• Electronics and Telecommunications Trends
• /
• v.29 no.6
• /
• pp.102-112
• /
• 2014

최근 빌링(billing, 과금), 벤치마킹, 확장성(scalability), 통계적 목적을 위해 오픈스택 클라우드의 개별 컴포넌트를 모니터링하고 메터링하는 텔레메터링 서비스가 Ceilometer라는 코드명으로 정식 프로젝트로 추가되었다. 초기의 빌링만을 위해 필수 요소만 모니터링하는 것에서, 상태를 감시하여 클라우드 자원의 오토스케일링 등의 오케스트레이션 기능을 위한 다목적성으로 발전하고 있다. 특히 이것은 빅데이터 등의 데이터 분석에 있어서 중요한 힌트를 제공해 준다. 본고는 소스분석을 통한 Ceilometer의 데이터 수집 구조, Ceilometer 모니터링의 핵심 키워드, 비정형 데이터 DB인 MongoDB, 외부인터페이스로써 API(Application Interface) 혹은 CLI(Command Line Interface) 명령어를 소개하고자 한다. 결론에서는 ceilometer의 전반적 구조에 대한 나름대로의 평가를 기술하였다.

• Journal of the Korea Computer Graphics Society
• /
• v.24 no.2
• /
• pp.21-28
• /
• 2018

We present interactive methods for visualizing the cloud height data and the backscatter data collected from ceilometers in the three-dimensional virtual space. Because ceilometer data is high-dimensional, large-size data associated with both spatial and temporal information, it is highly improbable to exhibit the whole aspects of ceilometer data simply with static, two-dimensional images. Based on the three-dimensional rendering technology, our visualization methods allow the user to observe both the global variations and the local features of the three-dimensional representations of ceilometer data from various angles by interactively manipulating the timing and the view as desired. The cloud height data, coupled with the terrain data, is visualized as a realistic cloud animation in which many clouds are formed and dissipated over the terrain. The backscatter data is visualized as a three-dimensional terrain which effectively represents how the amount of backscatter changes according to the time and the altitude. Our system facilitates the multivariate analysis of ceilometer data by enabling the user to select the date to be examined, the level-of-detail of the terrain, and the additional data such as the planetary boundary layer height. We demonstrate the usefulness of our methods through various experiments with real ceilometer data collected from 93 sites scattered over the country.

• Journal of Environmental Science International
• /
• v.31 no.1
• /
• pp.61-76
• /
• 2022

In this study, surface particulate matter (PM_2.5) concentrations were calculated based on empirical equations using measurements of ceilometer backscatter intensities and meteorological variables taken over 19 months. To quantify the importance of meteorological conditions on the calculations of surface PM_2.5 concentrations, eight different meteorological conditions were considered. For each meteorological condition, the optimal upper limit height for an integration of ceilometer backscatter intensity and coefficients for the empirical equations were determined using cross-validation processes with and without considering meteorological variables. The results showed that the optimal upper limit heights and coefficients depended heavily on the meteorological conditions, which, in turn, exhibited extensive impacts on the estimated surface PM_2.5 concentrations. A comparison with the measurements of surface PM_2.5 concentrations showed that the calculated surface PM_2.5 concentrations exhibited better results (i.e., higher correlation coefficient and lower root mean square error) when considering meteorological variables for all eight meteorological conditions. Furthermore, applying optimal upper limit heights for different weather conditions revealed better results compared with a constant upper limit height (e.g., 150 m) that was used in previous studies. The impacts of vertical distributions of ceilometer backscatter intensities on the calculations of surface PM_2.5 concentrations were also examined.

• Journal of the Korean earth science society
• /
• v.39 no.3
• /
• pp.228-240
• /
• 2018

Solar energy is calculated using meteorological (14 station), ceilometer (2 station) and microwave radiometer (MWR, 7 station)) data observed from the Weather Information Service Engine (WISE) on the Seoul metropolitan area. The cloud optical thickness and the cloud fraction are calculated using the back-scattering coefficient (BSC) of the ceilometer and liquid water path of the MWR. The solar energy on the surface is calculated using solar radiation model with cloud fraction from the ceilometer and the MWR. The estimated solar energy is underestimated compared to observations both at Jungnang and Gwanghwamun stations. In linear regression analysis, the slope is less than 0.8 and the bias is negative which is less than $-20W/m^2$. The estimated solar energy using MWR is more improved (i.e., deterministic coefficient (average $R^2=0.8$) and Root Mean Square Error (average $RMSE=110W/m^2$)) than when using ceilometer. The monthly cloud fraction and solar energy calculated by ceilometer is greater than 0.09 and lower than $50W/m^2$ compared to MWR. While there is a difference depending on the locations, RMSE of estimated solar radiation is large over $50W/m^2$ in July and September compared to other months. As a result, the estimation of a daily accumulated solar radiation shows the highest correlation at Gwanghwamun ($R^2=0.80$, RMSE=2.87 MJ/day) station and the lowest correlation at Gooro ($R^2=0.63$, RMSE=4.77 MJ/day) station.

• Atmosphere
• /
• v.29 no.5
• /
• pp.639-658
• /
• 2019

The classification of fog type and the characteristics of fog based on fog events over South Korea were investigated using a 3-year (2015~2017) visibility meter data. One-minute visibility meter data were used to identify fog with present weather codes and surface observation data. The concept of fog events was adopted for the better definition of fog properties and more objective classification through the detailed investigation of life cycle of fog. Decision tree method was used to classify the fog types and the final fog types were radiation fog, advection fog, precipitation fog, cloud base lowering fog and morning evaporation fog. We enhanced objectivity in classifying the types of fog by adding the satellite and the buoy observations to the conventional usage of AWS and ceilometer data. Radiation fog, the most common type in South Korea, frequently occurs in inland during autumn. A considerable number of advection fogs occur in island area in summer, especially in July. Precipitation fog accounts for more than a quarter of the total fog events and frequently occurs in islands and coastal areas. Cloud base lowering fog, classified using ceilometer, occurs occasionally for all areas but the occurrence rate is relatively high in east and west coastal area. Morning evaporation fog type is rarely observed in inland. The occurrence rate of thick fog with visibility less than 100 meters is amount to 21% of total fog events. Although advection fog develops into thick fog frequently, radiation fog shows the minimum visibility, in some cases.

• Korean Journal of Remote Sensing
• /
• v.34 no.2_1
• /
• pp.237-248
• /
• 2018

The vertical distribution of hydrometeor before precipitation near the cloud base has been analyzed using a scanning lidar, rawinsonde data, and Cloud-Resolving Storm Simulator (CReSS). This study mostly focuses on 13 Desember 2016 only. The typical synoptic pattern of lake-effect snowstorm induced easterly in the Yeongdong region. Clouds generated due to high temperature difference between 850 hPa and sea surface (SST) penentrated in the Yeongdong region along with northerly and northeasterly, which eventually resulted precipitation. The cloud base height before the precipitation changed from 750 m to 1,280 m, which was in agreement with that from ceilometer at Sokcho. However, ceilometer tended to detect the cloud base 50 m ~ 100 m below strong signal of lidar backscattering coefficient. As a result, the depolarization ratio increased vertically while the backscattering coefficient decreased about 1,010 m~1,200 m above the ground. Lidar signal might be interpreted to be attenuated with the penetration depth of the cloud layer with of nonspherical hydrometeor (snow, ice cloud). An increase in backscattering signal and a decrease in depolarization ratio occured in the layer of 800 to 1,010 m, probably being associated with an increase in non-spherical particles. There seemed to be a shallow liquid layer with a low depolarization ratio (<0.1) in the layer of 850~900 m. As the altitude increases in the 680 m~850 m, the backscattering coefficient and depolarization ratio increase at the same time. In this range of height, the maximum value (0.6) is displayed. Such a result can be inferred that the nonspherical hydrometeor are distributed by a low density. At this time, the depolarization ratio and the backscattering coefficient did not increase under observed melting layer of 680 m. The lidar has a disadvantage that it is difficult for its beam to penetrate deep into clouds due to attenuation problem. However it is promising to distinguish hydrometeor morphology by utilizing the depolarization ratio and the backscattering coefficient, since its vertical high resolution (2.5 m) enable us to analyze detailed cloud microphysics. It would contribute to understanding cloud microphysics of cold clouds and snowfall when remote sensings including lidar, radar, and in-situ measurements could be timely utilized altogether.

• Journal of the Korean earth science society
• /
• v.42 no.5
• /
• pp.504-513
• /
• 2021

Different physical processes, according to the atmospheric boundary layer types, were used in the Local Data Assimilation and Prediction System (LDAPS) of the Unified Model (UM) used by the Korea Meteorological Administration (KMA). Therefore, it is important to verify the atmospheric boundary layer types in the numerical model to improve the accuracy of the models performance. In this study, the atmospheric boundary layer types were verified using observational data. To classify the atmospheric boundary layer types, summer intensive observation data from radiosonde, flux observation instruments, Doppler wind Light Detection and Ranging(LIDAR) and ceilometer were used. A total number of 201 observation data points were analyzed over the course 61 days from June 18 to August 17, 2019. The most frequent types of differences between LDAPS and observed data were type 1 in LDAPS and type 2 in observed(each 53 times). And type 3 difference was observed in LDAPS and type 5 and 6 were observed 24 and 15 times, respectively. It was because of the simulation performance of the Cloud Physics such as that associated with the simulation of decoupled stratocumulus and cumulus cloud. Therefore, to improve the numerical model, cloud physics aspects should be considered in the atmospheric boundary layer type classification.