• 대기
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• 제32권3호
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• pp.235-246
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• 2022

A large number of MJO skill metrics and process-oriented MJO simulation metrics have been developed by previous studies including the MJO Working Group and Task Force. To assess models' successes and shortcomings in the MJO simulation, a standardized set of diagnostics with the additional set of dynamics-oriented diagnostics are applied. The Global Coupled (GC) model developed for the operation of the climate prediction system is used with the comparison between the GC2 and GC3.1. Two GC models successfully capture three-dimensional dynamic and thermodynamic structure as well as coherent eastward propagation from the reference regions of the Indian Ocean and the western Pacific. The low-level moisture convergence (LLMC) ahead of the MJO deep convection, the low-level westerly and easterly associated with the coupled Rossby-Kelvin wave and the upper-level divergence are simulated successfully. The GC3.1 model simulates a better three-dimensional structure of MJO and thus reproduces more realistic eastward propagation. In GC2, the MJO convection following the LLMC near and east of the Maritime Continent is much weaker than observation and has an asymmetric distribution of both low and upper-level circulation anomalies. The common shortcomings of GC2 and GC3.1 are revealed in the shorter MJO periods and relatively weak LLMC as well as convective activity over the western Indian Ocean.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.137-137
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• 2021

In this study we aimed to enhance streamflow prediction skill of a land-surface hydrological model, WRF-Hydro, over one of the snow dominated catchments lies in Himalayan mountainous range, Astore. To assess the response of the Himalayan river flows to climate change is complex due to multiple contributors: precipitation, snow, and glacier melt. WRF-Hydro model with default glacier module lacks generating streamflow in summer period but recently developed WRF-Hydro-CROCUS model overcomes this issue by melting snow/ice from the glaciers. We showed that by implementing WRF-Hydro-CROCUS model over Astore the results were significantly improved in comparison to WRF-Hydro with default glacier module. To constraint the model with the observed streamflow we chose 17 sensitive parameters of WRF-Hydro, which include groundwater parameters, surface runoff parameters, channel parameters, soil parameters, vegetation parameters and snowmelt parameters. We used Dynamically Dimensioned Search (DDS) method to calibrate the daily streamflow with the Nash-Sutcliffe efficiency (NSE) being greater than 0.7 both in calibration (2009-2010) and validation (2011-2013) period. Based on the number of iterations per parameter, we found that the parameters related to channel and runoff process are most sensitive to streamflow. The attempts to address the responses of the streamflows to climate change are still very weak and vague especially northwest Himalayan Part of Pakistan and this study is one of a few successful applications of process-based land-surface hydrologic model over this mountainous region of UIB that can be utilized to have an in-depth understanding of hydrological responses of climate change.

• 한국대기환경학회지
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• 제28권3호
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• pp.261-272
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• 2012

In this study, climate variability was predicted by the Weather Research and Forecasting (WRF) model under two different scenarios (current trends scenario; SC1 and managed scenario; SC2) for future urban growth over the Seoul metropolitan area (SMA). We used the urban growth model, SLEUTH (Slope, Land-use, Excluded, Urban, Transportation, Hill-Shade) to predict the future urban growth in SMA. As a result, the difference of urban ratio between two scenarios was the maximum up to 2.2% during 50 years (2000~2050). Also, the results of SLEUTH like this were adjusted in the Weather Research and Forecasting (WRF) model to analysis the difference of the future climate for the future urbanization effect. By scenarios of urban growth, we knew that the significant differences of surface temperature with a maximum of about 4 K and PBL height with a maximum of about 200 m appeared locally in newly urbanized area. However, wind speeds are not sensitive for the future urban growth in SMA. These results show that we need to consider the future land-use changes or future urban extension in the study for the prediction of future climate changes.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.112-115
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• 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
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.125-128
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• 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.

• 한국관개배수논문집
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• 제21권1호
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• pp.89-98
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• 2014

This study is to evaluate the future potential impact of climate change on soil erosion loss in a metropolitan area using Revised Universal Soil Loss Equation(RUSLE) with land use information of the Ministry of Environment and rainfall data for present and future years(30-year period). The spatial distribution map of vulnerable areas to soil erosion was prepared to provide the basis information for soil conservation and long-term land use planning. For the future climate change scenario, the MIROC3.2 HiRes A1B($CO_2720ppm$ level 2100) was downscaled for 2040-2069(2040s) and 2070-2099(2080s) using the stochastic weather generator(LARS-WG) with average rainfall data during past 30 years(1980-2010, baseline period). By applying the climate prediction to the RUSLE, the soil erosion loss was evaluated. From the results, the soil erosion loss showed a general tendency to increase with rainfall intensity. The soil loss increased up to 13.7%(55.7 ton/ha/yr) in the 2040s and 29.8%(63.6 ton/ha/yr) in the 2080s based on the baseline data(49.0 ton/ha/yr).

• 대한설비공학회:학술대회논문집
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• 대한설비공학회 2006년도 하계학술발표대회 논문집
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• pp.789-794
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• 2006

The potential impacts of climate change on heating and cooling energy demand were investigated by means of transient building energy simulations and hourly weather data scenarios for Inchon. Future trends for the 21 st century was assessed based oil climate change scenarios with 7 global climate models(GCMs), We constructed hourly weather data from monthly temperatures and total incident solar radiation ($W/m^2$) and then simulated heating and cooling load by Trnsys 16 for Inchon. For 2004-2080, the selected scenarios made by IPCC foresaw a $3.7-5.8^{\circ}C$rise in mean annual air temperature. In 2004-2080, the annual cooling load for a apartment with internal heat gains increased by 75-165% while the heating load fell by 52-71%. Our analysis showed widely varying shifts in future energy demand depending on the season. Heating costs will significantly decrease whereas more expensive electrical energy will be needed of air conditioning during the summer.

• 생태와환경
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• 제54권1호
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• pp.71-77
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• 2021

Using data from flies collected with pitfall traps in 365 forests on a nationwide scale in Korea, the abundance and distribution changes of two families (Phoridae and Lauxaniidae) in Korean forests were predicted at the genus level according to two climate change scenarios: RCP 4.5 and RCP 8.5. The most suitable temperature for the 17 major genera was estimated using a weighted average regression model. Stichillus and Anevrina displayed the lowest optimum temperature with 7.6℃ and 8.5℃ in annual mean temperature, respectively, whereas Chonocephalus had the highest optimum temperature with 12.1℃. Among thirty genera, seven genera (four from Phoridae and three from Lauxaniidae), which showed their abundance in a bell-type or linear pattern along the temperature gradient, were used for predicting the distribution changes according to the future climate change scenarios. All the taxa of this study are expected to decrease in abundance and distribution as a function of temperature increase. Moreover, cold-adapted taxa were found to be more affected than warm-adapted taxa.

• 한국농공학회논문집
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• 제65권1호
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• pp.1-13
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• 2023

Various techniques for bias correction and statistical downscaling have been developed to overcome the limitations related to the spatial and temporal resolution and error of climate change scenario data required in various applied research fields including agriculture and water resources. In this study, the characteristics of three different statistical dowscaling methods (i.e., SQM, SDQDM, and BCSA) provided by AIMS were summarized, and climate change scenarios produced by applying each method were comparatively evaluated. In order to compare the average rainfall characteristics of the past period, an index representing the average rainfall characteristics was used, and the reproducibility of extreme weather conditions was evaluated through the abnormal climate-related index. The reproducibility comparison of spatial distribution and variability was compared through variogram and pattern identification of spatial distribution using the average value of the index of the past period. For temporal reproducibility comparison, the raw data and each detailing technique were compared using the transition probability. The results of the study are presented by quantitatively evaluating the strengths and weaknesses of each method. Through comparison of statistical techniques, we expect that the strengths and weaknesses of each detailing technique can be represented, and the most appropriate statistical detailing technique can be advised for the relevant research.

• 정보처리학회 논문지
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• 제13권4호
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• pp.199-207
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• 2024

지역 저수지들은 농업용수 공급의 중요한 수원공으로 가뭄과 같은 극단적 기후 조건을 대비하여 안정적인 저수율 관리가 필수적이다. 저수율 예측은 국지적 강우와 같은 지역적 기후 특성뿐만 아니라 작부시기를 포함하는 계절적 요인 등에 크게 영향을 받기 때문에 적절한 예측 모델을 선정하는 것만큼 입/출력 데이터 간 상관관계 파악이 무엇보다 중요하다. 이에 본 연구에서는 1991년부터 2022년까지의 전라북도 400여 개 저수지의 광범위한 다변량 데이터를 활용하여 각 저수지의 복잡한 수문학·기후학적 환경요인을 포괄적으로 반영한 저수율 예측 모델을 학습 및 검증하고, 각 입력 특성이 저수율 예측 성능에 미치는 영향력을 분석하고자 한다. 신경망 구조에 따른 저수율 예측 성능 개선이 아닌 다변량의 입력 데이터와 예측 성능 간의 상관관계에 초점을 맞추기 위하여 실험에 사용된 예측 모델로 합성곱신경망 또는 순환신경망과 같은 복잡한 형태가 아닌 완전연결계층, 배치정규화, 드롭아웃, 활성화 함수 등의 조합으로 구성된 기본적인 순방향 신경망을 채택하였다. 추가적으로 대부분의 기존 연구에서는 하루 단위의 단기 예측 성능만을 제시하고 있으며 이러한 단기 예측 방식은 10일, 한 달 단위 등 중장기적 예측이 필요한 실무환경에 적합하지 않기 때문에, 본 연구에서는 하루 단위 예측값을 다음 입력으로 사용하는 재귀적 방식을 통해 최대 한 달 뒤 저수율 예측 성능을 측정하였다. 실험을 통해 예측 기간에 따른 성능 변화 양상을 파악하였으며, Ablation study를 바탕으로 예측 모델의 각 입력 특성이 전체 성능에 끼치는 영향을 분석하였다.