• 한국농공학회논문집
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• 제55권4호
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• pp.95-106
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• 2013

It is now generally known that dynamical climate modeling outputs include systematic biases in reproducing the properties of atmospheric variables such as, preciptation and temerature. There is thus, general consensus among the researchers about the need of bias-correction process prior to using climate model results especially for hydrologic applications. Among the number of bias-correction methods, distribution (e.g., cumulative distribution fuction, CDF) mapping based approach has been evaluated as one of the skillful techniques. This study investigates the uncertainty of using various CDF mapping-based methods for bias-correciton in assessing regional climate change Impacts. Two different dynamicailly-downscaled Global Circulation Model results (CCSM and GFDL under ARES4 A2 scenario) using Regional Spectial Model for retrospective peiod (1969-2000) and future period (2039-2069) were collected over the west central Florida. Total 12 possible methods (i.e., 3 for developing distribution by each of 4 for estimating biases in future projections) were examined and the variations among the results using different methods were evaluated in various ways. The results for daily temperature showed that while mean and standard deviation of Tmax and Tmin has relatively small variation among the bias-correction methods, monthly maximum values showed as significant variation (~2'C) as the mean differences between the retrospective simulations and future projections. The accuracy of raw preciptiation predictions was much worse than temerature and bias-corrected results appreared to be more significantly influenced by the methodologies. Furthermore the uncertainty of bias-correction was found to be relevant to the performance of climate model (i.e., CCSM results which showed relatively worse accuracy showed larger variation among the bias-correction methods). Concludingly bias-correction methodology is an important sourse of uncertainty among other processes that may be required for cliamte change impact assessment. This study underscores the need to carefully select a bias-correction method and that the approach for any given analysis should depend on the research question being asked.

• 대기
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• 제30권4호
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• pp.319-333
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• 2020

Aircraft observations constitute one of the major sources of temperature observations which provide three-dimensional information. But it is well known that the aircraft temperature data have warm bias against sonde observation data, and therefore, the correction of aircraft temperature bias is important to improve the model performance. In this study, the algorithm of the bias correction modified from operational KMA (Korea Meteorological Administration) global model is adopted in the preprocessing of aircraft observations, and the effect of the bias correction of aircraft temperature is investigated by conducting the two experiments. The assimilation with the bias correction showed better consistency in the analysis-forecast cycle in terms of the differences between observations (radiosonde and GPSRO (Global Positioning System Radio Occultation)) and 6h forecast. This resulted in an improved forecasting skill level of the mid-level temperature and geopotential height in terms of the root-mean-square error. It was noted that the benefits of the correction of aircraft temperature bias was the upper-level temperature in the midlatitudes, and this affected various parameters (winds, geopotential height) via the model dynamics.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.161-161
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• 2023

Bias correction of values is a necessary step in downscaling coarse and systematically biased global climate models for use in local climate change impact studies. In addition to univariate bias correction methods, many multivariate methods which correct multiple variables jointly - each with their own mathematical designs - have been developed recently. While some literature have focused on the inter-comparison of these multivariate bias correction methods, none have focused extensively on the effect of diverse configurations (i.e., different combinations of input variables to be corrected) of climate variables, particularly high-dimensional ones, on the ability of the different methods to remove biases in uni- and multivariate statistics. This study evaluates the impact of three configurations (inter-variable, inter-spatial, and full dimensional dependence configurations) on four state-of-the-art multivariate bias correction methods in a national-scale domain over South Korea using a gridded approach. An inter-comparison framework evaluating the performance of the different combinations of configurations and bias correction methods in adjusting various climate variable statistics was created. Precipitation, maximum, and minimum temperatures were corrected across 306 high-resolution (0.2°) grid cells and were evaluated. Results show improvements in most methods in correcting various statistics when implementing high-dimensional configurations. However, some instabilities were observed, likely tied to the mathematical designs of the methods, informing that some multivariate bias correction methods are incompatible with high-dimensional configurations highlighting the potential for further improvements in the field, as well as the importance of proper selection of the correction method specific to the needs of the user.

• 한국수자원학회논문집
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• 제53권3호
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• pp.201-214
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• 2020

본 연구에서는 듀얼칼만필터를 이용하여 이중편파 레이더 강우의 편의를 실시간으로 보정할 수 있는 방법을 제안하였다. 듀얼칼만필터는 기존의 칼만필터와 달리 두 개의 시스템(상태추정시스템, 모형추정시스템)이 동시에 가동되면서 실시간으로 상태변수가 예측된다. 강우강도 추정치에 보정계수를 적용함으로써 편의보정이 이루어지며, 보정계수는 듀얼칼만필터의 상태-공간모형에 의해 실시간으로 예측된다. 해당 기법을 2016년 7월에 발생한 지속시간이 긴 호우사상에 대해 적용하고 편의보정 결과를 평가하였다. 먼저, 보정계수는 대부분 1과 2 사이의 값으로 산정되어 지상관측 강우강도보다 레이더 강우강도가 약간 과소추정되는 경향을 보였다. 보정계수에 대한 시계열을 설명할 수 있는 모형으로는 AR(1) 모형이 적합한 것으로 확인되었다. 아울러 듀얼칼만필터로 예측한 보정계수는 관측된 자료를 이용하여 산정한 보정계수와 유사한 경향을 가지는 것으로 나타났다. 칼만필터와의 비교 결과, 보정계수의 변동성이 커질수록 듀얼칼만필터가 칼만필터에 비해 우수한 예측 성능을 가지는 것으로 확인되었다. 본 연구를 통해 강우의 변동성이 크고, 지속시간이 긴 호우사상에 대한 듀얼칼만필터의 적합성이 검증되었다.

• Wind and Structures
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• 제30권4호
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• pp.393-403
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• 2020

One of the growing concerns of the wind energy production is wind ramp events. To improve the wind ramp event forecasts, the nonlinear Kalman filter bias correction method was applied to 24-h wind speed forecasts issued from the WRF model at 70-m height in Zhangbei wind farm, Hebei Province, China for a two-year period. The Kalman filter shows the remarkable ability of improving forecast skill for real-time wind speed forecasts by decreasing RMSE by 32% from 3.26 m s_-1 to 2.21 m s_-1, reducing BIAS almost to zero, and improving correlation from 0.58 to 0.82. The bias correction improves the forecast skill especially in wind speed intervals sensitive to wind power prediction. The fact shows that the Kalman filter is especially suitable for wind power prediction. Moreover, the bias correction method performs well under abrupt weather transition. As to the overall performance for improving the forecast skill of ramp events, the Kalman filter shows noticeable improvements based on POD and TSS. The bias correction increases the POD score of up-ramps from 0.27 to 0.39 and from 0.26 to 0.38 for down-ramps. After bias correction, the TSS score is significantly promoted from 0.12 to 0.26 for up-ramps and from 0.13 to 0.25 for down-ramps.

• 한국수자원학회논문집
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• 제52권9호
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• pp.589-601
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• 2019

최근 수문기상학 분야에서 레이더 강수량을 활용한 응용연구가 활발하게 진행되고 있다. 하지만 레이더 강수량은 경험적인 레이더 반사도-강수강도 관계식을 활용하여 레이더 강수량을 추정하기 때문에 실제 지상에 도달하는 강수량과 정량적인 오차가 필연적으로 발생한다. 따라서 본 연구에서는 레이더 강수량 편의보정을 위하여 Bayesian 추론기법과 일반화 선형모형을 연계하여 불확실성을 고려한 편의보정 매개변수를 산정하였다. 일반화 선형모형을 적용한 레이더 강수량 편의보정 결과는 현재 널리 사용되고 있는 평균보정 기법보다 우수한 통계적 효율기준을 제시하였다. 추가로 지형학적 특성에 따른 편의보정 매개변수의 변동성을 분석하여 고도 및 이격거리에 따른 편의보정 매개변수의 지역화 공식을 제시하였다. 본 연구를 통하여 개발된 레이더 강수량 편의보정 매개변수 산정 및 지역화 결과는 레이더와 관련된 다양한 연구에 활용성이 클 것으로 판단된다.

• 한국농공학회논문집
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• 제57권4호
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• pp.61-71
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• 2015

Quantitative variability of AMSR2 (Advanced Microwave Scanning Radiometer 2) soil moisture data shows that the remotely sensed soil moisture is underestimated during Spring and Winter seasons and is overestimated during Summer and Fall seasons. Therefore the bias correction of the remotely sensed data is essential for the purpose of water resource management. To enhance their applicability, the bias of AMSR2 soil moisture data was corrected using ground observation data at Cheorwon Chuncheon, Suwon, Cheongju, Jeonju, and Jinju sites. Test statistics demonstrated that the correlation coefficient R is improved from 0.107~0.328 to 0.286~0.559 and RMSE is improved from 9.46~14.36 % to 5.38~9.62 %. Bias correction using ground network data improved the applicability of remotely sensed soil moisture data.

• 대기
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• 제28권4호
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• pp.357-367
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• 2018

In this study, the temperature bias of aircraft observation was estimated through comparison with sonde observation prior to developing the temperature bias correction method at the Korea Institute of Atmospheric Prediction Systems (KIAPS). First, we tried to compare aircraft temperature with collocated sonde observations at 0000 UTC on June 22, 2012. However, it was difficult to estimate the temperature bias due to the lack of samples and the uncertainty of the sonde position at high altitudes. Second, we attempted a background innovation comparison for sonde and aircraft using KIAPS Package for Observation Processing (KPOP). The one month averaged background innovation shows the aircraft temperature have a warm bias against sonde for all levels. In particular, there is a globally distinct warm bias about 0.4 K between 200 hPa and 300 hPa corresponding to flight level. Spatially, most of the areas showed the warm bias except for below 300 hPa in some part of China at 0000 and 1200 UTC and below 850 hPa in Australia at 0000 UTC. In general, the temperature bias was larger at 1200 UTC than 0000 UTC. Based on the estimated temperature bias, we have applied the static bias correction method to the aircraft temperature observation. As a result, the warm bias of the aircraft temperature has decreased at most levels, but a slight cold bias has occurred in some areas.

• 대기
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• 제30권2호
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• pp.155-167
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• 2020

In order to produce more detailed and accurate information of river discharge and freshwater discharge, global high-resolution hydrodynamic model (CaMa-Flood) is applied to an operational land surface model of global seasonal forecast system. In addition, bias correction to grid runoff for the hydrodynamic model is attempted. CaMa-Flood is a river routing model that distributes runoff forcing from a land surface model to oceans or inland seas along continentalscale rivers, which can represent flood stage and river discharge explicitly. The runoff data generated by the land surface model are bias-corrected by using composite runoff data from UNH-GRDC. The impact of bias-correction on the runoff, which is spatially resolved on 0.5° grid, has been evaluated for 1991~2010. It is shown that bias-correction increases runoff by 30% on average over all continents, which is closer to UNH-GRDC. Two experiments with coupled CaMa-Flood are carried out to produce river discharge: one using this bias correction and the other not using. It is found that the experiment adapting bias correction exhibits significant increase of both river discharge over major rivers around the world and continental freshwater discharge into oceans (40% globally), which is closer to GRDC. These preliminary results indicate that the application of CaMa-Flood as well as bias-corrected runoff to the operational global seasonal forecast system is feasible to attain information of surface water cycle from a coupled suite of atmospheric, land surface, and hydrodynamic model.

• 대기
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• 제25권2호
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• pp.205-219
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• 2015

In this study, the simulation results of temperature by regional climate model (Reg- CM4) over South Korea were corrected by Hempel et al. (2013)'s method (Hempel method), and evaluated with the observation data of 50 stations from Korea Meteorological Administration. Among the 30 years (1981~2010) of simulation data, 20 years (1981~2000) of simulation data were used as a training data, and the remnant 10 years (2001~2010) data were used for the evaluation of correction. In general, the Hempel method and parametric quantile mapping show a reasonable correction both in mean and extreme climate of temperature. As the results, the systematic underestimation of mean temperature was greatly reduced after bias correction by Hempel method. And the overestimation of extreme climate, such as the number of TN5% and freezing day, was significantly recovered. In addition to that, the Hempel method better preserved the temporal trend of simulated temperature than other bias correction methods, such as the quantile mapping. However, the overcorrection of the extreme climate related to the upper quantile, such as TX5% and hot days, resulted in the exaggeration of the simulation errors. In general, the Hempel method can reduce the systematic biases embedded in the simulation results preserving the temporal trend but it tends to overcorrect the non-linear biases, in particular, extreme climate related to the upper percentile.