• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.370-370
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• 2021

태풍으로 인한 피해를 줄이기 위해 경로, 강도 및 폭풍해일의 사전 예측은 매우 중요하다. 이중, 태풍의 경로와는 달리 강도 및 폭풍해일의 예측에 있어서 바람장은 수치 모델의 초기 입력값으로 요구되기 때문에 정확한 바람장 정보는 필수적이다. 대기 바람장 예측 방법은 크게 해석적 모델링, 라디오존데 측정과 위성 사진을 통한 산출로 구분할 수 있다. Holland의 해석적 모델링은 비교적 적은 입력값이 필요하지만 정확도가 낮고, 라디오존데 측정은 정확도가 높지만 점 측정에 가깝기 때문에 이차원 바람장을 산출하기에 한계가 있다. 위성 사진을 통한 바람장 산출은 위성기술의 고도화로 관측 채널 수 및 시공간 해상도가 크게 증가하고 있기 때문에 다양한 기법들이 개발되고 있다. 본 연구에서는 생성적 적대 신경망 (Generative Adversarial Network, GAN)을 통해 일련의 연속된 과거 적외 채널 위성 사진 흐름의 패턴을 학습시켜 미래 위성 사진을 예측하고, 예측된 연속적인 위성 사진들의 교차상관 (cross-correlation)을 통해 바람장을 산출하였다. GAN을 적용함에 있어 2011년부터 2019년까지 한반도 근방에 접근했던 태풍 중에 4등급 이상인 68개의 태풍의 한 시간 간격으로 촬영된 총 15,683개의 위성 사진을 학습시켜 생성된 이미지들은 실측 위성 사진들과 매우 유사한 것으로 나타났다. 또한, 생성된 이미지들의 교차상관으로 얻어진 바람장 벡터들의 풍향, 풍속, 벡터 일관성 및 수치 모델과의 비교를 통해 각각의 벡터들의 품질 계수를 구하고 정확도가 높은 벡터들만 결과에 포함하였다. 마지막으로 국내 6개의 라디오존데 관측점에서의 실측 벡터와의 비교를 통해 본 연구 결과의 실효성을 검증하였다. 본 연구에서 확장하여, 이와 같이 AI 기법과 이미지 교차상관 기법을 사용하여 얻어진 바람장으로부터 태풍 강도예측에 필요한 요소인 태풍의 눈의 위치, 최고 속도와 태풍 반경을 직접적으로 산출할 수 있고. 이러한 위성 사진을 기반으로 한 바람장은 단순화된 해석적 바람장을 대체하여 폭풍 해일 모델링의 예측 성능 개선에 기여할 것으로 보여진다.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.58 no.4
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• pp.299-309
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• 2022

Among the fishing vessels operating in the coastal waters, jigging fishing vessels were considered representative vessels engaged only by wind, sea, tide, and external force. Then, a fishing vessel with a length of shorter than 10 m from July 1, 2018 to August 5, 2019 was studied to obtain a drift prediction model by multiple regression analysis. In the correlation analysis between variables for leeway of speed and direction, the speed and direction of tidal seem to be the most affected in coastal waters. Therefore, it should be considered an explanatory variable when conducting drift tests. As a result of multiple regression analysis on the predicted equations of leeway speed and direction due to the external force on the drift of the fishing vessel, p < 0.000 was considered significant in the F-test, but the coefficient of determination was 55.2% and 37.8%. The effect on the predicted leeway speed was in the order of the tidal speed and current speed. In addition, the impact on the predicted leeway direction was in the order of the tidal speed and current speed. ŷ(m/s) = - 0.0011(x1) + 0.9206(x2) + 0.0001(x3) + 0.0002(x4) + 0.0050(x5) + 0.0529(x6) + 0.2457 ŷ(degree) = 0.6672(x1) + 93.1699(x2) + 0.0585(x3) - 0.0244(x4) - 1.2217(x5) + 4.6378(x6) - 0.0837

• Journal of Astronomy and Space Sciences
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• v.41 no.1
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• pp.1-15
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• 2024

The Korean heliospheric community, led by the Korea Astronomy and Space Science Institute (KASI), is currently assessing the viability of deploying a spacecraft at the Sun-Earth Lagrange Point L4 in collaboration with National Aeronautics and Space Administration (NASA). The aim of this mission is to utilize a combination of remote sensing and in situ instruments for comprehensive observations, complementing the capabilities of the L1 and L5 observatories. The paper outlines longterm scientific objectives, underscoring the significance of multi-point in-situ observations to better understand critical heliospheric phenomena. These include coronal mass ejections, magnetic flux ropes, heliospheric current sheets, kinetic waves and instabilities, suprathermal electrons and solar energetic particle events, as well as remote detection of solar radiation phenomena. Furthermore, the mission's significance in advancing space weather prediction and space radiation exposure assessment models through the integration of L4 observations is discussed. This article is concluded with an emphasis on the potential of L4 observations to propel advancements in heliospheric science.

• Atmosphere
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• v.34 no.2
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• pp.97-106
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• 2024

The seasonal forecast skill of tropical cyclones (TCs) in the Northern Hemisphere from the Korea Meteorological Administration (KMA) Global Seasonal Forecast System version 6 (GloSea6) hindcast has been verified for the period 1993 to 2016. The operational climate prediction system at KMA was upgraded from GloSea5 to GloSea6 in 2022, therefore further validation was warranted for the seasonal predictability and variability of this new system for TC forecasts. In this study, we examine the frequency, track density, duration, and strength of TCs in the North Indian Ocean, the western North Pacific, the eastern North Pacific, and the North Atlantic against the best track data. This methodology follows a previous study covering the period 1996 to 2009 published in 2020. GloSea6 indicates a higher frequency of TC generation compared to observations in the western North Pacific and the eastern North Pacific, suggesting the possibility of more TC generation than GloSea5. Additionally, GloSea6 exhibits better interannual variability of TC frequency, which shows relatively good correlation with observations in the North Atlantic and the western North Pacific. Regarding TC intensity, GloSea6 still underestimates the minimum surface pressures and maximum wind speeds from TCs, as is common among most climate models due to lower horizontal resolutions. However, GloSea6 is likely capable of simulating slightly stronger TCs than GloSea5, partly attributed to more frequent 6-hourly outputs compared to the previous daily outputs.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.19 no.5
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• pp.492-501
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• 2007

Coastal disasters have become one of the most important issues in every coastal country. In Korea, coastal disasters such as storm surge, sea level rise and extreme weather have placed many coastal regions in danger of being exposed or damaged during subsequent storms and gradual shoreline retreat. A storm surge is an onshore gush of water associated with a tow pressure weather system, typically in typhoon season. However, it is very difficult to predict storm surge height and inundation due to the irregularity of the course and intensity of a typhoon. To provide a new scheme of typhoon damage prediction model, the scenario which changes the central pressure, the maximum wind radius, the track and the proceeding speed by corresponding previous typhoon database, was composed. The virtual typhoon scenario database was constructed with individual scenario simulation and evaluation, in which it extracted the result from the scenario database of information of the hereafter typhoon and information due to climate change. This virtual typhoon scenario database will apply damage prediction information about a typhoon. This study performed construction and analysis of the simulation system with the storm surge/coastal inundation model at Masan coastal areas, and applied method for predicting using the scenario of the storm surge.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1653-1667
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• 2020

In this study, we coupled a computation fluid dynamics (CFD) model to the local data assimilation and prediction system (LDAPS), a current operational numerical weather prediction model of the Korea Meteorological Administration. We investigated the characteristics of fine particulate matter (PM2.5) distributions in a building-congested district. To analyze the effects of road emission on the PM2.5 concentrations, we calculated road emissions based on the monthly, daily, and hourly emission factors and the total amount of PM2.5 emissions established from the Clean Air Policy Support System (CAPSS) of the Ministry of Environment. We validated the simulated PM2.5 concentrations against those measured at the PKNU-AQ Sensor stations. In the cases of no road emission, the LDAPS-CFD model underestimated the PM2.5 concentrations measured at the PKNU-AQ Sensor stations. The LDAPS-CFD model improved the PM2.5 concentration predictions by considering road emission. At 07 and 19 LST on 22 June 2020, the southerly wind was dominant at the target area. The PM2.5 distribution at 07 LST were similar to that at 19 LST. The simulated PM2.5 concentrations were significantly affected by the road emissions at the roadside but not significantly at the building roof. In the road-emission case, the PM2.5 concentration was high at the north (wind speeds were weak) and west roads (a long street canyon). The PM2.5 concentration was low in the east road where the building density was relatively low.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.1
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• pp.1-10
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• 2012

Digital Forecast of the Korea Meteorological Administration (KMA) represents 5 km gridded weather forecast over the Korean Peninsula and the surrounding oceanic regions in Korean territory. Digital Forecast provides 12 weather forecast elements such as three-hour interval temperature, sky condition, wind direction, wind speed, relative humidity, wave height, probability of precipitation, 12 hour accumulated rain and snow, as well as daily minimum and maximum temperatures. These forecast elements are updated every three-hour for the next 48 hours regularly. The objective of this study was to construct Forest Fire Danger Rating Systems on the Korean Peninsula (FFDRS_KORP) based on the daily weather index (DWI) and to improve the accuracy using the digital forecast data. We produced the thematic maps of temperature, humidity, and wind speed over the Korean Peninsula to analyze DWI. To calculate DWI of the Korean Peninsula it was applied forest fire occurrence probability model by logistic regression analysis, i.e. $[1+{\exp}\{-(2.494+(0.004{\times}T_{max})-(0.008{\times}EF))\}]^{-1}$. The result of verification test among the real-time observatory data, digital forecast and RDAPS data showed that predicting values of the digital forecast advanced more than those of RDAPS data. The results of the comparison with the average forest fire danger rating index (sampled at 233 administrative districts) and those with the digital weather showed higher relative accuracy than those with the RDAPS data. The coefficient of determination of forest fire danger rating was shown as $R^2$=0.854. There was a difference of 0.5 between the national mean fire danger rating index (70) with the application of the real-time observatory data and that with the digital forecast (70.5).

• Korean Journal of Environment and Ecology
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• v.32 no.4
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• pp.413-424
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• 2018

The study analyzed the effects of topographic structures and altitude in mountainous parks in Mt. Namsan in Gyeongju on the generation of anions. The temperature was at ridge ($9.82^{\circ}C$) > valley ($8.44^{\circ}C$), the relative humidity valley (59.01 %) > ridge (58.64 %), the solar radiation ridge ($34.40W/m^2$) > valley($14.69W/m^2$), the wind speed ridge (0.63m/s) > valley(0.37m/s), and the negative ion valley($636.81ea/cm^3$) > ridge($580.04ea/cm^3$). In the valley, the correlation with altitude was verified for the temperature, relative humidity, solar radiation, and negative ion generation in the valley. The relative humidity, solar radiation, and negative ion indicated a positive correlation while the temperature had a negative correlation. In the ridge, the correlation with altitude was verified for the temperature, relative humidity, wind speed, solar radiation, and negative ion generation. The relative humidity, solar radiation, and negative ion generation indicated a positive correlation while the temperature and wind speed had a negative correlation. The regression analysis showed the prediction equation of y=-0.006x+9.663 (x=altitude, y=temperature) in the valley and y=-0.009x+11.595 (x=altitude, y=temperature) in the ridge for the temperature, y=0.027x+53.561 (x=altitude, y=relative humidity) in the valley and y=0.008x+56.646 (x=altitude, y=relative humidity) in the ridges for the relative humidity, and y=0.027x+53.561 (x=altitude, y=negative Ion generation) in the valley and y= 0.008x+56.646 (x=altitude, y=negative Ion generation) in the ridge for the negative ion generation.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.307-319
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• 2016

A Land-Atmosphere Modeling Package (LAMP) for supporting agricultural and forest management was developed at the National Center for AgroMeteorology (NCAM). The package is comprised of two components; one is the Weather Research and Forecasting modeling system (WRF) coupled with Noah-Multiparameterization options (Noah-MP) Land Surface Model (LSM) and the other is an offline one-dimensional LSM. The objective of this paper is to briefly describe the two components of the NCAM-LAMP and to evaluate their initial performance. The coupled WRF/Noah-MP system is configured with a parent domain over East Asia and three nested domains with a finest horizontal grid size of 810 m. The innermost domain covers two Gwangneung deciduous and coniferous KoFlux sites (GDK and GCK). The model is integrated for about 8 days with the initial and boundary conditions taken from the National Centers for Environmental Prediction (NCEP) Final Analysis (FNL) data. The verification variables are 2-m air temperature, 10-m wind, 2-m humidity, and surface precipitation for the WRF/Noah-MP coupled system. Skill scores are calculated for each domain and two dynamic vegetation options using the difference between the observed data from the Korea Meteorological Administration (KMA) and the simulated data from the WRF/Noah-MP coupled system. The accuracy of precipitation simulation is examined using a contingency table that is made up of the Probability of Detection (POD) and the Equitable Threat Score (ETS). The standalone LSM simulation is conducted for one year with the original settings and is compared with the KoFlux site observation for net radiation, sensible heat flux, latent heat flux, and soil moisture variables. According to results, the innermost domain (810 m resolution) among all domains showed the minimum root mean square error for 2-m air temperature, 10-m wind, and 2-m humidity. Turning on the dynamic vegetation had a tendency of reducing 10-m wind simulation errors in all domains. The first nested domain (7,290 m resolution) showed the highest precipitation score, but showed little advantage compared with using the dynamic vegetation. On the other hand, the offline one-dimensional Noah-MP LSM simulation captured the site observed pattern and magnitude of radiative fluxes and soil moisture, and it left room for further improvement through supplementing the model input of leaf area index and finding a proper combination of model physics.

• The Journal of the Acoustical Society of Korea
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• v.39 no.6
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• pp.606-614
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• 2020

In this paper, we analyze the influences of ship traffic and marine weather information on underwater ambient noise. Ambient noise is an important environmental factor that greatly affects the detection performance of underwater sonar systems. In order to implement an automated system such as prediction of detection performance using artificial intelligence technology, which has been recently studied, it is necessary to obtain and analyze major data related to these. The main sources of ambient noise have various causes. In the case of sonar systems operating in offshore seas, the detection performance is greatly affected by the noise caused by ship traffic and marine weather. Therefore, in this paper, the impact of each data was analyzed using the measurement results of ambient noise obtained in coastal area of the East Sea of Korea, and public data of nearby ship traffic and ocean weather information. As a result, it was observed that the underwater ambient noise was highly correlated with the change of the ship's traffic volume, and that marine environment factors such as wind speed, wave height, and rainfall had an effect on a specific frequency band.