• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.307-319
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• 2016

A Land-Atmosphere Modeling Package (LAMP) for supporting agricultural and forest management was developed at the National Center for AgroMeteorology (NCAM). The package is comprised of two components; one is the Weather Research and Forecasting modeling system (WRF) coupled with Noah-Multiparameterization options (Noah-MP) Land Surface Model (LSM) and the other is an offline one-dimensional LSM. The objective of this paper is to briefly describe the two components of the NCAM-LAMP and to evaluate their initial performance. The coupled WRF/Noah-MP system is configured with a parent domain over East Asia and three nested domains with a finest horizontal grid size of 810 m. The innermost domain covers two Gwangneung deciduous and coniferous KoFlux sites (GDK and GCK). The model is integrated for about 8 days with the initial and boundary conditions taken from the National Centers for Environmental Prediction (NCEP) Final Analysis (FNL) data. The verification variables are 2-m air temperature, 10-m wind, 2-m humidity, and surface precipitation for the WRF/Noah-MP coupled system. Skill scores are calculated for each domain and two dynamic vegetation options using the difference between the observed data from the Korea Meteorological Administration (KMA) and the simulated data from the WRF/Noah-MP coupled system. The accuracy of precipitation simulation is examined using a contingency table that is made up of the Probability of Detection (POD) and the Equitable Threat Score (ETS). The standalone LSM simulation is conducted for one year with the original settings and is compared with the KoFlux site observation for net radiation, sensible heat flux, latent heat flux, and soil moisture variables. According to results, the innermost domain (810 m resolution) among all domains showed the minimum root mean square error for 2-m air temperature, 10-m wind, and 2-m humidity. Turning on the dynamic vegetation had a tendency of reducing 10-m wind simulation errors in all domains. The first nested domain (7,290 m resolution) showed the highest precipitation score, but showed little advantage compared with using the dynamic vegetation. On the other hand, the offline one-dimensional Noah-MP LSM simulation captured the site observed pattern and magnitude of radiative fluxes and soil moisture, and it left room for further improvement through supplementing the model input of leaf area index and finding a proper combination of model physics.

• The Journal of the Acoustical Society of Korea
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• v.39 no.6
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• pp.606-614
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• 2020

In this paper, we analyze the influences of ship traffic and marine weather information on underwater ambient noise. Ambient noise is an important environmental factor that greatly affects the detection performance of underwater sonar systems. In order to implement an automated system such as prediction of detection performance using artificial intelligence technology, which has been recently studied, it is necessary to obtain and analyze major data related to these. The main sources of ambient noise have various causes. In the case of sonar systems operating in offshore seas, the detection performance is greatly affected by the noise caused by ship traffic and marine weather. Therefore, in this paper, the impact of each data was analyzed using the measurement results of ambient noise obtained in coastal area of the East Sea of Korea, and public data of nearby ship traffic and ocean weather information. As a result, it was observed that the underwater ambient noise was highly correlated with the change of the ship's traffic volume, and that marine environment factors such as wind speed, wave height, and rainfall had an effect on a specific frequency band.

• Journal of the Ergonomics Society of Korea
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• v.36 no.1
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• pp.23-36
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• 2017

Objective: This paper presents a study to evaluate the WBGT index for assessing the effects of a wide range of outdoor weather conditions on human responses. Background: The Wet Bulb Globe Temperature (WBGT) index was firstly developed for the assessment of hot outdoor conditions. It is a recognised index that is used world-wide. It may be useful over a range of outdoor conditions and not just for hot climates. Method: Four group experiments, involving people performing a light stepping activity, were conducted to determine human responses to outside conditions in the U.K. They were conducted in September 2007 (autumn), December 2007 (winter), March 2008 (spring) and June 2008 (summer). Environmental measurements included WBGT, air temperature, radiant temperature (including solar load), humidity and wind speed all measured at 1.2m above the ground, as well as weather data measured by a standard weather station at 3m to 4m above the ground. Participants' physiological and subjective responses were measured. When the overall results of the four seasons are considered, WBGT provided a strong prediction of physiological responses as well as subjective responses if aural temperature, heart rate and sweat production were measured. Results: WBGT is appropriate to predict thermal strain on a large group of ordinary people in moderate conditions. Consideration should be given to include the WBGT index in warning systems for a wide range of weather conditions. However, the WBGT overestimated physiological responses of subjects. In addition, tenfold Borg's RPE was significantly different with heart rate measured for the four conditions except autumn (p<0.05). Physiological and subjective responses over 60 minutes consistently showed a similar tendency in the relationships with the $WBGT_{head}$ and $WBGT_{abdomen}$. Conclusion: It was found that either $WBGT_{head}$ or $WBGT_{abdomen}$ could be measured if a measurement should be conducted at only one height. The relationship between the WBGT values and weather station data was also investigated. There was a significant relationship between WBGT values at the position of a person and weather station data. For UK daytime weather conditions ranging from an average air temperature of $6^{\circ}C$ to $21^{\circ}C$ with mean radiant temperatures of up to $57^{\circ}C$, the WBGT index could be used as a simple thermal index to indicate the effects of weather on people. Application: The result of evaluation of WBGT might help to develop the smart clothing for workers in industrial sites and improve the work environment in terms of considering workers' wellness.

• Journal of Korea Water Resources Association
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• v.43 no.1
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• pp.81-94
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• 2010

Input variable selection is one of the various techniques for improving the performance of artificial neural network. In this study, mutual information is applied for input variable selection technique instead of correlation coefficient that is widely used. Among 152 variables of RDAPS (Regional Data Assimilation and Prediction System) output results, input variables for artificial neural network are chosen by computing mutual information between rainfall records and RDAPS' variables. At first the rainfall forecast variable of RDAPS result, namely APCP, is included as input variable and the other input variables are selected according to the rank of mutual information and correlation coefficient. The input variables using mutual information are usually those variables about wind velocity such as D300, U925, etc. Several statistical error estimates show that the result from mutual information is generally more accurate than those from the previous research and correlation coefficient. In addition, the artificial neural network using input variables computed by mutual information can effectively reduce the relative errors corresponding to the high rainfall events.

• Journal of the Korean earth science society
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• v.35 no.4
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• pp.215-224
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• 2014

Data assimilation effect of mobile rawinsonde observation was evaluated using Unified Model (UM) with a Three-Dimensional Variational (3DVAR) data assimilation system during the intensive observation program of 2013 summer season (rainy season: 20 June-7 July 2013, heavy rain period: 8 July-30 July 2013). The analysis was performed by two sets of simulation experiments: (1) ConTroL experiment (CTL) with observation data provided by Korea Meteorological Administration (KMA) and (2) Observing System Experiment (OSE) including both KMA and mobile rawinsonde observation data. In the model verification during the rainy season, there were no distinctive differences for 500 hPa geopotential height, 850 hPa air temperature, and 300 hPa wind speed between CTL and OSE simulation due to data limitation (0000 and 1200 UTC only) at stationary rawinsonde stations. In contrast, precipitation verification using the hourly accumulated precipitation data of Automatic Synoptic Observation System (ASOS) showed that Equivalent Threat Score (ETS) of the OSE was improved by about 2% compared with that of the CTL. For cases having a positive effect of the OSE simulation, ETS of the OSE showed a significantly higher improvement (up to 41%) than that of the CTL. This estimation thus suggests that the use of mobile rawinsonde observation data using UM 3DVAR could be reasonable enough to assess the improvement of prediction accuracy.

• Journal of the Korea Institute of Building Construction
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• v.19 no.6
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• pp.529-538
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• 2019

Typhoons can cause significant financial damage worldwide. For this reason, states, local governments and insurance companies attempt to quantify and mitigate the financial risks related to these natural disasters by developing a typhoon risk assessment model. As such, the importance of typhoon risk assessment models is increasing, and it is also important to reflect local vulnerabilities to enable sophisticated assessments. Although a practical study of economic losses associated with natural disasters has identified essential risk indicators, comprehensive studies covering the correlation between vulnerability and economic loss are still needed. The purpose of this study is to identify typhoon damage indicators and to develop evaluation indicators for typhoon damage prediction functions, utilizing the loses from Typhoon Maemi as data. This study analyzes actual loss records of Typhoon Maemi provided by local insurance companies to prepare for a scenario of maximum losses. To create a vulnerability function, the authors used the wind speed and distance from the coast and the total value of property, construction type, floors, and underground floor indicators. The results and metrics of this study provide practical guidelines for government agencies and insurance companies in developing vulnerability functions that reflect the actual financial losses and regional vulnerabilities of buildings.

• Atmosphere
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• v.25 no.2
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• pp.221-233
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• 2015

Pollen is closely related to health issues such as allergenic rhinitis and asthma as well as intensifying atopic syndrome. Information on current and future spatio-temporal distribution of allergenic pollen is needed to address such issues. In this study, the Community Multiscale Air Quality Modeling (CMAQ) was utilized as a base modeling system to forecast pollen dispersal from oak trees. Pollen emission is one of the most important parts in the dispersal modeling system. Areal emission factor was determined from gridded areal fraction of oak trees, which was produced by the analysis of the tree type maps (1:5000) obtained from the Korea Forest Service. Daily total pollen production was estimated by a robust multiple regression model of weather conditions and pollen concentration. Hourly emission factor was determined from wind speed and friction velocity. Hourly pollen emission was then calculated by multiplying areal emission factor, daily total pollen production, and hourly emission factor. Forecast data from the KMA UM LDAPS (Korea Meteorological Administration Unified Model Local Data Assimilation and Prediction System) was utilized as input. For the verification of the model, daily observed pollen concentration from 12 sites in Korea during the pollen season of 2014. Although the model showed a tendency of over-estimation in terms of the seasonal and daily mean concentrations, overall concentration was similar to the observation. Comparison at the hourly output showed distinctive delay of the peak hours by the model at the 'Pocheon' site. It was speculated that the constant release of hourly number of pollen in the modeling framework caused the delay.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1275-1288
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• 2015

A microscale weather analysis module (about 1km or less) is a microscale numerical weather prediction model designed for operational forecasting and atmospheric research needs such as radiant energy, thermal energy, and humidity. The accuracy of the module is directly related to the usefulness and quality of real-time microscale weather information service in the metropolitan area. This paper suggests an object based verification method useful for spatio-temporal evaluation of the accuracy of the microscale weather analysis module. The method is a graphical method comprised of three steps that constructs a lattice field of evaluation statistics, merges and identifies objects, and evaluates the accuracy of the module. We develop lattice fields using various evaluation spatio-temporal statistics as well as an efficient object identification algorithm that conducts convolution, masking, and merging operations to the lattice fields. A real data application demonstrates the utility of the verification method.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.4 no.2
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• pp.74-83
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• 2001

The purpose of this study was investigate to the influence of forest roads characteristics and environment factors on the soil erosion, stability and vegetation survival of cut slope in forest roads. The results obtained could be summarized as follows; 1. The correlated factors between slope erosion and variables in cut slope were altitude, convex, degree of slope, length of slope and soil depth. In the stepwise regression analysis, length of slope and soil hardness was a high significant and its regression equation was given by -89.6136 + 15.0667X14 + 16.6713X15($R^2$ = 0.6712). 2. The main factors influencing the stability of cut slope were significant in order of coverage, middle, convex, length of slope and north, and its discriminant equation was given by -1.019 + 0.064X22 - 0.808X8 - 0.622X24 + 0.742X11 - 0.172X14 - 0.545X6 ($R^2$ = 0.793). 3. The centroids value of discriminant function in the stability and unstability estimated to 1.244 and -1.348, respectively. The boundary value between two groups related to slope stability was -0.1038. The prediction rate of discriminant function for stability evaluation of was as high as 91.3%. 4. The dominant species of invasion vegetation on the cut slope consist with Carex humilis, Agropyron tsukushiense var. transiens, Calamagrostis arundinacea, Miscanthus sinensis var. purpurascens, and Ixeris dentata in survey area. The rate of vegetation invasion more increased by time passed. 5. The life form of invasion vegetation in cut slop showed to $H-D_1-R_{2,3}-e$ type of the hemicryptophyte of dormancy form, dissem inated widely by wind and water of dissminule type, moderate extent and narrowest extent of radicoid type, erect form of growth form. 6. The correlated factors between forest enviroment and coverage appeared north, passage years and middle position of slope at 5% level. The forest environment factors influencing the invasion plants in survey area were shown in order to altitude, passage years, rock(none), forest type(mixed) and stone amount. The regression equation was given by 17.5228 - 0.0911X3 + 3.6189X28 15.8493X22 19.8544X25 + 0.3558X26 ($R^2$ = 0.4026).

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.3
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• pp.58-69
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• 2007

The vegetation area that occupies 76% in land surface of the earth can give a considerable impact on water resources, environment and ecological system by future climate change. The purpose of this study is to predict future vegetation cover information from NDVI (Normalized Difference Vegetation Index) extracted from satellite images. Current vegetation information was prepared from monthly NDVI (March to November) extracted from NOAA AVHRR (1994 - 2004) and Terra MODIS (2000 - 2004) satellite images. The NDVI values of MODIS for 5 years were 20% higher than those of NOAA. The interrelation between NDVIs and monthly averaged climate factors (daily mean, maximum and minimum temperature, rainfall, sunshine hour, wind velocity, and relative humidity) for 5 river basins of South Korea showed that the monthly NDVIs had high relationship with monthly averaged temperature. By linear regression, the future NDVIs were estimated using the future mean temperature of CCCma CGCM2 A2 and B2 climate change scenario. The future vegetation information by NOAA NDVI showed little difference in peak value of NDVI, but the peak time was shifted from July to August and maintained high NDVIs to October while the present NDVI decrease from September. The future MODIS NDVIs showed about 5% increase comparing with the present NDVIs from July to August.