• Journal of Astronomy and Space Sciences
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• v.24 no.4
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• pp.327-338
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• 2007

We estimate the momentum fluxes of short-period gravity waves which are observed in the OI 557.7 nm nightglow emission with all-sky camera at Mt. Bohyun ($36.2^{\circ}\;N,\;128.9^{\circ}\;E$) in Korea. The intrinsic phase speed ($C_{int}$), the intrinsic period (${\tau}_{int}$), and vertical wavelength (${\lambda}_z$) are also deduced from the horizontal wavelength (${\lambda}_h$), observed period (${\tau}_{ob}$), propagation direction (${\phi}_{ob}$), observe phase speed (${\upsilon}_{ob}$) of the gravity wave on the all-sky images. The neutral winds to deduce intrinsic wave parameters are measured with Fabry-Perot interferometer on Shigaraki ($34.8^{\circ}\;N,\;13.1^{\circ}\;E$) in Japan. We selected 5-nights of observations during the period between July 2002 and December 2006 considering of the weather and instrument conditions in two observation sites. The mean values of intrinsic parameter of gravity waves are $({\tau}_{int})\;=\;12.9\;{\pm}\;6.1\;m/s,\;({\lambda}_z)\;=\;12.9\;{\pm}\;6.5,\;and\;(C_{int})\;=\;40.6\;{\pm}\;11.6\;min$. The mean value of calculated momentum fluxes for four nights besides of ${\lambda}_z\;<\;6\;km$ is $12.0\;{\pm}\;15.2\;m^2/s^2$. It is needed the long-term coherent observation to obtain typical values of momentum fluxes of the mesospheric gravity waves using all-sky camera and the neutral wind measurements.

• Korean Journal of Remote Sensing
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• v.34 no.3
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• pp.481-494
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• 2018

The accurate radiative transfer model simulation is essential for an accurate ozone profile retrieval using optimal estimation from backscattered ultraviolet (BUV) measurement. The input parameters of the radiative transfer model are the main factors that determine the model accuracy. In particular, meteorological parameters such as temperature and surface pressure have a direct effect on simulating radiation spectrum as a component for calculating ozone absorption cross section and Rayleigh scattering. Hence, a sensitivity of UV ozone profile retrievals to these parameters has been investigated using radiative transfer model. The surface pressure shows an average error within 100 hPa in the daily / monthly climatological data based on the numerical weather prediction model, and the calculated ozone retrieval error is less than 0.2 DU for each layer. On the other hand, the temperature shows an error of 1-7K depending on the observation station and altitude for the same daily / monthly climatological data, and the calculated ozone retrieval error is about 4 DU for each layer. These results can help to understand the obtained vertical ozone information from satellite. In addition, they are expected to be used effectively in selecting the meteorological input data and establishing the system design direction in the process of applying the algorithm to satellite operation.

• Journal of Korea Water Resources Association
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• v.42 no.7
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• pp.547-558
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• 2009

General reliability assessment of levees embankment is performed with safety factors for rainfall characteristics and hydrologic and hydraulic parameters, based on the results of deterministic analysis. The safety factors are widely employed in the field of engineering handling model parameters and the diversity of material properties, but cannot explain every natural phenomenon. Uncertainty of flood analysis and related parameters by introducing stochastic method rather than deterministic scheme will be required to deal with extreme weather and unprecedented flood due to recent climate change. As a consequence, stochastic-method-based measures considering parameter uncertainty and related factors are being established. In this study, a variety of dimensionless cumulative rainfall curve for typhoon and monsoon season of July to September with generation method of stochastic temporal variation is generated by introducing Monte Carlo method and applied to the risk assessment of levee embankment using reliability index. The result of this study reflecting temporal and regional characteristics of a rainfall can be used for the establishment of flood defence measures, hydraulic structure design and analysis on a watershed.

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.99-106
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• 2008

A synthetic aperture radar (SAR) system is of great interest in many fields of civil and military applications because of all-weather and luminance free imaging capability. SAR image quality parameters such as spatial resolution, peak to sidelobe ratio (PSLR), and integrated sidelobe ratio (ISLR) can be normally estimated by modeling of impulse response function (IRF) which is obtained from various system design parameters such as altitude, operational frequency, PRF, etc. In modeling of IRF, however, background clutter environment surrounding the IRF is generally neglected. In this paper, analysis method for SAR mage quality is proposed in the real background clutter environment. First of all, SAR raw data of a point scatterer is generated based on various system parameters. Secondly, the generated raw data can be focused to ideal IRF by range Doppler algorithm (RDA). Finally, background clutter obtained from image of currently operating SAR system is applied to IRF. In addition, image quality is precisely analyzed by zooming and interpolation method for effective extraction of IRF, and then the effect of proposed methodology is presented with several simulation results under the assumption of estimation error of Doppler rate.

• Korean Chemical Engineering Research
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• v.45 no.2
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• pp.133-142
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• 2007

As the environmental pollution caused by excessive uses of chemical fertilizers and pesticides is aggravated, organic farming using pasture and livestock manure is gaining an increased necessity. The application rate of the organic farming materials to the field is determined as a function of crops and soil types, weather and cultivation surroundings. When livestock manure is used for organic farming materials, the volatilization of ammonia from field-spread animal manure is a major source of atmospheric pollution and leads to a significant reduction in the fertilizer value of the manure. Therefore, an ammonia emission model should be presented to reduce the ammonia emission and to know appropriate application rate of manure. In this study, the ammonia emission rate from field-applied pig manure is predicted using an artificial neural network (ANN) method, where the Michaelis-Menten equation is employed for the ammonia emission rate model. Two model parameters (total loss of ammonia emission rate and time to reach the half of the total emission rate) of the model are predicted using a feedforward-backpropagation ANN on the basis of the ALFAM (Ammonia Loss from Field-applied Animal Manure) database in Europe. The relative importance among 15 input variables influencing ammonia loss is identified using the weight partitioning method. As a result, the ammonia emission is influenced mush by the weather and the manure state.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.2
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• pp.93-98
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• 2016

For accurate precipitation forecasts the choice of weather factors and prediction method is very important. Recently, machine learning has been widely used for forecasting precipitation, and artificial neural network, one of machine learning techniques, showed good performance. In this paper, we suggest a new method for forecasting precipitation using DBN, one of deep learning techniques. DBN has an advantage that initial weights are set by unsupervised learning, so this compensates for the defects of artificial neural networks. We used past precipitation, temperature, and the parameters of the sun and moon's motion as features for forecasting precipitation. The dataset consists of observation data which had been measured for 40 years from AWS in Seoul. Experiments were based on 8-fold cross validation. As a result of estimation, we got probabilities of test dataset, so threshold was used for the decision of precipitation. CSI and Bias were used for indicating the precision of precipitation. Our experimental results showed that DBN performed better than MLP.

• Korean Journal of Food Science and Technology
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• v.17 no.1
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• pp.25-32
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• 1985

Low-temperature in-bin paddy drying has been examined to study the limitations of this drying method under Korean weather conditions, the initial moisture content of the paddy, the bulk depth and the airflow rate. The results are reported and discussed with regard to drying time, energy requirements and costs, uniformity in the moisture content of the dried kernels and, finally, the quality of the paddy. The tests carried out during the paddy-drying period in 1981 and 1982 have shown that under Korean weather conditions paddy can be dried to safe storage conditions by continuous aeration with ambient air. Depending upon the initial moisture content of the kernels(19.2%-25.5% w.b.), the bulk depth(1.1-3.5m) and the airflow $(3.0-6.9m^3\;air/m^3\;paddy/min)$ the paddy could be dried within 5 to 17 days. The energy requirements and energy costs are shown to be considerably lower than for conventional high-temperature drying. No significant changes in the quality in terms of milling yield, cracking ratio, acid value and germination were observed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.5
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• pp.469-479
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• 2014

N-RTK(Network based RTK) methods are able to improve the accuracy of GNSS positioning results through modelling of the distance-dependent error sources(i.e. primarily the ionospheric and tropospheric delays and orbit errors). In this study, the comparison of the TTFF(Time-To-Fix-First ambiguity), accuracy and discrepancies in horizontal/vertical components of N-RTK methods(VRS and FKP) with the static GNSS at 20 Unified Control Stations covering Incheon metropolitan city area during solar storms(Solar cycle 24 period) were performed. The results showed that the best method, compared with the statics GNSS survey, is the VRS, followed by the FKP, but vertical components of both VRS and FKP were approximately two times bigger than horizontal components. The reason for this is considered as the ionospheric scintillation because of irregularities in electron density, and the tropospheric scintillation because of fluctuations on the refractive index take the place. When the TTFF at each station for each technique used, VRS gave shorter initialization time than FKP. The possible reasons for this result might be the inherent differences in principles, errors in characteristics of different correction networks, interpolating errors of FKP parameters according to the non-linear variation of the dispersive and non-dispersive errors at rover when considering both domestic mobile communication infra and the standardized high-compact data format for N-RTK. Also, those test results revealed degradation of positing accuracy, long initialization time, and sudden re-initialization, but more failures to resolve ambiguity during space weather events caused by Sunspot activity and solar flares.

• Korean Journal of Environmental Agriculture
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• v.14 no.1
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• pp.7-14
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• 1995

This research was conducted to apply the QUAL2E model to be adopted to the Nam river under current water quality conditions. The survey area of total 60 Km was divided into five reaches. Each reach was then subdivided into the uniform computational elements of 1.5 Km. Based on the stream characteristics, nine sampling stations consisting of six at main streams and three at tributaries were selected. The field data were obtained from the selected stations twice during October of 1991 and May of 1992, which represented the cold weather and low flow, also the warm weather and low flow conditions, respectively. As the results of sensitivity analysis of the model, the important parameters were the rates of BOD decay, Org-N oxidation, $NH_3-N$ oxidation, Org-P decay. The calibrated and verified results by QUAL2E model were correlation coefficient of $0.45{\sim}0.94$. The results displayed a good agreement between the variables of the field measurements and the model simulations, indicating a potential use of the QUAL2E model for the water quality assessment in the Nam River.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.1
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• pp.40-49
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• 2013

Current service system of the Korea Meteorological Administration (KMA) for blooming date forecasting in spring depends on regression equations derived from long term observations in both temperature and phenology at a given station. This regression based system does not allow a timely correction or update of forecasts that are highly sensitive to fluctuating weather conditions. Furthermore, the system cannot afford plant responses to climate extremes which were not observed before. Most of all, this method may not be applicable to locations other than that which the regression equations were derived from. This note suggests a way to replace the location restricted regression equations with a thermal time based phenology model to complement the KMA blooming forecast system. Necessary parameters such as reference temperature, chilling requirement and heating requirement were derived from phenology data for forsythia, azaleas and Japanese cherry at 29 KMA stations for the 1951-1980 period to optimize spring phenology prediction model for each species. Best fit models for each species were used to predict blooming dates and the results were compared with the observed dates to produce a correction grid across the whole nation. The models were driven by the KMA's daily temperature data at a 5km grid spacing and subsequently adjusted by the correction grid to produce the blooming date maps. Validation with the 1971-2012 period data showed the RMSE of 2-3 days for Japanese cherry, showing a feasibility of operational service; whereas higher RMSE values were observed with forsythia and azaleas.