• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.4
• /
• pp.415-423
• /
• 2021

Recently, an R-based point time series data validation system has been established for the statistical post processing and improvement of the National Center for AgroMeteorology-Land Atmosphere Modeling Package (NCAM-LAMP) medium-range prediction data. The time series verification system was used to compare the NCAM-LAMP with the AWS observations and GDAPS medium-range prediction model data operated by Korea Meteorological Administration. For this comparison, the model latitude and longitude data closest to the observation station were extracted and a total of nine points were selected. For each point, the characteristics of the model prediction error were obtained by comparing the daily average of the previous prediction data of air temperature, wind speed, and hourly precipitation, and then we tried to improve the next prediction data using Support Vector Machine( SVM) method. For three months from August to October 2017, the SVM method was used to calibrate the predicted time series data for each run. It was found that The SVM-based correction was promising and encouraging for wind speed and precipitation variables than for temperature variable. The correction effect was small in August but considerably increased in September and October. These results indicate that the SVM method can contribute to mitigate the gradual degradation of medium-range predictability as the model boundary data flows into the model interior.

• Asian Journal of Innovation and Policy
• /
• v.5 no.3
• /
• pp.315-329
• /
• 2016

Public R&D investment information has diverse implications for researching R&D trends. Also, as it is important for the establishment of R&D policy to grasp the current situation and trends of R&D to improve science and technology level, science and technology information service system, such as NTIS (National Science & Technology Information Service), is operated at a national level in most countries. However, since the data forms provided by current NTIS are raw data, it is necessary to develop the R&D performance indicator or to use additional scientometric methods by analyzing scientific papers or scientific R&D project information for grasping R&D trends or analyzing R&D task results. Thus, this study applied public R&D investment information to investigate and monitor R&D trends in the field of information & communication technology (ICT) of meteorology and climatology by using NTIS data of Korea and NSF (National Science Foundation) data of USA.

• Journal of Environmental Science International
• /
• v.13 no.2
• /
• pp.135-141
• /
• 2004

Drift data from 17 Argo profiling floats in the East Sea are used to understand the mean flow and its variability in the upper portion of the East Sea Proper Water (UESPW) (around 800 m). The flow penetrates into the Ulleung basin (UB) through two paths: an extension of the southward flowing of the North Korean Cold Water along the east coast of Korea and between Ulleung Island and Dok island. Flows at 800 m are observed in the range of from 0.2 to 4.29 cms-1 and the variability in the north of the UB is larger than that in the south of the UB. In the UB, cyclonic flows from 0.3 to 1.6 cms-1 are observed with the bottom topography. We found that the mean kinetic energy (MKE) and the mean eddy kinetic energy (EKE) are 1.3 and 2.1 cm2s-2 respectively.

• Journal of Environmental Science International
• /
• v.24 no.5
• /
• pp.657-670
• /
• 2015

Visibility and Automatic Weather System(AWS) data near Nakdong river were analyzed to characterize fog formation during 2012-2013. The temperature was lower than its nearby city - Daegu, whereas the humidity was higher than the city. 157 fog events were observed in total during the 2 year period. About 65% of the events occurred in fall (September, October, and November) followed by winter, summer, and spring. 94 early morning fog events of longer than 30 minutes occurred when south westerly wind speed was lower than 2 m/s. During these events, the water temperature was highest followed by soil surface and air temperatures due to the advection of cold and humid air from nearby hill. The observed fog events were categorized using a fog-type classification algorithm, which used surface cooling, wind speed threshold, rate of change of air temperature and dew point temperature. As a result, frontal fog observed 6 times, radiation 4, advection 13, and evaporation 66. The evaporation fog in the study area lasted longer than other reports. It is due to the interactions of cold air drainage flow and warm surface in addition to the evaporation from the water surface. In particular, more than 60% of the evaporation fog events were accompanied with cold air flows over the wet and warm surface. Therefore, it is needed for the identification of the inland fog mechanism to evaluate the impacts of nearby topography and land cover as well as water body.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.18 no.4
• /
• pp.320-326
• /
• 2016

From the perspective of climate-smart agriculture, it is becoming more critical to accurately estimate the amount of greenhouse gas emissions in the agricultural sector. In order to accurately ascertain the methane emissions from rice paddies, which account for a significant portion of the emission from the agricultural sector, we used the data from the 2010 Agriculture, Forestry and Fisheries Census, the revised water management scaling factors and their calculation program. In order to facilitate the analyses and understanding, the results were mapped using the ArcGIS software. The fact that the validation of the mapped values against the actual field measurements at one site showed little difference encourages the necessity to further this study. The administrative districts-based map of methane emission can help clearly identify the regional differences. Furthermore, the analysis of their major controlling factors will provide important scientific basis for the practical policy makings for methane mitigation.

• Journal of Environmental Impact Assessment
• /
• v.20 no.3
• /
• pp.337-348
• /
• 2011

Urban environmental problem became one of major issues during its urbanization processes. Environmental impacts are assessed during recent urban planning and development. Though the environmental impact assessment considers meteorological impact as a minor component, changes in wind environment during development can largely affect the distribution pattern of air temperature, humidity, and pollutants. Impact assessment of local wind is, therefore, a major element for impact assessment prior to any other meteorological impact assessment. Computational Fluid Dynamics (CFD) models are utilized in various fields such as in wind field assessment during a construction of a new building and in post analysis of a fire event over a mountain. CFD models require specially formatted input data and produce specific output files, which can be analyzed using special programs. CFD's huge requirement in computing power is another hurdle in practical use. In this study, a CFD model and related software processors were automated and integrated as a microscale wind environmental impact assessment system. A supercomputer system was used to reduce the running hours of the model. Input data processor ingests development plans in CAD or GIS formatted files and produces input data files for the CFD model. Output data processor produces various analytical graphs upon user requests. The system was used in assessing the impacts of a new building near an observatory on wind fields and showed the changes by the construction visually and quantitatively. The microscale wind assessment system will evolve, of course, incorporating new improvement of the models and processors. Nevertheless the framework suggested here can be utilized as a basic system for the assessment.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.9 no.2
• /
• pp.132-139
• /
• 2007

The Moderate Resolution Imaging Spectroradiometer (MODIS) regularly provides the eight-day gross primary productivity (GPP) at 1 km resolution. In this study, we evaluated the uncertainties of MODIS GPP caused by errors associated with the Data Assimilation Office (DAO) meteorology and a biophysical variable (fraction of absorbed photosynthetically active radiation, FPAR). In order to recalculate the improved GPP estimate, we employed ground weather station data and reconstructed cloud-free FPAR. The official MODIS GPP was evaluated as +17% higher than the improved GPP. The error associated with DAO meteorology was identified as the primary and the error from the cloud-contaminated FPAR as the secondary constituent in the integrative uncertainty. Among various biome types, the highest relative error of the official MODIS GPP to the improved GPP was found in the mixed forest biome with RE of 20% and the smallest errors were shown in crop land cover at 11%. Our results indicated that the uncertainty embedded in the official MODIS GPP product was considerable, indicating that the MODIS GPP needs to be reconstructed with the improved input data of daily surface meteorology and cloud-free FPAR in order to accurately monitor vegetation productivity in Korea.

• The Bulletin of The Korean Astronomical Society
• /
• v.3
• /
• pp.45-51
• /
• 1978

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.19 no.1
• /
• pp.29-37
• /
• 2011

Last April, Europe was severly damaged as all social and economic activities came to a halt prompted by the cancellation of all flights resulting from volcanic ash. This exemplifies that the meteorology conditions have significant influence on the flights of airplanes. Hence, in this research the influence that the meteorology has on the domestic flights and its characteristics will be examined, and the core meteorological factors that influence flights in each airport will be drawn. In order to do this, statistical analysis on the influence that the meteorology has on flights was carried out in order to analyze the data about flight cancelation and delay and also its cause, primarily based on the Gimpo, Gimhae, and the Jeju airports. As a result, first, the meteorological factors which impact flight cancellation and delay were different among the domestic airports, and second, it was analyzed that fog was the main meteorological factor in the Gimpo airport, strong wind in the Jeju airport, and fog in the Gimhae airport. Third, between the day the flights were cancelled and delayed occurred, and the day that weren't, the fact that there existed a difference among the actual meteorological factors was statistically drawn. With the result of such analysis, meteorological factors pertaining to the cancellation and delay of flights must be considered seperately by each airport and specialized meteorological information must be provided accordingly. Further, when selecting the position of an airport that is to be constructed in the future, implications that there is a definite need for the meteorology effect evaluation based on past meteorology data can be drawn.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.24 no.4
• /
• pp.337-345
• /
• 2022

For data such as rainfall and soil moisture, it is important to obtain the values of all points required as geostatistical data. Spatial interpolation is generally performed in this process, and commercial software such as ArcGIS is often used. However, commercial software has fatal drawbacks due to its high expertise and cost. In this study, R, an open source-based environment with ArcGIS, a commercial software, was used to compare the differences according to the processing environment when performing spatial interpolation. The data for spatial interpolation was weather forecast data calculated through Land-Atmosphere Modeling Package (LAMP)-WRF model, and soil moisture data calculated for each cumulative rainfall scenario. There was no difference in the output value in the two environments, but there was a difference in user interface and calculation time. The results of spatial interpolation work in the test bed showed that the average time required for R was 5 hours and 1 minute, and for ArcGIS, the average time required was 4 hours and 40 minutes, respectively, showing a difference of 7.5%. The results of this study are meaningful in that researchers can derive the same results in a commercial software environment and an open source-based environment, and can choose according to the researcher's environment and level.