• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.59.5-60
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• 2017

We present the relationship between vector magnetic field parameters and solar major flare occurrence rate. Based on this, we are developing a forecast model of major flare (M and X-class) occurrence rate within a day using hourly vector magnetic field data of Space-weather HMI Active Region Patch (SHARP) from May 2010 to April 2017. In order to reduce the projection effect, we use SHARP data whose longitudes are within ${\pm}60$ degrees. We consider six SHARP magnetic parameters (the total unsigned current helicity, the total photospheric magnetic free energy density, the total unsigned vertical current, the absolute value of the net current helicity, the sum of the net current emanating from each polarity, and the total unsigned magnetic flux) with high F-scores as useful predictors of flaring activity from Bobra and Couvidat (2015). We have considered two cases. In case 1, we have divided the data into two sets separated in chronological order. 75% of the data before a given day are used for setting up a flare model and 25% of the data after that day are used for test. In case 2, the data are divided into two sets every year in order to reduce the solar cycle (SC) phase effect. All magnetic parameters are divided into 100 groups to estimate the corresponding flare occurrence rates. The flare identification is determined by using LMSAL flare locations, giving more numbers of flares than the NGDC flare list. Major results are as follows. First, major flare occurrence rates are well correlated with six magnetic parameters. Second, the occurrence rate ranges from 0.001 to 1 for M and X-class flares. Third, the logarithmic values of flaring rates are well approximated by two linear equations with different slopes: steeper one at lower values and lower one at higher values. Fourth, the sum of the net current emanating from each polarity gives the minimum RMS error between observed flare rates and predicted ones. Fifth, the RMS error for case 2, which is taken to reduce SC phase effect, are smaller than those for case 1.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.1
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• pp.27-35
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• 2017

This research was to elucidate the generation method of daily precipitation data from monthly data. We applied a combined method of Markov chain and gamma distribution function using 4 specific parameters of ${\alpha}$, ${\beta}$, p(W/W) and p(W/D) for generation of daily rainfall data using daily precipitation data for the past 30 years which were collected from the country's 23 meteorological offices. Four parameters, applied to use for the combination method, were calculated by maximum likelihood method in location of 23 sites. There are high correlations of 0.99, 0.98 and 0.98 in rainfall days, rainfall probability and mean amount of daily rainfall between measured and simulated data in case of those parameters. In case of using parameters estimated from monthly precipitation, correlation coefficients in rainfall days, rainfall probability and mean amount of daily rainfall are 0.84, 0.83 and 0.96, respectively. We concluded that a combination method with parameter estimation from monthly precipitation data can be applied, in practical purpose such as assessment of climate change in agriculture and water resources, to get daily precipitation data in Korea.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.E2
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• pp.63-73
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• 2008

In this study, neural network models (NNMs) were examined as alternatives to dispersion models in predicting the short-term $SO_2$ concentrations in a coastal area because the performances of dispersion models in coastal areas have been found to be unsatisfactory. The NNMs were constructed for various combinations of averaging time and prediction time in advance by using the historical data of meteorological parameters and $SO_2$ concentrations in 2002 in the coastal area of Boryeung, Korea. The NNMs were able to make much more accurate predictions of 1 hr $SO_2$ concentrations at ground level in the morning in coastal area than the atmospheric dispersion models such as fumigation models, ADMS3 and ISCST3 for identical conditions of atmospheric stability, area, and weather. Even when predictions of 24-h $SO_2$ concentrations were made 24 hours in advance, the predictions and measurements were in good accordance(correlation coefficient=0.65 for n=216). This accordance level could be improved by appropriate expansion of training parameters. Thus it may be concluded that the NNMs can be successfully used to predict short-term ground level concentrations averaged over time less than 24 hours even in complex terrain. The prediction performance of ANN models tends to improve as the prediction lagging time approaches the concentration averaging time, but to become worse as the lagging time departs from the averaging time.

• Ocean Science Journal
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• v.41 no.4
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• pp.245-254
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• 2006

Long term wave climate of both extreme wave and operational wave height is essential for planning and designing coastal structures. Since the field wave data for the waters around Korean peninsula is not enough to provide reliable wave statistics, the wave climate information has been generated by means of long-term wave hindcasting using available meteorological data. Basic data base of hindcasted wave parameters such as significant wave height, peak period and direction has been established continuously for the period of 25 years starting from 1979 and for major 106 typhoons for the past 53 years since 1951 for each grid point of the North East Asia Regional Seas with grid size of 18 km. Wind field reanalyzed by European Center for Midrange Weather Forecasts (ECMWF) was used for the simulation of waves for the extra-tropical storms, while wind field calculated by typhoon wind model with typhoon parameters carefully analyzed using most of the available data was used for the simulation of typhoon waves. Design wave heights for the return period of 10, 20, 30, 50 and 100 years for 16 directions at each grid point have been estimated by means of extreme wave analysis using the wave simulation data. As in conventional methodsi of design criteria estimation, it is assumed that the climate is stationary and the statistics and extreme analysis using the long-term hindcasting data are used in the statistical prediction for the future. The method of extreme statistical analysis in handling the extreme vents like typhoon Maemi in 2003 was evaluated for more stable results of design wave height estimation for the return periods of 30-50 years for the cost effective construction of coastal structures.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.87-90
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• 2005

The FORMOSAT-3/COSMIC mission is a micro satellite mission to deploy a constellation of six micro satellites at low Earth orbits. The final mission orbit is of an altitude of 750-800 lan. It is a collaborative Taiwan-USA science experiment. Each satellite consists of three science payloads in which the GPS occultation experiment (GOX) payload will collect the GPS signals for the studies of meteorology, climate, space weather, and geodesy. The GOX onboard FORMOSAT -3 is designed as a GPS receiver with 4 antennas. The fore and aft limb antennas are installed on the front and back sides, respectively, and as well as the two precise orbit determination (POD) antennas. The precise orbit information is needed for both the occultation inversion and geodetic research. However, the instrument associated errors, such as the antenna phase center offset and even the different cable delay due to the geometric configuration of fore- and aft-positions of the POD antennas produce error on the orbit. Thus, the focus of this study is to investigate the impact of POD antenna parameter on the determination of precise satellite orbit. Furthermore, the effect of the accuracy of the determined satellite orbit on the retrieved atmospheric and ionospheric parameters is also examined. The CHAMP data, the FORMOSAT-3 satellite and orbit parameters, the Bernese 5.0 software, and the occultation data processing system are used in this work. The results show that 8 cm error on the POD antenna phase center can result in ~8 cm bias on the determined orbit and subsequently cause 0.2 K deviation on the retrieved atmospheric temperature at altitudes above 10 lan.

• Korean Journal of Agricultural and Forest Meteorology
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• v.7 no.3
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• pp.185-191
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• 2005

An accurate prediction of dormancy release and bud burst in temperate zone fruit trees is indispensable for farmers to plan heating time under partially controlled environments as well as to reduce the risk of frost damage in open fields. A thermal time-based two-step phenological model that originated in Italy was applied to two important grapevine cultivars in Korea for predicting bud-burst dates. The model consists of two sequential periods: a rest period described by chilling requirement and a forcing period described by heating requirement. It requires daily maximum and minimum temperature as an input and calculates daily chill units (chill days in negative sign) until a pre-determined chilling requirement for rest release is met. After the projected rest release date, it adds daily heat units (anti-chill days in positive sign) to the chilling requirement. The date when the sum reaches zero isregarded as the bud-burst in the model. Controlled environment experiments using field sampled twigs of 'Campbell Early' and 'Kyoho' cultivars were carried out in the vineyard at the National Horticultural Research Institute (NHRI) in Suwon during 2004-2005 to derive the model parameters: threshold temperature for chilling and chilling requirement for breaking dormancy. The model adjusted with the selected parameters was applied to the 1994-2004 daily temperature data obtained from the automated weather station in the NHRI vineyard to estimate bud burst dates of two cultivars and the results were compared with the observed data. The model showed a consistently good performance in predicting the bud burst of 'Campbell Early' and 'Kyoho' cultivars with 2.6 and 2.5 days of root mean squared error, respectively.

• Journal of Biomedical Engineering Research
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• v.26 no.5
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• pp.343-349
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• 2005

Continuously motivating people to exercise regularly is more important than finding a way out of barriers such as lack of time, cost of equipment, lack of nearby facilities, and poor weather. Our proposed system presents practicable methods of motivation through a diverse exercise aid system. The Health Improvement and Management System (all-in-one system which saves space and maintenance costs) measures and evaluates a diverse body shape analysis and physical fitness test and directs users to automated personalized exercise prescription which is prescribed by the expert system of three types of exercise templates: aerobics, anaerobics, and leisure sports. Automated personalized exercise prescriptions are built into XML based documents because the data needs to be in the form of flexible, expansible, and convertible structures in order to process various exercise templates, BIOFIT, a digital exercise trainer, monitors and provides feedback on the physiological parameters while users are working out in the gymnasium. If these parameters do not range within the prescribed target zone, the device will alarm users to control the exercise and make the exercise trainer adjust systemically the proper exercise level. Numeric health information such as the report of the physical fitness test and the exercise prescription makes people stay interested in exercising. In addition, this service can be delivered through the Internet.

• Journal of Korean Society on Water Environment
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• v.32 no.1
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• pp.70-79
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• 2016

Based on the soil moisture data assimilation suggested in the first paper (I), we estimated root zone soil moisture and evaluated drought severity using remotely sensed (RS) data. We tested the impacts of various spatial resolutions on soil moisture variations, and the model outputs showed that resolutions of more than 2-3 km resulted in over-/under-estimation of soil moisture values. Thus, we derived the 2 km resolution-scaled soil moisture dynamics and assessed the drought severity at the study sites (Chungmi-cheon sites 1 and 2) based on the estimated soil/root parameters and weather forcings. The drought indices at the sites were affected mainly by precipitation during the spring season, while both the precipitation and land surface characteristics influence the spatial distribution of drought during the rainy season. Also, the drought severity showed a periodic cycle, but additional research on drought cycles should be conducted using long-term historical data. Our proposed approach enabled estimation of daily root zone soil moisture dynamics and evaluation of drought severity at various spatial scales using MODIS data. Thus, this approach will facilitate efficient management of water resources.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.61.1-61.1
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• 2017

As the application of deep-learning methods has been succeeded in various fields, they have a high potential to be applied to space weather forecasting. Convolutional neural network, one of deep learning methods, is specialized in image recognition. In this study, we apply the AlexNet architecture, which is a winner of Imagenet Large Scale Virtual Recognition Challenge (ILSVRC) 2012, to the forecast of daily solar flare occurrence using the MatConvNet software of MATLAB. Our input images are SOHO/MDI, EIT $195{\AA}$, and $304{\AA}$ from January 1996 to December 2010, and output ones are yes or no of flare occurrence. We consider other input images which consist of last two images and their difference image. We select training dataset from Jan 1996 to Dec 2000 and from Jan 2003 to Dec 2008. Testing dataset is chosen from Jan 2001 to Dec 2002 and from Jan 2009 to Dec 2010 in order to consider the solar cycle effect. In training dataset, we randomly select one fifth of training data for validation dataset to avoid the over-fitting problem. Our model successfully forecasts the flare occurrence with about 0.90 probability of detection (POD) for common flares (C-, M-, and X-class). While POD of major flares (M- and X-class) forecasting is 0.96, false alarm rate (FAR) also scores relatively high(0.60). We also present several statistical parameters such as critical success index (CSI) and true skill statistics (TSS). All statistical parameters do not strongly depend on the number of input data sets. Our model can immediately be applied to automatic forecasting service when image data are available.

• International Union of Geodesy and Geophysics Korean Journal of Geophysical Research
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• v.23 no.1
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• pp.34-38
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• 1995

A diagnostic study on the summertime air mass thunderstorms occurring in the middle region of South Korea was made by analyzing the data of surface and upper air observations as well as the surface and upper level weather charts. The key parameters used in the present study are the amount of precipitable water below 850 hPa level, the vertical profiles of water vapor content and wind, and both the temperature difference and the equivalent potential temperature difference between 850 hPa and 700 hPa levels. It is found from this study that the summertime air mass thunderstorms in the middle region of South Korea can be classified into two distinct types, type I and type II. The thunderstorms of type I occur under the atmospheric conditions of high moisture content, low vertical wind shear in low levels, and conditional instability between 850 hPa and 700 hPa levels. On the other hand, the thunderstorms of type II occur under the atmospheric conditions of less moisture content, higher wind shear and conditional instability. Furthermore, our study suggests that atmospheric instability and the amount of water vapor below 850 hPa level are complementary in the development of air mass thunderstorms. The complementary nature between these two parameters may be an explanation for the thunderstorm development in the areas of low atmospheric water vapor content such as the plains of eastern Colorado.