• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.471-484
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• 2022

This study examined the effects of topography and buildings around the automated synoptic observing system (ASOS) on the observation environment of air temperatures and wind speeds and directions using a computational fluid dynamics(CFD) model. For this, we selected 10 ASOSs operated by the Korea Meteorological Administration. Based on the data observed at the ASOSs in August during the recent ten years, we established the initial and boundary conditions of the CFD model. We analyzed the temperature observation environment by comparing the temperature change ratios in the case considering the actual land-cover types with those assuming all land-cover types as grassland. The land-cover types around the ASOSs significantly affected the air temperature observation environment. The temperature change ratios were large at the ASOSs around which buildings and roads were dense. On the other hand, when all land covers were assumed as grassland, the temperature change ratios were small. Wind speeds and directions at the ASOSs were also significantly influenced by topography and buildings when their heights were higher or similar to the observation heights. Obstacles even located at a long distance affected the wind observation environments. The results in this study would be utilized for evaluating ASOS observation environments in the relocating or newly organizing steps.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.693-706
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• 2020

For ten meteorological observatories running an automated synoptic observing system (ASOS), we classified the observation environments into five classes based on the World Meteorological Organization (WMO) classification guidelines. Obstacles (such as topography and buildings) and land-cover types were the main factors in evaluating the observation environments for the sunshine duration, air-temperature, and surface wind. We used the digital maps of topography, buildings, and land-cover types. The observation environment of the sunshine duration was most affected by the surrounding buildings when the solar altitude angle was low around the sunrise and sunset. The air-temperature observation environment was determined based on not only the solar altitude angle but the distance between the heat/water source and ASOS. There was no water source around the ASOSs considered in this study. Heat sources located near some ASOSs were not large enough to affect the observation environment. We evaluated the surface wind observation environment based on the roughness length around the ASOS and the distance between surrounding buildings and the ASOS. Most ASOSs lay at a higher altitude than the surroundings and the roughness lengths around the ASOSs were small enough to satisfy the condition for the best level.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1071-1081
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• 2021

In addition to the current quality control procedures for the weather observation performed by the Korea Meteorological Administration (KMA), this study proposes quality inspection standards for Internet of Things (IoT) urban weather observed data based on machine learning that can be used in smart cities of the future. To this end, in order to confirm whether the standards currently set based on ASOS (Automated Synoptic Observing System) and AWS (Automatic Weather System) are suitable for urban weather, usability was verified based on SKT AWS data installed in Seoul, and a machine learning-based quality control algorithm was finally proposed in consideration of the IoT's own data's features. As for the quality control algorithm, missing value test, value pattern test, sufficient data test, statistical range abnormality test, time value abnormality test, spatial value abnormality test were performed first. After that, physical limit test, stage test, climate range test, and internal consistency test, which are QC for suggested by the KMA, were performed. To verify the proposed algorithm, it was applied to the actual IoT urban weather observed data to the weather station located in Songdo, Incheon. Through this, it is possible to identify defects that IoT devices can have that could not be identified by the existing KMA's QC and a quality control algorithm for IoT weather observation devices to be installed in smart cities of future is proposed.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.171-171
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• 2017

강우의 시간분포는 다양한 시간규모에 걸쳐 프랙털 또는 멀티프랙털 특성을 가지고 있음이 알려졌다. 기존의 연구는 주로 시간단위 이상의 프랙털 특성에 관한 것이었다. 실제로 극한 홍수를 가져오는 집중호우는 짧은 시간 규모에서 발생함에도, 이것에 대해서는 관측 자료가 제한되어 극소수의 실험적 연구만 가능했다. 본 연구에서는 기상청에서 제공한 고해상도(1분 단위) ASOS(Automated Synoptic Observation System) 자료를 이용하여, 강우 사상 안에서의 프랙털 특성을 분석해보았다. 대부분의 사상에서 단일 멱함수보다는 2개의 멱함수로 나누어지는 것이 밝혀졌으며, 나뉘는 시간 규모(T*)는 $3{\times}10$ 분으로 파악되었다. 이 시간 규모는 한 단위의 집중호우를 가져올 수 있는 구름크기의 물리적 상한과 관련이 있는 것으로 보인다. T*보다 작은 시간 규모에서의 멱함수 지수는 그 이후의 값보다 대체로 작은 것으로 나타났다. 이는 호우가 집중되는 기간의 변동성이, 강수가 물리적 한계에 도달한 이후보다 훨씬 작기 때문으로 보인다. 구체적인 멱함수의 지수는 강수의 발생과정과도 관련이 있을 것으로 추정된다.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.65-81
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• 2021

In this study, soil moisture and evapotranspiration were calculated throughout South Korea using the Korea Land Data Assimilation System(KLDAS) of the Korea-Land Surface Information System(K-LIS) built on the basis of the Land Information System (LIS). The hydrometeorological data sets used to drive K-LIS and build KLDAS are MERRA-2(Modern-Era Retrospective analysis for Research and Applications, version 2) GDAS(Global Data Assimilation System) and ASOS(Automated Synoptic Observing System) data. Since ASOS is a point-based observation, it was converted into grid data with a spatial resolution of 0.125° for the application of KLDAS(ASOS-S, ASOS-Spatial). After comparing the hydrometeorological data sets applied to KLDAS against the ground-based observation, the mean of R² ASOS-S, MERRA-2, and GDAS were analyzed as temperature(0.994, 0.967, 0.975), pressure(0.995, 0.940, 0.942), humidity (0.993, 0.895, 0.915), and rainfall(0.897, 0.682, 0.695), respectively. For the hydrologic output comparisons, the mean of R² was ASOS-S(0.493), MERRA-2(0.56) and GDAS (0.488) in soil moisture, and the mean of R² was analyzed as ASOS-S(0.473), MERRA-2(0.43) and GDAS(0.615) in evapotranspiration. MERRA-2 and GDAS are quality-controlled data sets using multiple satellite and ground observation data, whereas ASOS-S is grid data using observation data from 103 points. Therefore, it is concluded that the accuracy is lowered due to the error from the distance difference between the observation data. If the more ASOS observation are secured and applied in the future, the less error due to the gridding will be expected with the increased accuracy.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.330-330
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• 2022

우리나라에서 발생하는 기상 재해 현상은 주로 태풍, 집중호우, 장마 등 인명 및 경제적인 피해가 크며, 단기간에 국지적으로 나타난다. 현재 재해 감시 및 예보는 주로 종관기상관측체계를 이용하고 있다. 하지만, 우리나라의 복잡한 지형, 인구 밀집 지형, 관측 시기가 일정하지 않은 지형과 같은 조건에서 미계측 자료 및 지역이 다수 존재 때문에 강수의 공간 분포와 강도에 대한 정밀한 정보를 제공하지 못하는 실정이다. 최근 광범위한 관측영역과 공간 분해능의 개선, 자료추출 알고리즘의 개발로 전세계적으로 위성영상 기반 기상관측 자료의 활용성이 증대되고 있다. 본 연구에서는 한반도 지역의 지상 관측데이터와 전지구 격자형 위성 강우자료를 비교하여 한반도의 적용성을 분석하고자 한다. 다양한 위성영상 기반 기상자료인 Climate Hazards Groups InfraRed Precipitation with Station (CHIRPS), Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Global Precipitation Climatology Centre (GPCC), Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) 4개의 강우위성영상을 수집하여, 1991년부터 2020년까지 30년 데이터를 활용하였다. 강수량 변동성 비교를 위하여 기상청의 종관기상관측장비 (Automated Synoptic Observation System, ASOS), 자동기상관측시설 (Automatic Weather System, AWS) 데이터와 상관 분석을 수행하고, 강우위성영상의 국내 적합성을 판단하고자 한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.134-134
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• 2021

Precipitation is a crucial component of water cycle and play a key role in hydrological processes. Traditionally, gauge-based precipitation is the main method to achieve high accuracy of rainfall estimation, but its distribution is sparsely in mountainous areas. Recently, satellite-based precipitation products (SPPs) provide grid-based precipitation with spatio-temporal variability, but SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution quite coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation using Automatic weather system (AWS) in Korea and multiple SPPs(i.e. CHIRPSv2, CMORPH, GSMaP, TRMMv7) during the period of 2003-2017. And this study used a machine learning based Random Forest (RF) model for generating new merging precipitation. In addition, several statistical linear merging methods are used to compare with the results of the RF model. In order to investigate the efficiency of RF, observed data from 64 observed Automated Synoptic Observation System (ASOS) were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the random forest model showed higher accuracy than each satellite rainfall product and spatio-temporal variability was better reflected than other statistical merging methods. Therefore, a random forest-based ensemble satellite precipitation product can be efficiently used for hydrological simulations in ungauged basins such as the Mekong River.

• Journal of the Korean earth science society
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• v.42 no.5
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• pp.514-523
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• 2021

Using the precipitation data observed at the Gochang Standard Weather Observatory (GSWO) during the winter seasons from 2014 to 2016, we analyzed the precipitation characteristics of the winter observation environment. For this study, we used four different types of precipitation gauges, i.e., No Shield (NS), Single Alter (SA), Double Fence Intercomparison Reference (DFIR), and Pit Gauge (PG). We analyzed the data from each to find differences in the accumulated precipitation, characteristics of the precipitation type, and the catch efficiency according to the wind speed based on the DFIR. We then classified these into three precipitation types, i.e., rain, mixed precipitation, and snow, according to temperature data from Gochang's Automated Synoptic Observing System (ASOS). We considered the DFIR to be the standard precipitation gauge for our analysis and the cumulative winter precipitation recorded by each other gauge compared to the DFIR data in the following order (from the most to least similar): SA, NS, and PG. As such, we find that the SA gauge is the most accurate when compared to the standard precipitation gauge used (DFIR), and the PG system is inappropriate for winter observations.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.393-393
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• 2023

최근 기후변화에 대한 관심이 높아짐에 따라 전 세계적으로 미래 기후변화 예측 전망에 대한 다양한 연구들이 수행되었으며, 특히 IPCC(Intergovernmental Panel on Climate Change)의 기후변화 6차 보고서에 채택된 SSP(Shared Socio-economic Pathway) 시나리오를 이용한 연구가 활발한 상황이다. 이들 연구에서는 미래 기후변화의 양상 비교를 위한 기간 구분은 통상적으로 F1(2011-2040), F2(2041-2070), F3(2071-2100)으로 구성된다. 하지만 이는 단순하게 동일한 기간으로 나누어 설정한 것으로, 통계적 근거가 부족할 뿐만 아니라 변화 추이를 확인하기 위한 수단으로 사용하기에 부족할 수 있다는 한계점이 존재한다. 이 연구에서는 기후변화 패턴에 대한 기존 연구의 한계, 특히 미래 기후변화를 비교하기 위해 사용되는 기간 분류와 관련하여 한계점을 보완하고자 한다. SSP 시나리오 모델 중 UKESM1 모델을 활용하여 ASOS(Automated Synoptic Observation System) 기상관측소 기준 59개 지점에서 추출한 강수량 데이터를 분석하였다. 이후, 기후변화 비교를 위한 최적의 분류를 결정하기 위해 장마철인 6월부터 9월까지의 강수 데이터에 대해 통계분석 및 Pettitt 검정을 수행해 최적 기간을 산정하였다. 이를 통해 기존의 F1, F2, F3 분류 방식과 통계분석을 통해 도출한 최적 시기의 유출 특성 분석결과의 변화양상을 비교하였으며, 각 방법에 대한 비교를 통해 기후변화 추이에 대한 이해를 제공할 수 있을 것으로 판단하였다. 결과적으로 이 연구는 기후변화 시나리오를 활용하는 연구 수행 시 기간 구분에 대한 발전된 접근 방식을 제시하고자 한다.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1849-1858
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• 2021

We analyzed the monthly and annual trends of the meteorological factors(wind speeds and directions and air temperatures) measured at an automated synoptic observation system (ASOS) and fine particle (PM₁₀ and PM_2.5) concentrations measured at the air quality monitoring systems(AQMSs) in Suwon. In addition, we investigated how the fine particle concentrations were related to the meteorological factors as well as urban morphological parameters (fractions of building volume and road area). We calculated the total volume of buildings and the total area of the roads in the area of 2 km × 2 km centered at each AQMS using the geographic information system and environmental geographic information system. The analysis of the meteorological factors showed that the dominant wind directions at the ASOS were westerly and northwesterly and that the average wind speed was strong in Spring. The measured fine particle concentrations were low in Summer and early Autumn (July to September) and high in Spring and Winter. In 2020, the annual mean fine particle concentration was lowest at most AQMSs. The fine particle concentrations were negatively and weakly correlated with the measured wind speeds and air temperatures (the correlation between PM_2.5 concentrations and air temperatures was relatively strong). In Suwon city, at least for 6 AQMSs except for the RAQMS 131116 and AQMS 131118, the PM₁₀ concentrations were affected mainly by the transport from outside rather than primary emission from mobile sources or wind speed decrease caused by buildings and, in the case of PM_2.5, vise versa.