• Journal of the Korean Solar Energy Society
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• v.36 no.1
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• pp.27-37
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• 2016

A simple but practical Ensemble Prediction System(EPS) for wind power forecasting was developed and evaluated using the measurement of the offshore meteorological tower, HeMOSU-1(Herald of Meteorological and Oceanographic Special Unite-1) installed at the Southwest Offshore in South Korea. The EPS developed by the Korea Institute of Energy Research is based on a simple ensemble mean of two Numerical Weather Prediction(NWP) models, WRF-NMM and WRF-ARW. In addition, the Kalman Filter is applied for real-time quality improvement of wind ensembles. All forecasts with EPS were analyzed in comparison with the HeMOSU-1 measurements at 97 m above sea level during Typhoon Bolaven episode in August 2012. The results indicate that EPS was in the best agreement with the in-situ measurement regarding (peak) wind speed and cut-out speed incidence. The RMSE of wind speed was 1.44 m/s while the incidence time lag of cut-out wind speed was 0 hour, which means that the EPS properly predicted a development and its movement. The duration of cut-out wind speed period by the EPS was also acceptable. This study is anticipated to provide a useful quantitative guide and information for a large-scale offshore wind farm operation in the decision making of wind turbine control especially during a typhoon episode.

• Atmosphere
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• v.22 no.2
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• pp.245-257
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• 2012

The Asian dust (Hwangsa) forecasting model, Asian Dust Aerosol Model (ADAM) has been modified by using satelliate monitoring of surface vegetation, which enables to simulate dusts occuring not only in springtime but also for all-year-round period. Coupled with the Unified Model (UM), the operational weather forecasting model at KMA, UM-ADAM2 was implemented for operational dust forecasting since 2010, with an aid of development of Meteorology-Chemistry Interface Processor (MCIP) for usage UM. The performance analysis of the ADAM2 forecast was conducted with $PM_{10}$ concentrations observed at monitoring sites in the source regions in China and the downstream regions of Korea from March to December in 2010. It was found that the UM-ADAM2 model was able to simulate quite well Hwangsa events observed in spring and wintertime over Korea. In the downstream region of Korea, the starting and ending times of dust events were well-simulated, although the surface $PM_{10}$ concentration was slightly underestimated for some dust events. The general negative bias less than $35{\mu}g\;m^{3}$ in $PM_{10}$ is found and it is likely to be due to other fine aerosol species which is not considered in ADAM2. It is found that the correlation between observed and forecasted $PM_{10}$ concentration increases as forecasting time approaches, showing stably high correlation about 0.7 within 36 hr in forecasting time. This suggests the possibility that there is potential for the UM-ADAM2 model to be used as an operational Asian dust forecast model.

• Journal of Wetlands Research
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• v.5 no.1
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• pp.29-40
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• 2003

This study was performed to research the relation between airmass thunderstorm and stability index with 12 years meteorological data(1990~2001) at Busan. Also We used the analysed stability indices from University of Wyoming to consider airmass thunderstorm. The frequency of thunderstorm occurrence during 12 years was 156 days(annual mean 13days). The airmass thunderstorm frequency was 14 days, most of those occurrence were summertime(59%). And occurrence hour of airmass thunderstorm was distributed from 1300LST to 2100LST broadly. The highest forecast index for airmass thunderstorm at Busan was K index, the lowest forecast index was SWEAT index. The forecasting of thunderstorms is based primary on the concepts of conditional instability, convective instability, and forced lifting of air near the surface. Instability is a critical factor in severe weather development. Severe weather stability indices can be a useful tool when applied correctly to a given convective weather situation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6B
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• pp.543-550
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• 2009

The traditional simple extrapolation type short term quantitative rainfall forecast can not realize the evolution of rainfall generating weather system. To overcome the drawback of the linear extrapolation type rainfall forecasting model, the history of a weather system from sequential weather radar information and a polynomial regression technique were used to generate forecast fileds of x-directional, y-directional velocities and radar reflectivity which considered the nonlinear behavior related to the evolution of weather systems. Results demonstrated that test statistics of forecasts using the developed model is better than that of 2-CAPPI forecast. However there is still a large room to improve the forecast of spatial and temporal evolution of local storms since the model is not based on a fully physical approach but a statistical approach.

• Research in Plant Disease
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• v.30 no.3
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• pp.256-267
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• 2024

We have developed FBcastS (Fire Blight Forecasting System), a cloud-based information system that leverages the K-Maryblyt forecasting model. The FBcastS provides an optimal timing for spraying antibiotics to prevent flower infection caused by Erwinia amylovora and forecasts the onset of disease symptoms to assist in scheduling field scouting activities. FBcastS comprises four discrete subsystems tailored to specific functionalities: meteorological data acquisition and processing, execution of the K-Maryblyt model, distribution of web-based information, and dissemination of spray timing notifications. The meteorological data acquisition subsystem gathers both observed and forecasted weather data from 1,583 sites across South Korea, including 761 apple or pear orchards where automated weather stations are installed for fire blight forecast. This subsystem also performs post-processing tasks such as quality control and data conversion. The model execution subsystem operates the K-Maryblyt model and stores its results in a database. The web-based service subsystem offers an array of internet-based services, including weather monitoring, mobile services for forecasting fire blight infection and symptoms, and nationwide fire blight monitoring. The final subsystem issues timely notifications of fire blight spray timing alert to growers based on forecasts from the K-Maryblyt model, blossom status, pesticide types, and field conditions, following guidelines set by the Rural Development Administration. FBcastS epitomizes a smart agriculture internet of things (IoT) by utilizing densely collected data with a spatial resolution of approximately 4.25 km to improve the accuracy of fire blight forecasts. The system's internet-based services ensure high accessibility and utility, making it a vital tool in data-driven smart agricultural practices.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.3
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• pp.403-412
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• 2018

In this study, a three-dimensional variational(3DVAR) data assimilation system was developed for the operational numerical weather prediction(NWP) system at the Republic of Korea Air Force Weather Group. The Air Force NWP system utilizes the Weather Research and Forecasting(WRF) meso-scale regional model to provide weather information for the military service. Thus, the data assimilation system was developed based on the WRF model. Experiments were conducted to identify the nested model domain to assimilate observations and the period appropriate in estimating the background error covariance(BEC) in 3DVAR. The assimilation of observations in domain 2 is beneficial to improve 24-h forecasts in domain 3. The 24-h forecast performance does not change much depending on the estimation period of the BEC in 3DVAR. The results of this study provide a basis to establish the operational data assimilation system for the Republic of Korea Air Force Weather Group.

• Journal of Navigation and Port Research
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• v.28 no.10 s.96
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• pp.833-841
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• 2004

This paper treats optimal route assessment system at seaway based on weather forecasting and wave measurement through observation Since early times, captain & officer have been sailing to select the optimum route considering the weather and ship status condition empirically. However, it is rare to find digitalized onboard route support system whereas weather fax or wave and swell chart are utilized for the officer, based on officer's experience. In this paper, optimal route assessment system which is composed of voyage efficiency and safety component is introduced. Optimum route minimized ETA(estimated time of arrival) and fuel consumption is evaluated for efficient voyage considering speed loss and power increase based on wave added resistance of ship. In the view point of safety, seakeeping prediction is performed based on 3 dimensional panel method. Basically, the weather forecast is assumed to be prepared previously in order to operate this system.

• Journal of Korean Society for Atmospheric Environment
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• v.25 no.6
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• pp.539-549
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• 2009

The purpose of this study is to estimate hourly Asian dust emission flux in springtime by using the optimized Weather Research Forecasting model (WRF) in order to accurately predict the horizontal flux of Asian dusts. Asian dust emission flux using 5 empirical formulas such as US EPA, Park and Inn, Wang, The Goddard Chemistry Aerosol Radiation and Transport (GOCART) and Dust Entrainment and Deposition (DEAD) were calculated and compared by using classified land-use types and size distribution at various locations in China and Mongolia together with the hourly meteorological elements of the WRF model. As a result, the empirical formula in US EPA among them, which was considered the various conditions such as vegetation, soil type and terrain, was better than the other 4 empirical formulas. However, these formulas were adjusted hourly and vertically in time and space because there was different order and time resolution of dust emissions from original empirical formulas.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.95-99
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• 2023

Recently Machine Learning has been considered as one of the active research areas of Computer Science. The various Artificial Intelligence techniques are used to solve the classification problems of environmental sciences, biological sciences, and medical sciences etc. Due to the heterogynous and malfunctioning weather sensors a considerable amount of noisy data with missing is generated, which is alarming situation for weather prediction stockholders. Filling of these missing values with proper method is really one of the significant problems. The data must be cleaned before applying prediction model to collect more precise & accurate results. In order to solve all above stated problems, this research proposes a novel weather forecasting system which consists upon two steps. The first step will prepare data by reducing the noise; whereas a decision model is constructed at second step using regression algorithm. The Confusion Matrix will be used to evaluation the proposed classifier.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.11a
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• pp.61-70
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• 2004

This paper treats optimal route assessment system at seaway based on weather forecasting and wave measurement through observation. Since early times. captain & officer have been sailing to select the optimum route considering the weather ana ship status condition empirically. However. it is rare to find digitalized onboard route support system whereas weather fax or wave and swell chart are utilized for the officer. based on officer's experience. In this paper, optimal route assessment system which is composed of voyage efficiency and safety component is introduced. Optimum route minimized ETA (estimated time of arrival) ana fuel consumption is evaluated for efficient voyage considering speed loss and power increase based on wave added resistance of ship. In the view point of safety, seakeeping prediction is performed based on 3 dimensional panel method. Basically. the weather forecast is assumed to be prepared previously in order to operate this system.