• Journal of Korean Society for Atmospheric Environment
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• v.25 no.1
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• pp.1-14
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• 2009

Wind energy has been recognized as one of the most important and fastest growing energy resources without emission of air pollutant. Thus, it is necessary to predict wind speed and direction accurately both in time and space toward the efficient usage of wind energy. Numerical simulation experiments using the Fifth-Generation Mesoscale Model (MM5) are carried out to clarify the impact of surface observation data assimilation on the estimation of wind energy resources. The EXP_Radius run was designed with respect to the radius of influence in the Four-Dimensional Data Assimilation (FDDA), and the EXP_Impact run was made by changing the nudging coefficient that determines the relative magnitude of the nudging term. The simulation period covers a clear-sky event on 3 - 5 June 2007 and another is on 2 - 4 December 2006. It is found that the simulated results are very sensitive to the radius of influence and nudging parameters in the FDDA. The further analysis of the results shows that the impact of the radius of influence tends to be stronger in weak synoptic flow episode than that in strong synoptic flows episode. The nudging factor is also sensitive to the intensity of the synoptic flows.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.5
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• pp.410-418
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• 2017

Results of an analysis of the continuous motion effect for FMCW-SAR system and a signal processing to correct it are presented in this paper. SAR images reconstructed by back-projection algorithm are included as well. To analyze how platform velocity and sampling frequency affect the continuous motion effect, FMCW signal model was used, and the signal processing in time-doppler(t, $k_u$) domain was adopted. Then, back-projection algorithm and modified matched-filter was used to reconstruct SAR images, and it was validated using measured data by airborne FMCW-SAR system in X-band frequency.

• Atmosphere
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• v.24 no.1
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• pp.101-110
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• 2014

This study estimates and evaluates the daily January temperature from 2003 to 2012 with 30 m-resolution over South Korea, using a modified Parameter-elevation Regression on Independent Slopes Model (K-PRISM). Several factors in K-PRISM are also adjusted to 30 m grid spacing and daily time scales. The performance of K-PRISM is validated in terms of bias, root mean square error (RMSE), and correlation coefficient (Corr), and is then compared with that of inverse distance weighting (IDW) and hypsometric methods (HYPS). In estimating the temperature over Jeju island, K-PRISM has the lowest bias (-0.85) and RMSE (1.22), and the highest Corr (0.79) among the three methods. It captures the daily variation of observation, but tends to underestimate due to a high-discrepancy in mean altitudes between the observation stations and grid points of the 30 m topography. The temperature over South Korea derived from K-PRISM represents a detailed spatial pattern of the observed temperature, but generally tends to underestimate with a mean bias of -0.45. In bias terms, the estimation ability of K-PRISM differs between grid points, implying that care should be taken when dealing with poor skill area. The study results demonstrate that K-PRISM can reasonably estimate 30 m-resolution temperature over South Korea, and reflect topographically diverse signals with detailed structure features.

• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.587-607
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• 2020

Soil moisture is essential information for meteorological and hydrological analyses. To date, many efforts have been made to achieve the two goals for soil moisture data, i.e., the improvement of accuracy and resolution, which is very challenging. We presented an ensemble downscaling method for quality improvement of gridded soil moisture data in terms of the accuracy and the spatial resolution by the integration of BMA (Bayesian model averaging) and ATPRK (area-to-point regression kriging). In the experiments, the BMA ensemble showed a 22% better accuracy than the data sets from ESA CCI (European Space Agency-Climate Change Initiative), ERA5 (ECMWF Reanalysis 5), and GLDAS (Global Land Data Assimilation System) in terms of RMSE (root mean square error). Also, the ATPRK downscaling could enhance the spatial resolution from 0.25° to 0.05° while preserving the improved accuracy and the spatial pattern of the BMA ensemble, without under- or over-estimation. The quality-improved data sets can contribute to a variety of local and regional applications related to soil moisture, such as agriculture, forest, hydrology, and meteorology. Because the ensemble downscaling method can be applied to the other land surface variables such as temperature, humidity, precipitation, and evapotranspiration, it can be a viable option to complement the accuracy and the spatial resolution of satellite images and numerical models.

• Journal of the Korean earth science society
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• v.45 no.2
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• pp.111-120
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• 2024

Cloud coverage is a key factor in determining whether to proceed with observations. In the past, human judgment played an important role in weather evaluation for observations. However, the development of remote and robotic observation has diminished the role of human judgment. Moreover, it is not easy to evaluate weather conditions automatically because of the diverse cloud shapes and their rapid movement. In this paper, we present the development of a cloud monitoring program by applying a machine learning-based Python module "cloudynight" on all-sky camera images obtained at Miryang Arirang Astronomical Observatory (MAAO). The machine learning model was built by training 39,996 subregions divided from 1,212 images with altitude/azimuth angles and extracting 16 feature spaces. For our training model, the F1-score from the validation samples was 0.97, indicating good performance in identifying clouds in the all-sky image. As a result, this program calculates "Cloudiness" as the ratio of the number of total subregions to the number of subregions predicted to be covered by clouds. In the robotic observation, we set a policy that allows the telescope system to halt the observation when the "Cloudiness" exceeds 0.6 during the last 30 minutes. Following this policy, we found that there were no improper halts in the telescope system due to incorrect program decisions. We expect that robotic observation with the 0.7 m telescope at MAAO can be successfully operated using the cloud monitoring program.

• Journal of information and communication convergence engineering
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• v.5 no.4
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• pp.295-299
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• 2007

This paper presents the ASF(Additional Secondary Factor) modeling of DGPS beacon signal. In addition to DGPS's original purpose, the feasibility to utilize DGPS system for timing and navigation has been studied. For timing and navigation, the positioning system must know the accurate time delay of signal traveling from the transmitter to receiver. Then the delay can be used to compute the user position. The DGPS beacon signal transmits the data using medium frequency, which travels through the surface and cause the additional delay rather than the speed of light according to conductivities and elevations of the irregular terrain. We introduce the modeling of additional delay(ASF) and present the results of implementation. The similar approach is Locan-C. Loran-C has been widely used as the maritime location system and was enhanced to E-Loran(Enhanced Loran). E-Loran system uses the ASF estimation method and is able to provide the more precise location service. However there was rarely research on this area in Korea. Hence, we introduce the ASF and its estimation model. With the comparison of the same condition and data from the original Monteath model and ASF estimation data of Loran system respectively, we guarantee that the implementation is absolutely perfect. For further works, we're going to apply the ASF estimation model to Korean DGPS beacon system with the Korean terrain data.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.395-408
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• 2021

In this study, we investigated the effects of wind speed and direction on building fires using GIS and a CFD model. We conducted numerical simulations for a fire event that occurred at an apartment in Ulsan on October 8, 2020. For realistic simulations, we used the profiles of wind speeds and directions and temperatures predicted by the local data assimilation and prediction system (LDAPS). First, using the realistic boundary conditions, we conducted two numerical simulations (a control run, CNTL, considered the building fire and the other assumed the same conditions as CNTL except for the building fire). Then, we conducted the additional four simulations with the same conditions as CNTL except for the inflow wind speeds and direction. When the ignition point was located on the windward of the building, strong updraft induced by the fire had a wide impact on the building roof and downwind region. The evacuation floor (15th floor) played a role to spread fire to the downwind wall of the building. The weaker the wind speed, the narrower fire spread around the ignition point, but the higher the flame above the building reaches. When the ignition point was located on the downwind wall of the building, the flame didn't spread to the upwind wall of the building. The results showed that wind speed and direction were important for the flow and temperature (or flame) distribution around a firing building.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1341-1352
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• 2023

The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.

• Atmosphere
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• v.25 no.1
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• pp.1-18
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• 2015

The most objective way to overcome the limitation of numerical weather prediction model is to represent the uncertainty of prediction by introducing probabilistic forecast. The uncertainty of the numerical weather prediction system developed due to the parameterization of unresolved scale motions and the energy losses from the sub-scale physical processes. In this study, we focused on the growth of model errors. We performed ensemble forecast to represent model uncertainty. By employing the multi-physics scheme (PHYS) and the stochastic kinetic energy backscatter scheme (SKEBS) in simulating typhoon Rusa (2002), we assessed the performance level of the two schemes. The both schemes produced better results than the control run did in the ensemble mean forecast of the track. The results using PHYS improved by 28% and those based on SKEBS did by 7%. Both of the ensemble mean errors of the both schemes increased rapidly at the forecast time 84 hrs. The both ensemble spreads increased gradually during integration. The results based on SKEBS represented model errors very well during the forecast time of 96 hrs. After the period, it produced an under-dispersive pattern. The simulation based on PHYS overestimated the ensemble mean error during integration and represented the real situation well at the forecast time of 120 hrs. The displacement speed of the typhoon based on PHYS was closest to the best track, especially after landfall. In the sensitivity tests of the model uncertainty of SKEBS, ensemble mean forecast was sensitive to the physics parameterization. By adjusting the forcing parameter of SKEBS, the default experiment improved in the ensemble spread, ensemble mean errors, and moving speed.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.29 no.2
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• pp.101-115
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• 2024

The ocean heatwave is emerging as a major issue due to global warming, posing a direct threat to marine ecosystems and humanity through decreased food resources and reduced carbon absorption capacity of the oceans. Consequently, the prediction of ocean heatwaves in the vicinity of the Korean Peninsula is becoming increasingly important for marine environmental monitoring and management. In this study, an LSTM model was developed to improve the underestimated prediction of ocean heatwaves caused by the coarse vertical grid system of the Korean Peninsula Ocean Prediction System. Based on the results of ocean heatwave predictions for the Korean Peninsula conducted in 2023, as well as those generated by the LSTM model, the performance of heatwave predictions in the East Sea, Yellow Sea, and South Sea areas surrounding the Korean Peninsula was evaluated. The LSTM model developed in this study significantly improved the prediction performance of sea surface temperatures during periods of temperature increase in all three regions. However, its effectiveness in improving prediction performance during periods of temperature decrease or before temperature rise initiation was limited. This demonstrates the potential of the LSTM model to address the underestimated prediction of ocean heatwaves caused by the coarse vertical grid system during periods of enhanced stratification. It is anticipated that the utility of data-driven artificial intelligence models will expand in the future to improve the prediction performance of dynamical models or even replace them.