• Title/Summary/Keyword: predicted meteorological data

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A Simulation Study on Future Climate Change Considering Potential Forest Distribution Change in Landcover (잠재 산림분포 변화를 고려한 토지이용도가 장래 기후변화에 미치는 영향 모사)

  • Kim, Jea-Chul;Lee, Chong Bum;Choi, Sungho
    • Journal of Environmental Impact Assessment
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    • v.21 no.1
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    • pp.105-117
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    • 2012
  • Future climate according to land-use change was simulated by regional climate model. The goal of study was to predict the distribution of meteorological elements using the Weather Research & Forecasting Model (WRF). The KME (Korea Ministry of Environment) medium-category land-use classification was used as dominant vegetation types. Meteorological modeling requires higher and more sophisticated land-use and initialization data. The WRF model simulations with HyTAG land-use indicated certain change in potential vegetation distribution in the future (2086-2088). Compared to the past (1986-1988) distribution, coniferous forest area was decreased in metropolitan and areas with complex terrain. The research shows a possibility to simulate regional climate with high resolution. As a result, the future climate was predicted to $4.5^{\circ}$ which was $0.5^{\circ}$ higher than prediction by Meteorological Administration. To improve future prediction of regional area, regional climate model with HyTAG as well as high resolution initial values such as urban growth and CO2 flux simulation would be desirable.

A Study on the Development of a Simulation Model for Predicting Soil Moisture Content and Scheduling Irrigation (토양수분함량 예측 및 계획관개 모의 모형 개발에 관한 연구(I))

  • 김철회;고재군
    • Magazine of the Korean Society of Agricultural Engineers
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    • v.19 no.1
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    • pp.4279-4295
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    • 1977
  • Two types of model were established in order to product the soil moisture content by which information on irrigation could be obtained. Model-I was to represent the soil moisture depletion and was established based on the concept of water balance in a given soil profile. Model-II was a mathematical model derived from the analysis of soil moisture variation curves which were drawn from the observed data. In establishing the Model-I, the method and procedure to estimate parameters for the determination of the variables such as evapotranspirations, effective rainfalls, and drainage amounts were discussed. Empirical equations representing soil moisture variation curves were derived from the observed data as the Model-II. The procedure for forecasting timing and amounts of irrigation under the given soil moisture content was discussed. The established models were checked by comparing the observed data with those predicted by the model. Obtained results are summarized as follows: 1. As a water balance model of a given soil profile, the soil moisture depletion D, could be represented as the equation(2). 2. Among the various empirical formulae for potential evapotranspiration (Etp), Penman's formula was best fit to the data observed with the evaporation pans and tanks in Suweon area. High degree of positive correlation between Penman's predicted data and observed data with a large evaporation pan was confirmed. and the regression enquation was Y=0.7436X+17.2918, where Y represents evaporation rate from large evaporation pan, in mm/10days, and X represents potential evapotranspiration rate estimated by use of Penman's formula. 3. Evapotranspiration, Et, could be estimated from the potential evapotranspiration, Etp, by introducing the consumptive use coefficient, Kc, which was repre sensed by the following relationship: Kc=Kco$.$Ka+Ks‥‥‥(Eq. 6) where Kco : crop coefficient Ka : coefficient depending on the soil moisture content Ks : correction coefficient a. Crop coefficient. Kco. Crop coefficients of barley, bean, and wheat for each growth stage were found to be dependent on the crop. b. Coefficient depending on the soil moisture content, Ka. The values of Ka for clay loam, sandy loam, and loamy sand revealed a similar tendency to those of Pierce type. c. Correction coefficent, Ks. Following relationships were established to estimate Ks values: Ks=Kc-Kco$.$Ka, where Ks=0 if Kc,=Kco$.$K0$\geq$1.0, otherwise Ks=1-Kco$.$Ka 4. Effective rainfall, Re, was estimated by using following relationships : Re=D, if R-D$\geq$0, otherwise, Re=R 5. The difference between rainfall, R, and the soil moisture depletion D, was taken as drainage amount, Wd. {{{{D= SUM from { {i }=1} to n (Et-Re-I+Wd)}}}} if Wd=0, otherwise, {{{{D= SUM from { {i }=tf} to n (Et-Re-I+Wd)}}}} where tf=2∼3 days. 6. The curves and their corresponding empirical equations for the variation of soil moisture depending on the soil types, soil depths are shown on Fig. 8 (a,b.c,d). The general mathematical model on soil moisture variation depending on seasons, weather, and soil types were as follow: {{{{SMC= SUM ( { C}_{i }Exp( { - lambda }_{i } { t}_{i } )+ { Re}_{i } - { Excess}_{i } )}}}} where SMC : soil moisture content C : constant depending on an initial soil moisture content $\lambda$ : constant depending on season t : time Re : effective rainfall Excess : drainage and excess soil moisture other than drainage. The values of $\lambda$ are shown on Table 1. 7. The timing and amount of irrigation could be predicted by the equation (9-a) and (9-b,c), respectively. 8. Under the given conditions, the model for scheduling irrigation was completed. Fig. 9 show computer flow charts of the model. a. To estimate a potential evapotranspiration, Penman's equation was used if a complete observed meteorological data were available, and Jensen-Haise's equation was used if a forecasted meteorological data were available, However none of the observed or forecasted data were available, the equation (15) was used. b. As an input time data, a crop carlender was used, which was made based on the time when the growth stage of the crop shows it's maximum effective leaf coverage. 9. For the purpose of validation of the models, observed data of soil moiture content under various conditions from May, 1975 to July, 1975 were compared to the data predicted by Model-I and Model-II. Model-I shows the relative error of 4.6 to 14.3 percent which is an acceptable range of error in view of engineering purpose. Model-II shows 3 to 16.7 percent of relative error which is a little larger than the one from the Model-I. 10. Comparing two models, the followings are concluded: Model-I established on the theoretical background can predict with a satisfiable reliability far practical use provided that forecasted meteorological data are available. On the other hand, Model-II was superior to Model-I in it's simplicity, but it needs long period and wide scope of observed data to predict acceptable soil moisture content. Further studies are needed on the Model-II to make it acceptable in practical use.

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Variability of Wind Energy in Korea Using Regional Climate Model Ensemble Projection (지역 기후 앙상블 예측을 활용한 한반도 풍력 에너지의 시·공간적 변동성 연구)

  • Kim, Yumi;Kim, Yeon-Hee;Kim, Nayun;Lim, Yoon-Jin;Kim, Baek-Jo
    • Atmosphere
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    • v.26 no.3
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    • pp.373-386
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    • 2016
  • The future variability of Wind Energy Density (WED) over the Korean Peninsula under RCP climate change scenario is projected using ensemble analysis. As for the projection of the future WED, changes between the historical period (1981~2005) and the future projection (2021~2050) are examined by analyzing annual and seasonal mean, and Coefficient of Variation (CV) of WED. The annual mean of WED in the future is expected to decrease compared to the past ones in RCP 4.5 and RCP 8.5 respectively. However, the CV is expected to increase in RCP 8.5. WEDs in spring and summer are expected to increase in both scenarios RCP 4.5 and RCP 8.5. In particular, it is predicted that the variation of CV for WED in winter is larger than other seasons. The time series of WED for three major wind farms in Korea exhibit a decrease trend over the future period (2021~2050) in Gochang for autumn, in Daegwanryeong for spring, and in Jeju for autumn. Through analyses of the relationship between changes in wind energy and pressure gradients, the fact that changes in pressure gradients would affect changes in WED is identified. Our results can be used as a background data for devising a plan to develop and operate wind farm over the Korean Peninsula.

Development for Estimation Improvement Model of Wind Velocity using Deep Neural Network (심층신경망을 활용한 풍속 예측 개선 모델 개발)

  • Ku, SungKwan;Hong, SeokMin;Kim, Ki-Young;Kwon, Jaeil
    • Journal of Advanced Navigation Technology
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    • v.23 no.6
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    • pp.597-604
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    • 2019
  • Artificial neural networks are algorithms that simulate learning through interaction and experience in neurons in the brain and that are a method that can be used to produce accurate results through learning that reflects the characteristics of data. In this study, a model using deep neural network was presented to improve the predicted wind speed values in the meteorological dynamic model. The wind speed prediction improvement model using the deep neural network presented in the study constructed a model to recalibrate the predicted values of the meteorological dynamics model and carried out the verification and testing process and Separate data confirm that the accuracy of the predictions can be increased. In order to improve the prediction of wind speed, an in-depth neural network was established using the predicted values of general weather data such as time, temperature, air pressure, humidity, atmospheric conditions, and wind speed. Some of the data in the entire data were divided into data for checking the adequacy of the model, and the separate accuracy was checked rather than being used for model building and learning to confirm the suitability of the methods presented in the study.

Estimation of Atmospheric Dispersion Coefficients in A Coastal Area with Complex Topography (복잡한 지형의 임해지역에서 대기 분산계수의 평가)

  • 박옥현;천성남
    • Journal of Korean Society for Atmospheric Environment
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    • v.14 no.5
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    • pp.411-420
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    • 1998
  • To estimate the dispersion coefficients in a coastal area with complex topography, several schemes using empirical equations expressed with and in lateral and vertical directions, respectively have been examined. Estimation results using these equations and meteorological data obtained from SODAR system were compared' with previously measured dispersion coefficients in other coastal areas. Validations of estimation results have been performed by comparing the measured concentrations with predicted ones empolying in Boryung coastal area. Important conclusions were drawn as follows; (1) Variations of lateral and vertical wind direction revealed different height dependency in upper and lower mixed boundary layer. (2) Because of turbulent constraint effect by large water body in a coastal region, the lateral and the vertical dispersion coefficients were smaller than those of P-G system. (3) As a result of examining the performance measure of these schemes through checking of coincidence between measured and predicted concentrations, vertical dispersion coefficients were smaller than those of P-G system, and the Cramer scheme was found to be more appropriate rather than others in the coastal area surrounding Boryung power plant.

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Attenuation of the Atmospheric Aerosol Transmissivity due to Air Pollution (대기오염에 의한 대기투과도 감쇠에 대한 연구)

  • Kim, Yoo-Keun;Lee, Hwa-Woon;Lee, Yong-Seob
    • Journal of Korean Society for Atmospheric Environment
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    • v.11 no.E
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    • pp.23-29
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    • 1995
  • Relationship between atmospheric aerosol transmissivity and air pollution was analyzed using observed data in a large industrial city, Pusan, Korea. The atmospheric aerosol transmissivity predicted by method of present study in Pusan was assessed by the method of Yamamoto et al.(1968) in order to set up an empirical model to predict the transmissivity using the various meteorological parameters and air pollution. As a result, good correlation between these tow method re observed. Thus, it is possible to conclude that the parameterization of air pollution suggested by this study is another method to give reliable estimate of atmospheric aerosol transmissivity and direct solar irradiance in Pusan.

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Performance Evaluation of Four Different Land Surface Models in WRF

  • Lee, Chong Bum;Kim, Jea-Chul;Belorid, Miloslav;Zhao, Peng
    • Asian Journal of Atmospheric Environment
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    • v.10 no.1
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    • pp.42-50
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    • 2016
  • This study presents a performance evaluation of four different land surface models (LSM) available in Weather Forecast Research (WRF). The research site was located in Haean Basin in South Korea. The basin is very unique by its geomorphology and topography. For a better representation of the complex terrain in the mesoscale model were used a high resolution topography data with a spatial resolution of 30 meters. Additionally, land-use layer was corrected by ground mapping data-sets. The observation equipments used in the study were an ultrasonic anemometer with a gas analyzer, an automatic weather station and a tethered balloon sonde. The model simulation covers a four-day period during autumn. The result shows significant impact of LSM on meteorological simulation. The best agreement between observation and simulation was found in the case of WRF with Noah LSM (WRF-Noah). The WRF with Rapid Update Cycle LSM (WRF-RUC) has a very good agreement with temperature profiles due to successfully predicted fog which appeared during measurements and affected the radiation budget at the basin floor. The WRF with Pleim and Xiu LSM (WRF-PX) and WRF with Thermal Diffusion LSM (WRF-TD) performed insufficiently for simulation of heat fluxes. Both overestimated the sensible and underestimated the latent heat fluxes during the daytime.

A Generation and Accuracy Evaluation of Common Metadata Prediction Model Using Public Bicycle Data and Imputation Method

  • Kim, Jong-Chan;Jung, Se-Hoon
    • Journal of Korea Multimedia Society
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    • v.25 no.2
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    • pp.287-296
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    • 2022
  • Today, air pollution is becoming a severe issue worldwide and various policies are being implemented to solve environmental pollution. In major cities, public bicycles are installed and operated to reduce pollution and solve transportation problems, and operational information is collected in real time. However, research using public bicycle operation information data has not been processed. This study uses the daily weather data of Korea Meteorological Agency and real-time air pollution data of Korea Environment Corporation to predict the amount of daily rental bicycles. Cross- validation, principal component analysis and multiple regression analysis were used to determine the independent variables of the predictive model. Then, the study selected the elements that satisfy the significance level, constructed a model, predicted the amount of daily rental bicycles, and measured the accuracy.

Aviation Convective Index for Deep Convective Area using the Global Unified Model of the Korean Meteorological Administration, Korea: Part 2. Seasonal Optimization and Case Studies (안전한 항공기 운항을 위한 현업 전지구예보모델 기반 깊은 대류 예측 지수: Part 2. 계절별 최적화 및 사례 분석)

  • Yi-June Park;Jung-Hoon Kim
    • Atmosphere
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    • v.33 no.5
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    • pp.531-548
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    • 2023
  • We developed the Aviation Convective Index (ACI) for predicting deep convective area using the operational global Numerical Weather Prediction model of the Korea Meteorological Administration. Seasonally optimized ACI (ACISnOpt) was developed to consider seasonal variabilities on deep convections in Korea. Yearly optimized ACI (ACIYrOpt) in Part 1 showed that seasonally averaged values of Area Under the ROC Curve (AUC) and True Skill Statistics (TSS) were decreased by 0.420% and 5.797%, respectively, due to the significant degradation in winter season. In Part 2, we developed new membership function (MF) and weight combination of input variables in the ACI algorithm, which were optimized in each season. Finally, the seasonally optimized ACI (ACISnOpt) showed better performance skills with the significant improvements in AUC and TSS by 0.983% and 25.641% respectively, compared with those from the ACIYrOpt. To confirm the improvements in new algorithm, we also conducted two case studies in winter and spring with observed Convectively-Induced Turbulence (CIT) events from the aircraft data. In these cases, the ACISnOpt predicted a better spatial distribution and intensity of deep convection. Enhancements in the forecast fields from the ACIYrOpt to ACISnOpt in the selected cases explained well the changes in overall performance skills of the probability of detection for both "yes" and "no" occurrences of deep convection during 1-yr period of the data. These results imply that the ACI forecast should be optimized seasonally to take into account the variabilities in the background conditions for deep convections in Korea.

Forecasting Monthly Agricultural Reservoir Storage and Estimation of Reservoir Drought Index (RDI) Using Meteorological Data Based Multiple Linear Regression Analysis (기상자료기반 다중선형회귀분석에 의한 농업용 저수지 월단위 저수율 예측 및 저수지 가뭄지수(RDI) 추정)

  • LEE, Ji-Wan;KIM, Jin-Uk;JUNG, Chung-Gil;KIM, Seong-Joon
    • Journal of the Korean Association of Geographic Information Studies
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    • v.21 no.3
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    • pp.19-34
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    • 2018
  • The purpose of this study is to estimate monthly agricultural reservoir storage with multiple linear regression model(MLRM) based on reservoir storage and meteorological data. The regression model was developed using 15 years(2002 to 2016) of 3,067 reservoirs by KRC(Korea Rural Community) and 63 meteorological stations by KMA (Korean Meteorological Administration), and the MLRM showed the determination coefficient($R^2$) of 0.51~0.95. The MLRM was applied to 9 selected reservoirs among the whole reservoirs and validated with $R^2$ of 0.44~0.81. The ROC(Receiver Operating Characteristics) analysis of Reservoir Drought Index(RDI) classified by comparing the present reservoir storage with normal year(1976~2005 average) reservoir storage showed average value of 0.64 for 2 years(2015~2016) with the highest value of 0.70 for winter period, lowest value of 0.58 for summer period. If 1 to 3 months weather forecasting data such as Glosea5 produced by KMA are applied, the predicted monthly reservoir storage from the MLRM can be a useful information for agricultural drought pre-preparation.