• 한국수자원학회논문집
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• 제50권8호
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• pp.563-577
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• 2017

기후변화로 인해 강수의 불확실성이 증가하는 현 시점에서 효율적인 물 관리를 위한 계절예측 및 기상 예보의 활용은 필수적이다. 본 연구에서는 기상청에서 2014년 6월부터 시행하고 있는 범주형 확률장기예보를 Hit Rate, Reliability Diagram, Relative Operating Curve (ROC)의 평가지표를 활용하여 예측력을 검증하였고, 추가적으로 확률예보를 활용하여 정량적인 예측 강수량을 생산하는 기법을 제안하였다. 확률장기예보의 예측력 검증결과 최대 48%의 예측력을 갖는 것을 확인할 수 있었다. 확률예보를 활용하여 예측 강수량을 추정한 결과, 정량적으로 관측 자료와 유사하게 모의되는 것을 확인할 수 있었으며 예측 적합도 평가결과 100%의 정확도를 가진 예보의 경우 최대 0.98, 실제 예보의 경우 최대 0.71의 상관계수를 보였다. 본 연구에서 제안하는 확률예보를 활용한 예측 강수량 추출기법은 강수의 불확실성을 고려한 물 관리를 가능하게 해줄 것으로 판단되며 효율적인 수자원 장기 이수계획 및 저수지 운영의 의사결정지원 등에 활용 가능할 것으로 기대된다.

• 대기
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• 제28권4호
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• pp.509-520
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• 2018

The performance of the newly designed Pusan National University Coupled General Circulation Model (PNU CGCM) Ensemble Forecast System which produce 40 ensemble members for 12-month lead prediction is evaluated and analyzed in terms of boreal winter temperature over South Korea (S. Korea). The influence of ensemble size on prediction skill is examined with 40 ensemble members and the result shows that spreads of predictability are larger when the size of ensemble member is smaller. Moreover, it is suggested that more than 20 ensemble members are required for better prediction of statistically significant inter-annual variability of wintertime temperature over S. Korea. As for the ensemble average (ENS), it shows superior forecast skill compared to each ensemble member and has significant temporal correlation with Automated Surface Observing System (ASOS) temperature at 99% confidence level. In addition to forecast skill for inter-annual variability of wintertime temperature over S. Korea, winter climatology around East Asia and synoptic characteristics of warm (above normal) and cold (below normal) winters are reasonably captured by PNU CGCM. For the categorical forecast with $3{\times}3$ contingency table, the deterministic forecast generally shows better performance than probabilistic forecast except for warm winter (hit rate of probabilistic forecast: 71%). It is also found that, in case of concentrated distribution of 40 ensemble members to one category out of the three, the probabilistic forecast tends to have relatively high predictability. Meanwhile, in the case when the ensemble members distribute evenly throughout the categories, the predictability becomes lower in the probabilistic forecast.

• 대기
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• 제34권1호
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• pp.55-67
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• 2024

This paper aims to provide a detailed introduction to the concept of the Ratio of Predictable Component (RPC) and the Signal-to-Noise Paradox. Then, we derive insights from them by exploring the paradoxical features by conducting a seasonal and regional analysis through ensemble expansion in KMA's climate prediction system (GloSea). We also provide an explanation of the ensemble generation method, with a specific focus on stochastic physics. Through this study, we can provide the predictability limits of our forecasting system, and find way to enhance it. On a global scale, RPC reaches a value of 1 when the ensemble is expanded to a maximum of 56 members, underlining the significance of ensemble expansion in the climate prediction system. The feature indicating RPC paradoxically exceeding 1 becomes particularly evident in the winter North Atlantic and the summer North Pacific. In the Siberian Continent, predictability is notably low, persisting even as the ensemble size increases. This region, characterized by a low RPC, is considered challenging for making reliable predictions, highlighting the need for further improvement in the model and initialization processes related to land processes. In contrast, the tropical ocean demonstrates robust predictability while maintaining an RPC of 1. Through this study, we have brought to attention the limitations of potential predictability within the climate prediction system, emphasizing the necessity of leveraging predictable signals with high RPC values. We also underscore the importance of continuous efforts aimed at improving models and initializations to overcome these limitations.

• 한국지구과학회지
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• 제42권6호
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• pp.605-622
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• 2021

Seasonal forecasting has numerous socioeconomic benefits because it can be used for disaster mitigation. Therefore, it is necessary to diagnose and improve the seasonal forecast model. Moreover, the model performance is partly related to the ocean model. This study evaluated the hindcast performance in the upper ocean of the Global Seasonal Forecasting System version 5-Global Couple Configuration 2 (GloSea5-GC2) using a multivariable integrated evaluation method. The normalized potential temperature, salinity, zonal and meridional currents, and sea surface height anomalies were evaluated. Model performance was affected by the target month and was found to be better in the Pacific than in the Atlantic. An increase in lead time led to a decrease in overall model performance, along with decreases in interannual variability, pattern similarity, and root mean square vector deviation. Improving the performance for ocean currents is a more critical than enhancing the performance for other evaluated variables. The tropical Pacific showed the best accuracy in the surface layer, but a spring predictability barrier was present. At the depth of 301 m, the north Pacific and tropical Atlantic exhibited the best and worst accuracies, respectively. These findings provide fundamental evidence for the ocean forecasting performance of GloSea5.

• 대기
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• 제20권4호
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• pp.399-414
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• 2010

Sand and dust storm in East Asia, so called Asian dust, is a seasonal meteorological phenomenon. Mostly in spring, dust particles blown into atmosphere in the arid area over northern China desert and Manchuria are transported to East Asia by prevailing flows. An Asian dust event occurred on 6-8 May 2007 is chosen to investigate how sensitive the Asian dust transport forecast to the initial condition uncertainties and to interpret the characteristics of sensitivity structures from the viewpoint of dynamics and predictability. To investigate the forecast sensitivities to the initial condition, adjoint sensitivities that calculate gradient of the forecast aspect (i.e., response function) with respect to the initial condition are used. The forecast aspects relevant to Asian dust transports are dry energy forecast error and lower tropospheric pressure forecast error. The results show that the sensitive regions for the dry energy forecast error and the lower tropospheric pressure forecast error are initially located in the vicinity of the trough and then propagate eastward as the surface low system moves eastward. The vertical structures of the adjoint sensitivities for the dry energy forecast error are upshear tilted structures, which are typical adjoint sensitivity structures for extratropical cyclones. Energy distribution of singular vectors also show very similar structures with the adjoint sensitivities for the dry energy forecast error. The adjoint sensitivities of the lower tropospheric pressure forecast error with respect to the relative vorticity show that the accurate forecast of the trough (or relative vorticity) location and intensity is essential to have better forecasts of the Asian dust event. Forecast error for the atmospheric circulation during the dust event is reduced 62.8% by extracting properly weighted adjoint sensitivity perturbations from the initial state. Linearity assumption holds generally well for this case. Dynamics of the Asian dust transport is closely associated with predictability of it, and the improvement in the overall forecast by the adjoint sensitivity perturbations implies that adjoint sensitivities would be beneficial in improving the forecast of Asian dust events.

• 대기
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• 제22권4호
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• pp.415-427
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• 2012

$CO_2$ is the most important trace gas related to climate change. Therefore, understanding surface carbon sources and sinks is important when seeking to estimate the impact of $CO_2$ on the environment and climate. CarbonTracker, developed by NOAA, is an inverse modeling system that estimates surface carbon fluxes using an ensemble Kalman filter with atmospheric $CO_2$ measurements as a constraint. In this study, to investigate the capability of CarbonTracker as an analysis tool for estimating surface carbon fluxes in Asia, an experiment with a nesting domain centered in Asia is performed. In general, the results show that setting a nesting domain centered in Asia region enables detailed estimations of surface carbon fluxes in Asia. From a rank histogram, the prior ensemble spread verified at observational sites located in Asia is well represented with a relatively flat rank histogram. The posterior flux in the Eurasian Boreal and Eurasian Temperate regions is well analyzed with proper seasonal cycles and amplitudes. On the other hand, in tropical regions of Asia, the posterior flux does not differ greatly from the prior flux due to fewer $CO_2$ observations. The root mean square error of the model $CO_2$ calculated by the posterior flux is less than the model $CO_2$ calculated by the prior flux, implying that CarbonTracker based on the ensemble Kalman filter works appropriately for the Asia region.

• 한국농림기상학회지
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• 제24권4호
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• pp.201-217
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• 2022

미국은 전 세계 주요 곡물(밀, 옥수수, 콩 등)의 생산 및 수출 국가로 알려져 있다. 따라서 신뢰할 만한 기상 예측 정보를 바탕으로 해당 지역에 대한 작황을 추정하는 것은 우리나라의 곡물 수급을 안정적으로 계획하기 위해서 중요하다. 본 연구에서는 지역 규모의 일 기온 및 이를 기반으로 산출되는 농업기후지수의 계절 예측성을 향상시키는 데 목적을 두었다. 이를 위해 먼저 역학적 규모축소법을 위한 지역기후모형으로 WRF가 사용되었으며, 해당 모형의 초기 및 측면 경계조건으로 PNU CGCM에서 생산된 시간 별 전지구 예측자료가 활용되었다. WRF의 적분은 22년(2000~2021년) 동안 매년 하반기를 포함하는 기간(6~12월)에 대해 수행되었다. 본 연구에서는 WRF에 의해 모의된 일 평균⋅최저⋅최고기온에 대해 EQM을 적용하여 모형이 갖는 편의를 보정하였다. EQM을 이용하여 보정된(보정되지 않은) 자료들은 WRF_C (WRF_UC)로 명명하였다. WRF_UC는 미국 내 대부분의 지역에서 일 최저기온(최고기온)을 과대(과소) 모의했는데, 이는 저온(고온) 범위를 과소 모의한 특징에서 비롯되었다. WRF_C는 WRF_UC에 나타난 일 평균⋅최저⋅최고기온의 편의가 감소하고 공간분포에 대한 예측성이 향상되었기 때문에 결과적으로 일 기온을 기반으로 산출되는 농업기후지수의 예측성 향상을 유도했다.

• 한국수자원학회논문집
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• 제42권8호
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• pp.673-680
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• 2009

지표수 부족과 수질에 대한 불신 때문에 대체 수자원의 확보가 요구되고 있으며, 유력한 대안으로 강변여과에 관심이 모아지고 있다. 국내 최초의 강변여과는 경남 창원에서 2001년에 시작되었으며, 현재 창원시 수돗물의 100%를 여기에 의존하고 있다. 본 연구는 강변여과 취수장 부근 지하수위를 설명하는 시계열 모형의 개발에 관한 것이다. 연구 대상지역은 창원시 대산면 현장으로 11개 관측정으로부터의 5년간(2003년 1월$\sim$2007년 12월) 지하수위 자료를 이용했다. 지하수위의 장기변동을 알아보기 위해 분단위 자료를 월자료로 변환하고, 결측치를 보완하여 Box-Jenkins 방법에 따라 시계열분석을 실시했다. 대상지역의 지하수위 자료는 계절형 ARIMA 모형으로 잘 설명되는 것이 입증되었다. 본 연구는 향후 증가할 강변여과를 이용한 상수 공급시설의 안정적인 운영을 위해 반드시 필요한 지하수위 예측능력을 확보하기 위한 하나의 원형이 될 것이다.

• 한국지하수토양환경학회지:지하수토양환경
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• 제21권6호
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• pp.36-45
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• 2016

Variability in precipitation due to climate change causes difficulties in securing stable surface water resource, which requires understanding of relation between precipitation and stream discharge. This study simulated stream discharge in a small mountainous forested catchment using antecedent precipitation index (API) models which represent variability of saturation conditions of soil layers depending on rainfall events. During 13 months from May 2015 to May 2016, stream discharge and rainfall were measured at the outlet and in the central part of the watershed, respectively. Several API models with average recession coefficients were applied to predict stream discharge using measured rainfall, which resulted in the best reflection time for API model was 1 day in terms of predictability of stream discharge. This indicates that soil water in riparian zones has fast response to rainfall events and its storage is relatively small. The model can be improved by employing seasonal recession coefficients which can consider seasonal fluctuation of hydrological parameters. These results showed API models can be useful to evaluate variability of streamflow in ungauged small forested watersheds in that stream discharge can be simulated using only rainfall data.

• 대기
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• 제19권2호
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• pp.127-143
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• 2009

This study investigates characteristics of wintertime simulation conducted by METRI AGCM utilizing new radiation parameterization scheme. New radiation scheme is based on the method of Chou et al., and is utilized in the METRI AGCM recently. In order to analyze characteristics of seasonal simulation in boreal winter, hindcast dataset from 1979 to 2005 is produced in two experiments - control run (CTRL) and new model's run (RADI). Also, changes in performance skill and predictability due to implementation of new radiation scheme are examined. In the wintertime simulation, the RADI experiment tends to reduce warm bias in the upper troposphere probably due to intensification of longwave radiative cooling over the whole troposphere. The radiative cooling effect is related to weakening of longitudinal temperature gradient, leading to weaker tropospheric jet in the upper troposphere. In addition, changes in vertical thermodynamic structure have an influence on reduction of tropical precipitation. Moreover, the RADI case is less sensitive to variation of tropical sea surface temperature than the CTRL case, even though the RADI case simulates the mean climate pattern well. It implies that the RADI run does not have significant improvement in seasonal prediction point of view.