• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2177-2186
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• 2017

The goal of this paper is to provide the specific forecasting steps and to explain how to design the forecasting architecture and training data sets to forecast very short-term wind power when the numerical weather prediction (NWP) is unavailable, and when the sampling periods of the wind power and training data are different. We forecast the very short-term wind power every 15 minutes starting two hours after receiving the most recent measurements up to 40 hours for a total of 38 hours, without using the NWP data but using the historical weather data. Generally, the NWP works as a predictor and can be converted to wind power forecasts through machine learning-based forecasting algorithms. Without the NWP, we can still build the predictor by shifting the historical weather data and apply the machine learning-based algorithms to the shifted weather data. In this process, the sampling intervals of the weather and wind power data are unified. To verify our approaches, we participated in the 2017 wind power forecasting competition held by the European Energy Market conference and ranked sixth. We have shown that the wind power can be accurately forecasted through the data shifting although the NWP is unavailable.

• Atmosphere
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• v.33 no.2
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• pp.73-104
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• 2023

Over the past 60 years, Korean numerical weather prediction (NWP) has advanced rapidly with the collaborative effort between the science community and the operational modelling center. With an improved scientific understanding and the growth of information technology infrastructure, Korea is able to provide reliable and seamless weather forecast service, which can predict beyond a 10 days period. The application of NWP has expanded to support decision making in weather-sensitive sectors of society, exploiting both storm-scale high-impact weather forecasts in a very short range, and sub-seasonal climate predictions in an extended range. This article gives an approximate chronological account of the NWP over three periods separated by breakpoints in 1990 and 2005, in terms of dynamical core, physics, data assimilation, operational system, and forecast application. Challenges for future development of NWP are briefly discussed.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.147-147
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• 2019

The Numerical Weather Prediction (NWP) models determine the future state of the weather by forcing current weather conditions into the atmospheric models. The NWP models approximate mathematically the physical dynamics by nonlinear differential equations; however these approximations include uncertainties. The errors of the NWP estimations can be related to the initial and boundary conditions and model parameterization. Development in the meteorological forecast models did not solve the issues related to the inevitable biases. In spite of the efforts to incorporate all sources of uncertainty into the forecast, and regardless of the methodologies applied to generate the forecast ensembles, they are still subject to errors and systematic biases. The statistical post-processing increases the accuracy of the forecast data by decreasing the errors. Error prediction of the NWP models which is updating the NWP model outputs or model output statistics is one of the ways to improve the model forecast. The regression methods (including linear, polynomial and scaling regression) are applied to the present study to improve the real time forecast skill. Such post-processing consists of two main steps. Firstly, regression is built between forecast and measurement, available during a certain training period, and secondly, the regression is applied to new forecasts. In this study, the WRF real-time forecast data, in comparison with the observed data, had systematic biases; the errors related to the NWP model forecasts were reflected in the underestimation of the meteorological data forecast by the WRF model. The promising results will indicate that the post-processing techniques applied in this study improved the meteorological forecast data provided by WRF model. A comparison between various bias correction methods will show the strength and weakness of the each methods.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6B
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• pp.579-587
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• 2010

The mid-range streamflow forecast was performed using NWP(Numerical Weather Prediction) provided by KMA. The NWP consists of RDAPS for 48-hour forecast and GDAPS for 240-hour forecast. To enhance the accuracy of the NWP, QPM to downscale the original NWP and Quantile Mapping to adjust the systematic biases were applied to the original NWP output. The applicability of the suggested streamflow prediction system which was verified in Geum River basin. In the system, the streamflow simulation was computed through the long-term continuous SSARR model with the rainfall prediction input transform to the format required by SSARR. The RQPM of the 2-day rainfall prediction results for the period of Jan. 1~Jun. 20, 2006, showed reasonable predictability that the total RQPM precipitation amounts to 89.7% of the observed precipitation. The streamflow forecast associated with 2-day RQPM followed the observed hydrograph pattern with high accuracy even though there occurred missing forecast and false alarm in some rainfall events. However, predictability decrease in downstream station, e.g. Gyuam was found because of the difficulties in parameter calibration of rainfall-runoff model for controlled streamflow and reliability deduction of rating curve at gauge station with large cross section area. The 10-day precipitation prediction using GQPM shows significantly underestimation for the peak and total amounts, which affects streamflow prediction clearly. The improvement of GDAPS forecast using post-processing seems to have limitation and there needs efforts of stabilization or reform for the original NWP.

• Atmosphere
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• v.29 no.2
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• pp.219-226
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• 2019

Generally, the weather forecast system has been run using prescribed ocean condition. As it is widely known that coupling between atmosphere and ocean process produces consistent initial condition at all-time scales to improve forecast skill, there are many trials on the application of data assimilation of coupled model. In this study, we implemented a weakly coupled data assimilation (short for WCDA) system in global NWP model with low horizontal resolution for coupled forecast with uncoupled initialization, following WCDA system at the Met Office. The experiment is carried out for a typhoon evolution forecast in 2017. Air-sea exchange process provides SST cooling and gives a substantial impact on tendency of central pressure changes in the decaying phase of the typhoon, except the underestimated central pressure. Coupled data assimilation is a challenging new area, requiring further work, but it would offer the potential for improving air-sea feedback process on NWP timescales and finally contributing forecast accuracy.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.15 no.3
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• pp.57-62
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• 2007

In this paper, we attempted to produce the cloud forecast that use the numerical weather prediction(NWP) MM5 for objective cloud forecast. We presented two methods for cloud forecast. One of them used total cloud mixing ratio registered to sum(synthesis) of cloud-water and cloud-ice grain mixing ratio those are variables related to cloud among NWP result data and the other method that used relative humidity. An experiment was carried out period from 23th to 24th July 2004. According to the sequence of comparing the derived cloud forecast data with the observed value, it was indicated that both of those have a practical use possibility as cloud forecast method. Specially in this Case study, cloud forecast method that use total cloud mixing ratio indicated good forecast availability to forecast of the low level clouds as well as middle and high level clouds.

• Applied Chemistry for Engineering
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• v.28 no.5
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• pp.501-505
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• 2017

Due to the rapid urbanization and increased population, there is an increase in airtight nature of buildings which causes serious indoor air pollution. Among several indoor air pollutants, volatile organic compounds (VOCs) emitted from paint are of major concern. Therefore, there is an urge for the development of environmental friendly paint products. In this wok, a natural wall paint (NWP) was prepared by utilizing a natural clay material "sericite" as a main component. A small chamber test was carried out to identify the toxic substances release from NWP and the results were compared with two eco-friendly commercial paints. The total VOCs were detected in trace level inside the test chamber and their concentrations were below the recommended indoor air quality standards. Toluene was not detected for NWP, whereas formaldehyde was observed in trace level. The toxicity index results were compared with two commercial paints and found that NWP exhibited less harmful gas emission. Based on certification rating of building materials, NWP can be classified as the first grade of building materials. Due to the above advantages, the use of sericite as a major component in NWP will be a useful technique to maintain the indoor air quality.

• Atmosphere
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• v.27 no.3
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• pp.317-329
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• 2017

Ocean mixed layer (OML) depth affects diurnal cycle of sea surface temperature (SST) induced by change of solar radiation absorption and heat budget in ocean. The diurnal SST variation can lead to convection over the ocean, which can impact on localized precipitation both over coastal and inland. In this study, we investigate the OML characteristics affecting the diurnal cycle of SST for the Korean Peninsula and surrounding areas. To analyze OML characteristics, HYCOM oceanic mixed layer depth (MLD) and wind field at 10 m from ERA-interim during 2008~2016 are used. In the winter, MLD is deeply formed when the strong wind field is located on perpendicular to continental slope over deep seafloor areas. Besides, cooling SST-induced vertical mixing in OML is reinforced by dry cold air originated from Siberia. The OML in summer is shallowly distributed about 20 m. In order to estimate the impact of OML model in high resolution NWP model, four experimental simulations are performed. At this time, the prognostic scheme of skin SST is applied in NWP to simulate diurnal SST. The simulation results show that CNTL (off-OML) overestimates diurnal cycle of SST, while EXPs (on-OML) indicate similar results to observations. The prediction performance for precipitation of EXPs shows improvement compared with CNTL over coastal as well as inland. This results suggest that the application of the OML model in summer season can contribute to improving the prediction for performance of SST and precipitation over coastal area and inland.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.23 no.3
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• pp.205-214
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• 2011

Finite element grid refinements with different intensities having 14 K, 52 K and 211 K on the Yellow Sea (YS) have been constructed to make precise tidal simulations. In the meanwhile 57 K grid was made to the extended North Western Pacific (NWP) sea. Numerical simulation were done based on 32 parallel processors by using pADCIRC v 49.21 model. In the YS tidal simulation on YS-G52K and YS-G211K grid structure, KorBathy30s and ETOPO1 bathymetry data are used and 4 major tidal constituents are prescribed from FES2004. Computed results are in good agreement within 0.138 meter in RMS error for amplification and 14.80 degree of phase compared to observed tidal records. Similar error bounds are acquired in the extended NWP tidal simulation on NWP-G57K grid with 8 tidal constituent prescription on the open boundary.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.153-155
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• 2007

Korea Meteorological Administration (KMA) has operationally produced Atmospheric Motion Vector (AMV) from the consecutive MTSAT-1R satellite image dataset. Comparing with radiosonde data, our current AMV scheme shows more than 10 m/s RMSE. Therefore we need to improve continuously its accuracy. Many AMV producers have stated that the bad performance of the Height Assignment (HA) algorithm is the main reason of degrading the accuracy of AMV. The uncertainties in AMV HA can occur in the algorithm itself, used NWP profiles, and the performance of Radiative Transfer Model (RTM) etc. This study introduces currently operated AMV HA schemes and the impacts of NWP profile data and RTM that these schemes use were investigated. Finally we analyzed the relationship between vectors by vector tracking and heights assigned to each vector by using collocated wind profile dataset with radiosonde data. This study is a preliminary work to improve the accuracy of AMV by removing or decreasing the uncertainties in AMV estimation.