• Water Engineering Research
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• v.5 no.1
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• pp.47-54
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• 2004

Several computer programs have been developed to make stochastically generated weather data from observed daily data. But they require fully dataset to run WGEN. Mostly, meterological data frequently have sporadic missing data as well as totally missing data. The modified WGEN has data filling algorithm for incomplete meterological datasets. Any other WGEN models have not the function of data filling. Modified WGEN with data filling algorithm is processing from the equation of Matalas for first order autoregressive process on a multi dimensional state with known cross and auto correlations among state variables. The parameters of the equation of Matalas are derived from existing dataset and derived parameters are adopted to fill data. In case of WGEN (Richardson and Wright, 1984), it is one of most widely used weather generators. But it has to be modified and added. It uses an exponential distribution to generate precipitation amounts. An exponential distribution is easier to describe the distribution of precipitation amounts. But precipitation data with using exponential distribution has not been expressed well. In this paper, generated precipitation data from WGEN and Modified WGEN were compared with corresponding measured data as statistic parameters. The modified WGEN adopted a formula of CLIGEN for WEPP (Water Erosion Prediction Project) in USDA in 1985. In this paper, the result of other parameters except precipitation is not introduced. It will be introduced through study of verification and review soon

• Water for future
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• v.28 no.6
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• pp.193-202
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• 1995

The subjective research attempts to apply a rainfall-runoff model capable of considering time-variation of soil water contents which are highly correlated to the river flows on the qpqyungchang river basin and to evaluate its performance for flow forecasting. The model used in this study is a physically-based conceptual time-continuous model, which is composed of the Sacramento soil moisture accounting model and the nonlinear multiple conceptual reservoirs model. The daily precipitation and evaporation data for 7 years and for 3 years were used for the parameter estimation and the model verification, respectively. As a result, the flows including a significant flood event were well simulated, and the cross-correlation coefficient between observed flows and computed flows for the verification periods was 0.87, but in general computed flows were underestimated for the low-flow periods. Also, the effects of precipitation and soil water content to the river flows were analysed for the flood and the drought.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.24 no.7
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• pp.599-604
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• 2011

A highly strained nanostructure comprising crystallographically aligned HgTe nanoinclusions and a surrounding PbTe matrix has been synthesized using a precipitation process of supersaturated HgTe-PbTe alloys. From the early precipitation stage, HgTe nanoinclusions take disk shape, which is transformed from initial HgTe nuclei, although there is no lattice constant difference of the two end components at standard state. As a primary reason for the morphological transformation of the initial spherical HgTe nuclei to HgTe nanodisks, the induced lattice mismatch is suggested. On the condition that the HgTe nanodisks maintain perfect coherent nature with PbTe matrix, the stress-free lattice constant of constrained HgTe nanodisks has been calculated based on the defined concept of the strain-induced tetragonality, the linear elasticity and the actual measurement in HRTEM images.

• Atmosphere
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• v.17 no.4
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• pp.435-453
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• 2007

KEOP (Korea Enhanced Observing Period)-2004 intensive summer observation was carried out from 20 June to 5 July 2004 over the Southwestern part of the Korean peninsula. In this study, the effects of KEOP-2004 intensive observation data on the simulation of precipitation system are investigated using KLAPS (Korea Local Analysis and Prediction System) and PSU/NCAR MM5. Three precipitation cases during the intensive observation are selected for detailed analysis. In addition to the control experiments using the traditional data for its initial and boundary conditions, two sensitivity experiments using KEOP data with and without Jindo radar are performed. Although it is hard to find a clear and consistent improvement in the verification score (threat score), it is found that the KEOP data play a role in improving the position and intensity of the simulated precipitation system. The experiments started at 00 and 12 UTC show more positive effect than those of 06 and 18 UTC. The effect of Jindo radar is dependent on the case. It plays a significant role in the heavy rain cases related to a mesoscale low over Changma front and the landing of a Typhoon. KEOP data produce more strong difference in the 06/18 UTC experiments than in 00/12 UTC, but give more positive effects in 00/12 UTC experiments. One of the possible explanations for this is that : KEOP data could properly correct the atmosphere around them when there are certain amounts of data, while gives excessive effect to the atmospheric field when there are few data. CRA analysis supports this reasoning. According to the CRA (Contiguous Rain Area) analysis, KEOP data in 00/12 UTC experiments improve only the surrounding area, resulting in essentially same precipitation system so the effects remain only in each convective cell rather than the system itself. On the other hand, KEOP data modify the precipitation system itself in 06/18 UTC experiments. Therefore the effects become amplified with time integration.

• Journal of Wetlands Research
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• v.7 no.3
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• pp.41-48
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• 2005

Generally the estimation of effective rainfall is important in the rainfall-runoff analysis. So, we must pay attention to selecting more accurate effective rainfall estimation method. Although there are many effective rainfall estimation methods, the NRCS method is widely used for the estimation of effective rainfall in the ungaged basin. However, the NRCS method was developed based on the characteristics of the river basin in USA. So, it may have problems to use the NRSC method in Korea without its verification. In the NRCS method, the antecedent precipitation of 5-day is usually used for the estimation of effective rainfall. The main purpose of this study is to investigate the suitable antecedent precipitation day in Korea river basin through the case study. This study performs the rainfall-runoff simulation for the Tanbu river basin by HEC-HMS model under the condition of varying the antecedent precipitation day from 1-day to 7-day and performs goodness of fit test by Monte Carlo simulation method. The antecedent precipitation of 2-day shows the most preferable result in the analysis. This result indicates that the NRCS method should be applied with caution according to the characteristics of the river basin.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2008.11a
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• pp.434-437
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• 2008

In this study, we developed a fuel moisture variation prediction model on each day after precipitation during a spring forest fire exhibition period. For this research, we selected plots in pine forest on Sam-Chuck si and Dong-hae si in Kangwon do according to a forest density(low, mediate, high) and classified a surface woody fuel by a diameter.(below 0.6cm, $0.6{\sim}3cm$, $3{\sim}6cm$, and above 6cm). A validity of this model was verified by applying a fuel moisture variation after precipitation in this spring. In the result, $R^2$ was $0.76{\sim}0.92$. This model will be a useful for improvement of a forest fire danger rate forcast through a prediction a fule moisture in forest.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.7
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• pp.730-735
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• 2014

This paper introduces a GP (Genetic Programming) based robust technique for the prediction of a heavy rain newsflash. The nature of prediction for precipitation is very complex, irregular and highly fluctuating. Especially, the prediction of heavy precipitation is very difficult. Because not only it depends on various elements, such as location, season, time and geographical features, but also the case data is rare. In order to provide a robust model for precipitation prediction, a nonlinear and symbolic regression method using GP is suggested. The remaining part of the study is to evaluate the performance of prediction for a heavy rain newsflash using a GP based nonlinear regression technique in Korean regions. Analysis of the feature selection is executed and various fitness functions are proposed to improve performances. The KLAPS data of 2006-2010 is used for training and the data of 2011 is adopted for verification.

• 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 Korea Water Resources Association
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• v.53 no.11
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• pp.999-1013
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• 2020

Weather modification research has been actively performed worldwide, but a technology that can more quantitatively prove the research effects are needed. In this study, the seeding effect, the efficiency of precipitation enhancement in weather modification experiment, was verified using the radar data. Also, the effects of seeding material on hydrometeor change was analyzed. For this, radar data, weather conditions, and numerical simulation data for diffusion were applied. First, a method to analyze the seeding effect in three steps was proposed: before seeding, during seeding, and after seeding. The proposed method was applied to three cases of weather modification experiments conducted in Gangwon-do and the West Sea regions. As a result, when there is no natural precipitation, the radar reflectivity detected in the area where precipitation change is expected was determined as the seeding effect. When natural precipitation occurs, the seeding effect was determined by excluding the effect of natural precipitation from the maximum reflectivity detected. For the application results, it was found that the precipitation intensity increased by 0.1 mm/h through the seeding effect. In addition, it was confirmed that ice crystals, supercooled water droplets, and mixed-phase precipitation were distributed in the seeding cloud. The results of these weather modification research can be used to secure water resources as well as for future study of cloud physics.

• Journal of the Korean earth science society
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• v.43 no.3
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• pp.367-385
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• 2022

This study evaluates the temperature and precipitation results in East Asia simulated from the Hadley Centre Global Environmental Model version 3 regional climate model (HadGEM3-RA) developed by the UK Met Office. The HadGEM3-RA is conducted in the Coordinated Regional climate Downscaling Experiment-East Asia (CORDEX-EA) Phase II domain for 15 year (2000-2014). The spatial distribution of rainbands produced from the HadGEM3-RA by the summer monsoon is in good agreement with the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of water resources (APRODITE) data over the East Asia. But, precipitation amount is overestimated in Southeast Asia and underestimated over the Korean Peninsula. In particular, the simulated summer rainfall and APRODITE data show the least correlation coefficient and the maximum value of root mean square error in South Korea. Prediction of temperature in Southeast Asia shows underestimation with a maximum error during winter season, while it appears the largest underestimation in South Korea during spring season. In order to evaluate local predictability, the time series of temperature and precipitation compared to the ASOS data of the Seoul Meteorological Station is similar to the spatial average verification results in which the summer precipitation and winter temperature underestimate. Especially, the underestimation of the rainfall increases when the amounts of precipitation increase in summer. The winter temperature tends to underestimate at low temperature, while it overestimates at high temperature. The results of the extreme climate index comparison show that heat wave is overestimated and heavy rainfall is underestimated. The HadGEM3-RA simulated with a horizontal resolution of 25 km shows limitations in the prediction of mesoscale convective system and topographic precipitation. This study indicates that improvement of initial data, horizontal resolution, and physical process are necessary to improve predictability of regional climate model.