.2
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    • pp.59-66
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    • 2019
  • Since Jeju is the absolute weight of agriculture and tourism, the analysis of precipitation is more important than other regions. Currently, some numerical models are used for analysis of precipitation of Jeju Island using observation data from meteorological satellites. However, since precipitation changes are more diverse than other regions, it is difficult to obtain satisfactory results using the existing numerical models. In this paper, we propose a Jeju precipitation pattern analysis method using the texture analysis method based on Convolution Neural Network (CNN). The proposed method converts the water vapor image and the temperature information of the area of ​​Jeju Island from the weather satellite into texture images. Then converted images are fed into the CNN to analyse the precipitation patterns of Jeju Island. We implement the proposed method and show the effectiveness of the proposed method through experiments.

Astronomical Phenomenon Records from Sukjong's Chunbang-Ilgi

  • Ki-Won Lee
    • Journal of The Korean Astronomical Society
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    • v.56 no.1
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    • pp.75-89
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    • 2023
  • We investigate the astronomical phenomenon records of Sukjong's Chunbang-Ilgi made by Sigangwon (Royal Educational Office of the Crown Prince) at which King Sukjong was the crown prince (i.e., 1667 January 24-1674 September 22). From the daily records of 2,799 days, we extract the astronomical records of 1,443 days and classify them into 14 categories. Then, we group the records of each category into five phenomena (Atmosphere, Eclipse, Daylight Appearance, Apparition, and Appulse) and compare them with the results of modern astronomical computations wherever possible. Except for Atmosphere group comprising records of meteorological events, such as solar halo, lunar halo, and unusual clouds, the significant findings in every other group are as follows: In Eclipse group, the solar eclipse that occurred on 1673 August 12 was unobservable in Korea, which is in contrast to the record of Joseonwangjo-Sillok (Annals of the Joseon Dynasty), which states that the sun was in eclipse around sunset time, as observed at Nam mountain. From the lunar eclipse records, we verify that the Joseon court did not change the date of the events observed after midnight. In Daylight Appearance group, we confirm that this phenomenon was observed during the daytime and not during twilight. We further suggest that if observation conditions are met, a celestial body brighter than -2.3 mag could be seen during the daytime with the naked-eye. In Apparition group, we find the possibilities that the Orionid meteor shower had influence on the meteor records and the seasonality on the aurora records. We also find that the Korean records in which the coma of comet C/1668 E1 was located below the horizon were overlooked in previous studies. Finally, we find that the records of Appulse group generally agree with the results of modern calculations. The records of Beom (trespass in literal) and Sik (eating in literal) events show average angular separations of 1.2° and 1.0°, respectively. In conclusion, we believe this work helps study the astronomical records of other logs of Sigangwon, such as Sukjong's Chunbang-Ilgi.

Comparative Analysis of the 2022 Southern Agricultural Drought Using Evapotranspiration-Based ESI and EDDI (증발산 기반 ESI와 EDDI를 활용한 2022년 남부지역의 농업 가뭄 분석)

  • Park, Gwang-Su;Nam, Won-Ho;Lee, Hee-Jin;Sur, Chanyang;Ha, Tae-Hyun;Jo, Young-Jun
    • Journal of The Korean Society of Agricultural Engineers
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    • v.66 no.3
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    • pp.25-37
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    • 2024
  • Global warming-induced drought inflicts significant socio-economic and environmental damage. In Korea, the persistent drought in the southern region since 2022 has severely affected water supplies, agriculture, forests, and ecosystems due to uneven precipitation distribution. To effectively prepare for and mitigate such impacts, it is imperative to develop proactive measures supported by early monitoring systems. In this study, we analyzed the spatiotemporal changes of multiple evapotranspiration-based drought indices, focusing on the flash drought event in the southern region in 2022. The indices included the Evaporative Demand Drought Index (EDDI), Standardized Precipitation Evapotranspiration Index (SPEI) considering precipitation and temperature, and the Evaporative Stress Index (ESI) based on satellite images. The Standardized Precipitation Index (SPI) and SPEI indices utilized temperature and precipitation data from meteorological observation stations, while the ESI index was based on satellite image data provided by the MODIS sensor on the Terra satellite. Additionally, we utilized the Evaporative Demand Drought Index (EDDI) provided by the North Oceanic and Atmospheric Administration (NOAA) as a supplementary index to ESI, enabling us to perform more effective drought monitoring. We compared the degree and extent of drought in the southern region through four drought indices, and analyzed the causes and effects of drought from various perspectives. Findings indicate that the ESI is more sensitive in detecting the timing and scope of drought, aligning closely with observed drought trends.