• 한국환경과학회지
• /
• 제15권12호
• /
• pp.1141-1154
• /
• 2006

Orographic effect is one of the important factors to induce Local circulations and to make atmospheric turbulence, so it is necessary to use the exact topographic data for prediction of local circulations. In order to clarify the sensitivity of the spatial resolution of topography data, numerical simulations using several topography data with different spatial resolution are carried out under stable and unstable synoptic conditions. The results are as follows: 1) Influence of topographic data resolution on local circulation tends to be stronger at simulation with fine grid than that with coarse grid. 2) The hight of mountains in numerical model become mote reasonable with high resolution topographic data, so the orographic effect is also emphasized and clarified when the topographic data resolution is higher. 2) The higher the topographic resolution is, the stronger the mountain effect is. When used topographic data resolution become fine, topography in numerical model becomes closer to real topography. 3) The topographic effect tends to be stronger when atmospheric stability is strong stable. 4) Although spatial resolution of topographic data is not fundamental factor for dramatic improvement of weather prediction accuracy, some influence on small scale circulation can be recognized, especially in fluid dynamic simulation.

• 응용통계연구
• /
• 제21권4호
• /
• pp.705-716
• /
• 2008

본 연구는 한반도 지역의 지상기온에서 나타나는 기후변화시그널의 탐지와 예측을 목적으로 하고 있으며, 일본기상청 전지구 수치모델(MRI/JMA CGCM) 모의실험자료인 통제실험자료(대기 중 $CO_2$ 농도 변화가 없다는 가정 아래 실험된 자료)와 시나리오실험자료($CO_2$ 농도가 4배까지 연 1%씩 증가하는 가정 아래 실험된 자료)를 사용하였다. 수치모델 자료기간은 142년 자료이며, 관측치로 사용되는 ECMWF 재분석자료는 43년 자료이다. 모든 자료는 42개 격자점으로 이루어진 동일한 공간구조로 구성되었다. 베이지안 지문법과 자기회귀과정인 회귀모형(AUTOREG 모형)을 각각 적용하여 격자점별로 탐지 작업을 수행하였다. 탐지 결과가 유의한 격자점에 대하여 2100년까지 예측 작업을 수행하였다.

• 대한원격탐사학회지
• /
• 제40권3호
• /
• pp.269-274
• /
• 2024

High-quality precipitation data are crucial for various industries, including disaster prevention. In South Korea, long-term high-quality data are collected through numerous ground observation stations. However, data between these stations are reprocessed into a grid format using interpolation methods, which may not perfectly match actual precipitation. A prime example of real-time observational grid data globally is the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG) from National Aeronautics and Space Administration (NASA), while in South Korea, ground radar data are more commonly used. GPM and ground radar data exhibit distinct differences due to their respective processing methods. This study aims to analyze the characteristics of GPM and Constant Altitude Plan Position Indicator(CAPPI),representative real-time grid data, by comparing them with ground-observed precipitation data. The study period spans from 2021 to 2022, focusing on hourly data from Automated Synoptic Observing System (ASOS) sites in South Korea. The GPM data tend to underestimate precipitation compared to ASOS data, while CAPPI shows errors in estimating low precipitation amounts. Through this comparative analysis, the study anticipates identifying key considerations for utilizing these data in various applied fields, such as recalculating design rainfall, thereby aiding researchers in improving prediction accuracy by using appropriate data.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2009년도 학술발표회 초록집
• /
• pp.112-115
• /
• 2009

The research of climate change impact in hydrometeorology often relies on climate change information. In this paper, neural networks models such as support vector machine neural networks model (SVM-NNM) and multilayer perceptron neural networks model (MLP-NNM) are proposed statistical downscaling of the monthly precipitation. The input nodes of neural networks models consist of the atmospheric meteorology and the atmospheric pressure data for 2 grid points including $127.5^{\circ}E/35^{\circ}N$ and $125^{\circ}E/35^{\circ}N$, which produced the best results from the previous study. The output node of neural networks models consist of the monthly precipitation data for Seoul station. For the performances of the neural networks models, they are composed of training and test performances, respectively. From this research, we evaluate the impact of SVM-NNM and MLP-NNM performances for the downscaling of the monthly precipitation data. We should, therefore, construct the credible monthly precipitation data for Seoul station using statistical downscaling method. The proposed methods can be applied to future climate prediction/projection using the various climate change scenarios such as GCMs and RCMs.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2009년도 학술발표회 초록집
• /
• pp.125-128
• /
• 2009

The research of climate change impact in hydrometeorology often relies on climate change information. In this paper, neural networks models such as generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM) are proposed statistical downscaling of the daily precipitation. The input nodes of neural networks models consist of the atmospheric meteorology and the atmospheric pressure data for 4 grid points including $127.5^{\circ}E/37.5^{\circ}N$, $127.5^{\circ}E/35^{\circ}N$, $125^{\circ}E/37.5^{\circ}N$ and $125^{\circ}E/35^{\circ}N$, respectively. The output node of neural networks models consist of the daily precipitation data for Seoul station. For the performances of the neural networks models, they are composed of training and test performances, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM performances for the downscaling of the daily precipitation data. We should, therefore, construct the credible daily precipitation data for Seoul station using statistical downscaling method. The proposed methods can be applied to future climate prediction/projection using the various climate change scenarios such as GCMs and RCMs.

• 국제학술발표논문집
• /
• The 6th International Conference on Construction Engineering and Project Management
• /
• pp.738-739
• /
• 2015

The main objective of this study, "Spatial Downscaling of Precipitation from GCMs for Assessing Climate Change over Han River and Imjin River Watersheds", is to carry out over Han River and Imjin River watersheds. To this end, a statistical regression method with MOS (Model Output Statistics) corrections at every downscaling step was developed and applied for downscaling the spatially-coarse Global Climate Model Projections (GCMPs) from CCSM3 and CSIRO with respect to precipitation into 0.1 degree (about 11 km) spatial grid over study regions. The spatially archived hydro-climate data sets such as Willmott, GsMap and APHRODITE datasets were used for MOS corrections by means of monthly climatology between observations and downscaled values. Precipitation values downscaled in this study were validated against ground observations and then future climate simulation results on precipitation were evaluated for the projections.

• 한국농림기상학회지
• /
• 제19권2호
• /
• pp.37-42
• /
• 2017

본 연구에서는 RCP 8.5 기후변화 시나라오를 바탕으로 온량지수와 쾨펜의 기후구분을 통한 우리나라의 기후지대 변화를 살펴 보았다. 그 결과, 온량지수에 의한 기후지대를 구분하였을 경우 21세기 후반으로 갈수록 기온이 증가하여 전국적으로 난온대의 기후특성이 나타날 것으로 예상되었으며, 쾨펜의 기후지대 구분에서는 기온의 꾸준한 증가와 강수량의 연중 빈도 차이에 의해 Cfa와 Cwa의 기후특성이 주로 나타날 것으로 예상된다.

• 한국농공학회논문집
• /
• 제61권1호
• /
• pp.31-44
• /
• 2019

The main objective of this research is to develop a hydroclimate drought index (HCDI) using the gridded climate data inputs in a Variable Infiltration Capacity (VIC) modeling platform. Typical drought indices, including, Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Self-calibrated Palmer Drought Severity Index (SC-PDSI) in South Korea are also used and compared. Inverse Distance Weighting (IDW) method is applied to create the gridded climate data from 56 ground weather stations using topographic information between weather stations and the respective grid cell ($12km{\times}12km$). R statistical software packages are used to visualize HCDI in Google Earth. Skill score (SS) are computed to evaluate the drought predictability based on water information derived from the observed reservoir storage and the ground weather stations. The study indicates that the proposed HCDI with the gridded climate data input is promising in the sense that it can help us to predict potential drought extents and to mitigate its impacts in a changing climate. The longer term drought prediction (e.g., 9 and 12 month) capability, in particular, shows higher SS so that it can be used for climate-driven future droughts.

• 한국항해항만학회:학술대회논문집
• /
• 한국항해항만학회 2009년도 공동학술대회
• /
• pp.449-451
• /
• 2009

지구온난화에 따른 해수면 상승과 태풍 강도의 증가는 연안역에 밀집한 주거 및 산업공간을 위협하는 요소로 최근 그 연구가 활발하게 진행되어 오고 있다. 본 연구에서는 안전한 해상교통 및 폭풍해일과 파랑예측을 위해서 반드시 필요한 해상풍에 대한 연구이다. 해상풍은 연안역에서의 자연재해를 유발하는 여러 요소 중에서 중요한 연구과제이나, 현재 가상수치모델에 의한 해상풍 및 해면기압은 시 공간적으로 불충분하다. 따라서, 중규모 기상 모형인 Weather Research and Forecasting(WRF)을 사용하여 우리나라 주변해역을 모두 포함하며, 약 9km 격자로 매일 두 번씩 72시간을 예보하는 해상풍을 산출하는 시스템을 구축하였다. 이어도 해양과학기지와 황해중부부이에서 실측한 해상풍과 검증한 결과 상당히 유의할 만한 결과를 얻었으며, 자료동화을 이용하여 향후에는 보다 정확한 해상풍을 산출할 계획이다.

• 한국농림기상학회:학술대회논문집
• /
• 한국농림기상학회 2016년도 추계 학술발표논문집
• /
• pp.25-28
• /
• 2016

Recently, because of the weather forecasts through the low-resolution data has been limited, the demand of the high-resolution data is sharply increasing. Therefore, in this study, we restore the ultra-high resolution synthetic precipitation and temperature data for 2000-2014 due to small-scale topographic effect using the QPM (Quantitative Precipitation Model)/QTM (Quantitative Temperature Model). First, we reproduce the detailed precipitation and temperature data with 1km resolution using the distribution of Automatic Weather System (AWS) data and Automatic Synoptic Observation System (ASOS) data, which is about 10km resolution with irregular grid over South Korea. Also, we recover the precipitation and temperature data with 1km resolution using the MERRA reanalysis data over North Korea, because there are insufficient observation data. The precipitation and temperature from restored current climate reflect more detailed topographic effect than irregular AWS/ASOS data and MERRA reanalysis data over the Korean peninsula. Based on this analysis, more detailed prospect of regional climate is investigated.