• Atmosphere
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• v.25 no.2
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• pp.353-366
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• 2015

The impact of land and ocean initial condition on coupled general circulation model seasonal predictability is assessed in this study. The CGCM used here is Pusan National University Couple General Circulation Model (PNU CGCM). The seasonal predictability of the surface air temperature and ocean potential temperature for boreal winter are evaluated with 4 different experiments which are combinations of 2 types of land initial conditions (AMI and CMI) and 2 types of ocean initial conditions (DA and noDA). EXP1 is the experiment using climatological land initial condition and ocean initial condition to which the data assimilation technique is not applied. EXP2 is same with EXP1 but used ocean data assimilation applied ocean initial condition. EXP3 is same with EXP1 but AMIP-type land initial condition is used for this experiment. EXP4 is the experiment using the AMIP-type land initial condition and data assimilated ocean initial condition. By comparing these 4 experiments, it is revealed that the impact of data assimilated ocean initial is dominant compared to AMIP-type land initial condition for seasonal predictability of CGCM. The spatial and temporal patterns of EXP2 and EXP4 to which the data assimilation technique is applied were improved compared to the others (EXP1 and EXP3) in boreal winter 2m temperature and sea surface temperature prediction.

• Water Engineering Research
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• v.6 no.2
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• pp.73-89
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• 2005

Two kinds of high resolution GCMs with the same spatial resolutions but with different schemes run by domestic and foreign agencies are used to clarify the usefulness and sensitivity of GCM for water resources applications for Korea. One is AMIP-II (Atmospheric Model Intercomparison Project-II) type GCM simulation results done by ECMWF (European Centre for Medium-Range Weather Forecasts) and the other one is AMIP-I type GCM simulation results done by METRI (Korean Meteorological Research Institute). Observed mean areal precipitation, temperature, and discharge values on 7 major river basins were used for target variables. Monte Carlo simulation was used to establish the significance of the estimator values. Sensitivity analyses were done in accordance with the proposed ways. Through the various tests, discrimination condition is sensitive for the distribution of the data. Window size is sensitive for the data variation and the area of the basins. Discrimination abilities of each nodal value affects on the correct association. In addition to theses sensitivity analyses results, we also noticed some characteristics of each GCM. For Korean water resources, monthly and small window setting analyses are recommended using GCMs.

• Journal of Korea Water Resources Association
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• v.37 no.3
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• pp.173-184
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• 2004

The objective of this study is to examine the usefulness of climate model simulations (GCM) in Korea water resource management. The methods are based on probabilistic measures of the effectiveness of GCM simulations of an indicator variable for discriminating high versus low regional observations of a target variable. The formulation uses the significance probability of the Kolmogorov-Smirnov test for detecting differences between two variables. AMIP-II(Atmospheric Model Intercomparison Project-II) type GCM simulation done by ECMWF(European Centre for Medium-Range Weather Forecasts) was used for indicator variable and observed mean average precipitation(MAP) values on 7 major river basins were used as target variable. Monte Carlo simulation is used to establish the significance of the estimator values. The results show that GCM simulations done by ECMWF are skillful in discriminating the high from the low of the observed MAP for wet season in all seven basins of Korea, but not enough for dry season.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.360-360
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• 2017

이상기상현상의 발생횟수가 지속적으로 증가함에 따라 기상 예측은 국가 재난 관리에 중요한 요소로써 부상하고 있다. 계절예측 또한 재난관리의 한 부분으로, 농업, 에너지, 수자원 그리고 공공보건 등 다양한 분야에서 잠재적 위험을 파악하는데 도움이 되는 보조 자료로 활용이 가능하다. 본 연구에서는 ICON(ICOsahedral-Nonhydrostatic) 모델을 이용하여 2015년 여름철(JJA) 강수를 예측하였다. 2015년은 장마기간을 포함한 여름철 동안 평년대비 약 절반수준(54%)에 그치는 비가 내렸으며, 태풍으로 인한 강수량도 적어 연 강수량이 평년대비 72%로 역대 최저 3위를 기록하였다. 지역별로 보면 제주도와 남해안 지방을 제외한 대부분 지방에서 강수량이 적게 나타났으며, 수도권을 중심으로는 60% 미만의 강수량을 보였다. ICON 모델은 독일 기상청(DWD)과 막스플랑크 연구소(MPI-M)에서 공동 개발하여 현업 운영중인 전 지구 모델로 비정역학 코어를 사용한다. 전 지구를 정 20면체의 삼각형으로 격자화 시켜 모든 격자의 크기가 동일하고, 극점은 1개의 꼭짓점으로 구성되어 CFL(Courant-Friderich-Lewy) 문제가 해소될 수 있다. 또한 hybrid의 병렬구조를 사용하여 전산사용 효율성을 극대화 하는 특징이 있다. 강수의 계절 예측 수행 과정은 다음과 같다. 우선, 계절예측 자료 분석 시 활용할 ICON모델의 기후값을 생산하기 위해 30년(1980년~2009년)간의 AMIP기반 규준실험을 수행한다. 다음으로, SST와 Sea ice의 평년대비 현재 변동량을 계산하고, 이 자료는 모델 적분을 수행할 때 경계 자료로서 활용하게 된다. 계절 예측은 시간 지연기법(Time-lagged method)를 이용한 앙상블예측으로 수행하며, 예측하고자 하는 계절이 시작하기 약 1개원 이전부터 1일 간격으로 전 지구 모델의 초기자료를 다르게 선택하여 총 10개의 앙상블 멤버를 구성한다. 모델의 해상도는 수평 40km, 수직 90개 층으로 구성하였으며, 적분이 완료되면 AMIP기반 실험을 통해 모의된 기후값을 토대로 예측된 계절전망 자료의 변동성을 분석한다.

• Journal of the Korean earth science society
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• v.43 no.6
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• pp.691-711
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• 2022

The state-of-the-art Earth system model as a virtual Earth is required for studies of current and future climate change or climate crises. This complex numerical model can account for almost all human activities and natural phenomena affecting the atmosphere of Earth. The Unified Model (UM) from the United Kingdom Meteorological Office (UK Met Office) is among the best Earth system models as a scientific tool for studying the atmosphere. However, owing to the expansive numerical integration cost and substantial output size required to maintain the UM, individual research groups have had to rely only on supercomputers. The limitations of computer resources, especially the computer environment being blocked from outside network connections, reduce the efficiency and effectiveness of conducting research using the model, as well as improving the component codes. Therefore, this study has presented detailed guidance for installing a new version of the UM on high-performance parallel computers (Linux clusters) owned by individual researchers, which would help researchers to easily work with the UM. The numerical integration performance of the UM on Linux clusters was also evaluated for two different model resolutions, namely N96L85 (1.875° ×1.25° with 85 vertical levels up to 85 km) and N48L70 (3.75° ×2.5° with 70 vertical levels up to 80 km). The one-month integration times using 256 cores for the AMIP and CMIP simulations of N96L85 resolution were 169 and 205 min, respectively. The one-month integration time for an N48L70 AMIP run using 252 cores was 33 min. Simulated results on 2-m surface temperature and precipitation intensity were compared with ERA5 re-analysis data. The spatial distributions of the simulated results were qualitatively compared to those of ERA5 in terms of spatial distribution, despite the quantitative differences caused by different resolutions and atmosphere-ocean coupling. In conclusion, this study has confirmed that UM can be successfully installed and used in high-performance Linux clusters.