• Journal of the Korean earth science society
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• v.44 no.2
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• pp.119-134
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• 2023

The synoptic, thermodynamic, and dynamic characteristics of a snowfall event that occurred in the Yeongdong region on March 1-2, 2021, were investigated. Surface weather charts, ERA5 reanalysis data, rawinsonde data, GK-2A satellite data, and WISSDOM data were used for analysis. The snow depth, exceeding 10 cm, was observed at four weather stations during the analysis period. The maximum snow depth (37.4 cm) occurred at Bukgangneung. According to the analysis of the weather charts, old and dry air was trapped within relatively warm, humid air in the upper atmosphere over the East Sea and adjacent Yeongdong region. This caused unstable atmospheric conditions that led to developing convective clouds and snowfall over Bukgangneung. In particular, based on the thermodynamic and kinematic vertical analysis, we suggest that strong winds attributable to the vertical gradient of potential temperature in the low layer and the development of convective instability due to cold advection played a significant role in the occurrence of snowfall in the Yeongdong region. These results were confirmed from the vertical analysis of the rawinsonde data.

• Journal of Digital Convergence
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• v.14 no.3
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• pp.209-217
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• 2016

Recently, the outdoor activities were increased in accordance with economic growth and improved quality of life. In addition, weather and outdoor activities are closely related. Currently, Outdoor Activities decisions are determined by the Korea Meteorological Administrator's forecasts and subjective experience. Therefore, we need the analysis method that can provide a basis for the decision on outdoor activities based on meteorological information. In this paper, we propose an algorithm that can analyze meteorological information to support decision-making outdoor activities. And the algorithm is based on the data mining. In addition, we have constructed a baseball game schedule with automatic weather system's observation data in the training data. We verified the improved performance of the proposed algorithm.

• Journal of Korean Society of Disaster and Security
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• v.11 no.2
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• pp.45-52
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• 2018

This study identifies the cause of the accident and presents a new concept for safe urban stream management by numerical simulating the flood event of Cheonggyecheon on August 17, 2017, using rain data measured through a dense weather observation network. In order to simulate water retention in the CSO channel listed as one of the causes of the accident, a reliable urban runoff model(XP-SWMM) was used which can simulate various channel conditions. Rainfall data measured through SK Techx using SK Telecom's cell phone station was used as rain data to simulate the event. The results of numerical simulations show that rainfall measured through AWSs of Korea Meteorological Administration did not cause an accident, but a similar accident occurred under conditions of rainfall measured in SK Techx, which could be estimated more similar to actual phenomena due to high spatial density. This means that the low spatial density rainfall data of AWSs cannot predict the actual phenomenon occurring in Cheonggyecheon and safe river management needs high spatial density weather stations. Also, the results of numerical simulation show that the residual water in the CSO channel directly contributed to the accident.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.3
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• pp.104-118
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• 2018

This study is to evaluate the accuracy improvement of the model using SWAT(Soil and Water Assessment Tool) model and multi - point hydrological observation data. The watershed is located in the Yongdam Dam($930.4km^2$), the Donghyang($165.5km^2$), the Chuncheon($290.9km^2$), the Juchun($57.8km^2$) and the Seokjeong($80.5km^2$). The watershed covers 70.0 % forest. In order to improve the accuracy of the model, precipitation data were used from two weather stations(Jangsu, Geumsan) and 16 AWS stations daily precipitation data(2003~2011) managed by KMA, MLIT, and K-water. Based on the reliable data of the Yongam test basin in 2003~2011, the runoff of single point (Yongdam dam) and multi-point (Donghyang, Chuncheon, Jucheon, Seokjeong). Simulation results show that the $R^2$ of the single subwatershed (Donghyang, Chuncheon, Jucheon, Seokjeong) is single point(0.84) and multipoint(0.88). For model efficiency coefficient of Nash-Sutcliffe at single point(0.45) and multipoint(0.70).

• Journal of the Korean Association of Geographic Information Studies
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• v.4 no.1
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• pp.57-66
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• 2001

This study used four theoretical models, such as two-point linear model, linear regression model, quadratic regression model and cubic regression model which are presented from The Ministry of Science and Technology, for extraction of urban surface temperature from Landsat TM band 6 image. Through correlation and regression analysis between result of four models and AWS(automatic weather station) observation data, this study could verify spatial distribution characteristic of urban surface temperature using GIS spatial analysis method. The result of analysis for surface temperature by landcover showed that the urban and the barren land belonged to the highest surface temperature class. And there was also -0.85 correlation in the result of correlation analysis between surface temperature and NDVI. In this result, the meteorological environmental characteristics wuld be regarded as one of the important factor in urban planning.

• Korean Journal of Agricultural and Forest Meteorology
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• v.13 no.1
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• pp.35-40
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• 2011

While high-definition precipitation maps with a 270 m spatial resolution are available for South Korea, there is little information on geospatial availability of precipitation water for the famine - plagued North Korea. The restricted data access and sparse observations prohibit application of the widely used PRISM (Parameter-elevation Regressions on Independent Slopes Model) to North Korea for fine-resolution mapping of precipitation. A hybrid method which complements the PRISM grid with a sub-grid scale elevation function is suggested to estimate precipitation for remote areas with little data such as North Korea. The fine scale elevation - precipitation regressions for four sloping aspects were derived from 546 observation points in South Korea. A 'virtual' elevation surface at a 270 m grid spacing was generated by inverse distance weighed averaging of the station elevations of 78 KMA (Korea Meteorological Administration) synoptic stations. A 'real' elevation surface made up from both 78 synoptic and 468 automated weather stations (AWS) was also generated and subtracted from the virtual surface to get elevation difference at each point. The same procedure was done for monthly precipitation to get the precipitation difference at each point. A regression analysis was applied to derive the aspect - specific coefficient of precipitation change with a unit increase in elevation. The elevation difference between 'virtual' and 'real' surface was calculated for each 270m grid points across North Korea and the regression coefficients were applied to obtain the precipitation corrections for the PRISM grid. The correction terms are now added to the PRISM generated low resolution (~2.4 km) precipitation map to produce the 270 m high resolution map compatible with those available for South Korea. According to the final product, the spatial average precipitation for entire territory of North Korea is 1,196 mm for a climatological normal year (1971-2000) with standard deviation of 298 mm.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.4
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• pp.298-303
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• 2013

In this study, the installation effects of a windbreak net on reducing wind speed were examined using meteorological observation data. Three automatic weather systems (AWS) were installed at the inside of the windbreak net of an apple orchard along the distance, and one AWS was located at the outside. They were set up on Oct. $25^{th}$, 2012 at the apple orchard of Geochang County, and meteorological data, which were observed at four stations for about 1 year from October 26, 2012 through October 21, 2013, were used for this study. Meteorological variables include air temperature & relative humidity at a height of 1.5 meter, and wind speed & wind direction at a height of 2 meter and 4 meter. These variables were measured and recorded every 60 seconds. The preliminary results of this study was as follows: (1) Daily mean temperature at the inside of the windbreak net was lower than that of the outside by 0.1 through $0.2^{\circ}C$. (2) Daily mean relative humidity at the inside of the windbreak net was higher than that of the outside by 0.7 through 1.7%. (3) As compared to the outside, northerly daily maximum wind speed of the inside of the windbreak net at a height of 2 and 4 meter was reduced by 0.7~1.5 $ms^{-1}$ (30.4~65.2%) and 0.3~0.7 $ms^{-1}$ (10.3~24.1%), respectively.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1948-1952
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• 2007

본 연구에서는 레이더 강우의 지상보정(ground-truth)을 위해 사용가능한 기상청(KMA), AWS 및 건설교통부(MOCT) 강우자료를 다양한 지상보정 설계에 적용하여 비교 평가하였다. 본 연구에서는 동일 기간의 KMA, MOCT, AWS의 우량계 자료와 관악산 레이더 강우자료를 이용하였으며, 각각 두 관측방법사이의 차이(오차)를 편의(bias)의 유무 및 크기의 관점에서 평가하였다. 추가로 호우 사상의 특성에 따른 차이도 함께 검토하였다. 그 적용 결과 지상우량계 자료별 편의의 차이는 확연하게 부각되지는 않았으나, 통계 특성치에서는 어느 정도의 차이가 존재함을 확인하였다. 전체적으로 보면 MOCT 우량계 자료를 이용하는 경우가 다른 강우자료를 이용하는 경우에 비해 편의의 규모가 제일 작은 것으로 확인되었다. 호우 사상별로는 강우의 공간적 간헐성이 가장 큰 장마 기간의 경우가 태풍이나 대류성 강우에 비해 설계편의가 작게 나타나는 것으로 확인되었다.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.280-280
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• 2023

강수의 정확한 시·공간적 추정은 홍수 대응, 가뭄 관리, 수자원 계획 등 수문학적 모델링의 핵심 기술이다. 우주 기술의 발전으로 전지구 강수량 측정 프로젝트(Global Precipitation Measurement, GPM)가 시작됨에 따라 위성의 여러 센서를 이용하여 다양한 고해상도 강수량 자료가 생산되고 있으며, 기후변화로 인한 수재해의 빈도가 증가함에 따라 준실시간(Near-Real-Time) 위성 강수 자료의 활용성 및 중요성이 높아지고 있다. 하지만 준실시간 위성 강수 자료의 경우 빠른 지연시간(latency) 확보를 위해 관측 이후 최소한의 보정을 거쳐 제공되므로 상대적으로 강수 추정치의 불확실성이 높다. 이에 따라 본 연구에서는 앙상블 머신러닝 기반 수집된 위성 강수 자료들을 관측 자료와 병합하여 보정된 준실시간 강수량 자료를 생성하고자 한다. 모형의 입력에는 시단위 3가지 준실시간 위성 강수 자료(GSMaP_NRT, IMERG_Early, PERSIANN_CCS)와 방재기상관측 (AWS)의 온도, 습도, 강수량 지점 자료를 활용하였다. 지점 강수 자료의 경우 결측치를 고려하여 475개 관측소를 선정하였으며, 공간성을 고려한 랜덤 샘플링으로 375개소(약 80%)는 훈련 자료, 나머지 100개소(약 20%)는 검증 자료로 분리하였다. 모형의 정량적 평가 지표로는 KGE, MAE, RMSE이 사용되었으며, 정성적 평가 지표로 강수 분할표에 따라 POD, SR, BS 그리고 CSI를 사용하였다. 머신러닝 모형은 개별 원시 위성 강수 자료 및 IDW 기법보다 높은 정확도로 강수량을 추정하였으며 공간적으로 안정적인 결과를 나타내었다. 다만, 최대 강수량에서는 다소 과소추정되므로 이는 강수와 관련된 입력 변수의 개수 업데이트로 해결할 수 있을 것으로 판단된다. 따라서 불확실성이 높은 개별 준실시간 위성 자료들을 관측 자료와 병합하여 보정된 최적 강수 자료를 생성하는 머신러닝 기법은 돌발성 수재해에 실시간으로 대응 가능하며 홍수 예보에 신뢰도 높은 정량적인 강수량 추정치를 제공할 수 있다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.491-493
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• 2022

Flood damage due to torrential rains and typhoons is occurring in many parts of the world. In this paper, we propose a water level prediction model using water level, precipitation, and humidity data, which are key parameters for flood prediction, as input data. Based on the LSTM and GRU models, which have already proven time-series data prediction performance in many research fields, different input datasets were constructed using the ASOS(Automated Synoptic Observing System) data and AWS(Automatic Weather System) data provided by the Korea Meteorological Administration, and performance comparison experiments were conducted. As a result, the best results were obtained when using ASOS data. Through this paper, a performance comparison experiment was conducted according to the input data, and as a future study, it is thought that it can be used as an initial study to develop a system that can make an evacuation decision in advance in connection with the flood risk determination model.