• Journal of Astronomy and Space Sciences
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• 제25권3호
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• pp.267-282
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• 2008

대류층의 중성 대기는 전자가파의 신호 지연을 일으키기 때문에, GNSS(Global Navigation Satel-lite System)를 이용한 정밀측위의 가장 큰 오차요인으로 작용한다. 대류층 지연오차는 대류층의 굴절률과 연관 있으며, 대류층의 굴절률은 경험적으로 압력, 온도 및 수증기 분압으로 표현된다. 따라서 GNSS 안테나 위치의 기상 정보를 알고 있다면, 대류층 지연오차는 경험적 법칙에 의해 산출될 수 있다. 이 연구에서는 임의의 장소와 시간에 대한 대류층 지연오차를 생성하기 위한 기상정보 생성에 대하여 연구하였다. 한국천문연구원이 운영하는 9개의 상시 관측소에 설치된 디지털 기상 센서의 관측값을 가지고 범용 크리깅 (Ordinary Kriging)을 적용하여 기상 정보를 생성하였고, 상시 관측소의 데이터 공백을 메우기 위해 각 상시관측소의 기상 데이터를 분석하여 수치 모델을 만들어 보완하였다.

• 대기
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• 제31권3호
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• pp.341-359
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• 2021

This technical note introduces the new Korea Meteorological Administration (KMA) Global Seasonal forecasting system version 6 (GloSea6) to provide a reference for future scientific works on GloSea6. We describe the main areas of progress and improvements to the current GloSea5 in the scientific and technical aspects of all the GloSea6 components - atmosphere, land, ocean, and sea-ice models. Also, the operational architectures of GloSea6 installed on the new KMA supercomputer are presented. It includes (1) pre-processes for atmospheric and ocean initial conditions with the quasi-real-time land surface initialization system, (2) the configurations for model runs to produce sets of forecasts and hindcasts, (3) the ensemble statistical prediction system, and (4) the verification system. The changes of operational frameworks and computing systems are also reported, including Rose/Cylc - a new framework equipped with suite configurations and workflows for operationally managing and running Glosea6. In addition, we conduct the first-ever run with GloSea6 and evaluate the potential of GloSea6 compared to GloSea5 in terms of verification against reanalysis and observations, using a one-month case of June 2020. The GloSea6 yields improvements in model performance for some variables in some regions; for example, the root mean squared error of 500 hPa geopotential height over the tropics is reduced by about 52%. These experimental results show that GloSea6 is a promising system for improved seasonal forecasts.

• 대기
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• 제32권2호
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• pp.119-133
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• 2022

From 2015 to 2021, high-concentration fine dust episodes with a daily average PM_2.5 concentration of 50 ㎍ m^-3 or higher were selected and classified into 3 types [long range transport (LRT), mixed (MIX) and Local emission and stagnant (LES)] using synoptic chart and backward trajectory analysis. And relationships between the fine particle data (PM_2.5 and PM₁₀ concentration and PM_2.5/PM₁₀ ratio) and meteorological data (PBLH, Ta, WS, U-wind, and Rainfall) were analyzed using hourly observation for the classification episodes on the Korean Peninsula and the Seoul metropolitan area (SMA). In LRT, relatively large particles such as dust are usually included, and in LES, fine particle is abundant. In the Korean peninsula, the rainfall was relatively increased centered on the middle and western coasts in MIX and LES. In the SMA, wind speed was rather strong in LRT and weak in LES. In LRT, rainfall was centered in Seoul, and in MIX and LES, rainfall appeared around Seoul. However, when the dust cases were excluded, the difference between the LRT and other types of air quality was decreased, but the meteorological variables (Ta, RH, Pa, PBLH, etc.) were further strengthened. In the case of the Korean Peninsula, it is difficult to find a clear relationship because regional influences (topographical elevation, cities and coasts, etc.) are complexly included in a rather wide area. In the SMA, it is analyzed that the effects of urbanization such as the urban heat island centered on Seoul coincide with the sea and land winds, resulting in a combination of high concentrations and meteorological phenomena.

• 대기
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• 제34권2호
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• pp.177-185
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• 2024

As increasing global interest in renewable energy due to the ongoing climate crisis, there is a growing need for efficient technologies to manage such resources. This study focuses on the predictive skill of daily solar power generation using weather observation and forecast data. Meteorological data from the Korea Meteorological Administration and solar power generation data from the Korea Power Exchange were utilized for the period from January 2017 to May 2023, considering both inland (Daejeon) and coastal (Busan) regions. Temperature, wind speed, relative humidity, and precipitation were selected as relevant meteorological variables for solar power prediction. All data was preprocessed by removing their systematic components to use only their residuals and the residual of solar data were further processed with weighted adjustments for homoscedasticity. Four models, MLR (Multiple Linear Regression), RF (Random Forest), DNN (Deep Neural Network), and RNN (Recurrent Neural Network), were employed for solar power prediction and their performances were evaluated based on predicted values utilizing observed meteorological data (used as a reference), 1-day-ahead forecast data (referred to as fore1), and 2-day-ahead forecast data (fore2). DNN-based prediction model exhibits superior performance in both regions, with RNN performing the least effectively. However, MLR and RF demonstrate competitive performance comparable to DNN. The disparities in the performance of the four different models are less pronounced than anticipated, underscoring the pivotal role of fitting models using residuals. This emphasizes that the utilized preprocessing approach, specifically leveraging residuals, is poised to play a crucial role in the future of solar power generation forecasting.

• 한국조경학회지
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• 제34권1호
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• pp.21-36
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• 2006

This study was undertaken to analyze the effects of pavement thermal properties and water retention characteristics on the surface temperature of the gray permeable cement concrete pavement during the summer. Following is a summary of major results. 1) The hourly surface temperature of pavement could be well predicted with a heat transfer model program that incorporated the input data of major meteorological variables including solar radiation, atmospheric temperature, dew point, wind velocity, cloudiness and the evaporation rate of the pavements predicted by the time domain reflectometry (TDR) method. 2) When the albedo was changed to 0.5 from an arbitrary starting condition of 0.3, holding other variables constant, the peak surface temperature of the pavement dropped by 11.5%. When heat capacity was changed to $2.5\;kJm^{-3}K^{-1}\;from\;1.5\;kJm^{-3}K^{-1}$, surface temperature dropped by 8.0%. When daily evaporation was changed to 1 mm from 2 mm, temperature dropped by 5.7%. When heat conductivity was changed to $2.5\;Wm^{-1}K^{-1}\;from\;1.5\;Wm^{-1}K^{-1}$, the peak surface temperature of the pavement fell by 1.2%. The peak pavement surface temperature under the arbitrary basic condition was $24.46^{\circ}C$ (12 a.m.). 3) It accordingly became evident that the pavement surface temperature can be most effectively lowered by using materials with a high albedo, a high heat capacity or a high evaporation at the pavement surface. The glare situation, however, is intensified by raising of the albedo, moreover if reflected light is absorbed into surrounding physical masses, it is changed into heat. It accordingly became evident that raising the heat capacity and the evaporative capacity may be the moot acceptable measures to improve the thermal characteristics of the pavement. 4) The sensitivity of the surface temperature to major meteorological variables was as follows. When the daily average temperature changed to $0^{\circ}C\;from\;15^{\circ}C$, holding all other variables constant, the peak surface temperature of the pavement decreased by 56.1 %. When the global solar radiation changed to $200\;Wm^{-2}\;from\;600\;Wm^{-2}$, the temperature of the pavement decreased by 23.4%. When the wind velocity changed to $8\;ms^{-1}\;from\;4\;ms^{-1}$, the temperature decreased by 1.4%. When the cloudiness level changed to 1.0 from 0.5, the peak surface temperature decreased by 0.7%. The peak pavement surface temperature under the arbitrary basic conditions was $24.46^{\circ}C$ (12 a.m.)

• 해양환경안전학회:학술대회논문집
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• 해양환경안전학회 2017년도 추계학술발표회
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• pp.153-153
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• 2017

• 식물병연구
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• 제30권1호
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• pp.34-49
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• 2024

The development of sustainable smut management techniques requires an understanding of the impacts of smut on sugarcane growth and the relationships between smut intensity and meteorological variables, varieties, and crop types. Thus, assessments were made with the objectives to 1) determine the effect of smut on the above-ground growth of sugarcane, and 2) quantify the association of smut with weather variables, varieties and crop types. The effect of smut on above-ground growth was assessed in six fields planted with NCo 334 (wider coverage) having 6 months of age in Fincha and Metehara fields in 2021. Data on above-ground growth were taken from 20 randomly selected smut-affected and healthy stools from each field. Besides, 6 years' data (2015 to 2021) on the numbers of smut-affected stools and smut whips of 79 fields were collected. Furthermore, 10 years' (2011 to 2021) weather data were acquired from the sugar plantations. The results demonstrated reduction in the above-ground growth of sugarcane in the range of 18.39% and 73.42% due to smut. In addition, weather variables explained about 68.48% and 66.58% of the variability in the number of smut-affected stools and whips respectively. Smut intensity increased with crop types for susceptible varieties. The tight association between the smut epidemic and crop types, varieties, and weather, implied that these parameters must be carefully considered in management decisions. Continuous monitoring of smut disease, meteorological variables, varieties, and crop types in all the sugarcane plantations could be done as a part of integrated smut management in the future.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.991-993
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• 2003

The 4 sets of environmental variables dealing with meteorology, hydrology and physiography were analyzed to generate a spatial drought risk index of Phitsanulok province of Thailand. The analysis of K-mean and discriminant were applied to the set of the selective drought variables for grouping each of spatial variable set into 4 classes. The obtained 4 classes, based on group statistics, were thus recoded in the meaning of no risk, low risk, moderate risk, and high risk. The regression coefficient between recoded classes and a set of the selective environmental variables were then applied as spatial variable weighting on thematic dataset in GIS spatial analysis. The results showed that the weighting score of drought variable was highest in meteorological variable compared to other variables.

• Wind and Structures
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• 제15권2호
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• pp.111-129
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• 2012

Codes for structural design usually assume that the incident mean wind velocity is parallel to the ground, which constitutes a valid simplification for frequent winds caused by sypnoptic events. Wind effects due to other phenomena, such as thunderstorm downbursts, are simply neglected. In this paper, results of recent and ongoing research on this topic in Brazil are presented. The model of the three-dimensional wind velocity field originated from a downburst in a thunderstorm (TS), proposed by Ponte and Riera for engineering applications, is first described. This model allows the generation of a spatially and temporally variable velocity field, which also includes a fluctuating component of the velocity. All parameters are related to meteorological variables, which are susceptible of statistical assessment. An application of the model in the simulation of the wind climate in a region sujected to both EPS and TS winds is discussed next. It is shown that, once the relevant meteorological variables are known, the simulation of the wind excitation for purposes of design of transmission lines, long-span crossings and similar structures is feasible. Complementing the theoretical studies, wind velocity records during a recent TS event in southern Brazil are presented and preliminary conclusions on the validity of the proposed models discussed.

• 대기
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• 제22권4호
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• pp.455-464
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• 2012

This study investigates a 12 month-lead predictability of PNU Coupled General Circulation Model (CGCM) V1.1 hindcast, for which an oceanic data assimilated initialization is used to generate ocean initial condition. The CGCM, a participant model of APEC Climate Center (APCC) long-lead multi-model ensemble system, has been initialized at each and every month and performed 12-month-lead hindcast for each month during 1980 to 2011. The 12-month-lead hindcast consisted of 2-5 ensembles and this study verified the ensemble averaged hindcast. As for the sea-surface temperature concerns, it remained high level of confidence especially over the tropical Pacific and the mid-latitude central Pacific with slight declining of temporal correlation coefficients (TCC) as lead month increased. The CGCM revealed trustworthy ENSO prediction skills in most of hindcasts, in particular. For atmospheric variables, like air temperature, precipitation, and geopotential height at 500hPa, reliable prediction results have been shown during entire lead time in most of domain, particularly over the equatorial region. Though the TCCs of hindcasted precipitation are lower than other variables, a skillful precipitation forecasts is also shown over highly variable regions such as ITCZ. This study also revealed that there are seasonal and regional dependencies on predictability for each variable and lead.