• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.155-155
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• 2018

Uncertainty in flood forecasting using a coupled meteorological and hydrological model is arisen from various sources, especially the uncertainty comes from the inaccuracy of Quantitative Precipitation Forecasts (QPFs). In order to improve the capability of flood forecast, the uncertainty estimation and mitigation are required to perform. This study is conducted to investigate and reduce such uncertainty. First, ensemble QPFs are generated by using Monte - Carlo simulation, then each ensemble member is forced as input for a hydrological model to obtain ensemble streamflow prediction. Likelihood measures are evaluated to identify feasible member. These members are retained to define upper and lower limits of the uncertainty interval and assess the uncertainty. To mitigate the uncertainty for very short lead time, a blending method, which merges the ensemble QPFs with radar-based rainfall prediction considering both qualitative and quantitative skills, is proposed. Finally, blending bias ratios, which are estimated from previous time step, are used to update the members over total lead time. The proposed method is verified for the two flood events in 2013 and 2016 in the Yeonguol and Soyang watersheds that are located in the Han River basin, South Korea. The uncertainty in flood forecasting using a coupled Local Data Assimilation and Prediction System (LDAPS) and Sejong University Rainfall - Runoff (SURR) model is investigated and then mitigated by blending the generated ensemble LDAPS members with radar-based rainfall prediction that uses McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE). The results show that the uncertainty of flood forecasting using the coupled model increases when the lead time is longer. The mitigation method indicates its effectiveness for mitigating the uncertainty with the increases of the percentage of feasible member (POFM) and the ratio of the number of observations that fall into the uncertainty interval (p-factor).

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.7-7
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• 2016

인도네시아 산불에 의한 연무는 동남아시아 인접한 국가들에 있어서 심각한 환경문제 중 하나이다. 국제적으로 심각한 문제를 야기하는 인도네시아의 산불은 건조기에 강수량이 적게 내리는 극심한 가뭄 조건에서 발생한다. 건조기 강수량을 모니터링 하는 것은 산불 발생 가능성을 예측하기 위해 중요하지만 산불을 사전에 예방하고 영향을 최소화하기에는 부족하다. 따라서 산불에 대한 선제적 사전예방을 위해서는 수개월의 선행예측 기간을 갖는 조기경보 시스템이 절실하다. 따라서 본 연구는 인도네시아 산불에 대한 선제적 대응을 위한 강수량 예측시스템을 개발하고 예측성을 평가하여 동남아시아 지역의 화재 연무 조기경보 시스템의 시제품(Prototype)을 개발하는데 있다. 강수량 예측을 위해서 APEC 기후센터의 계절예측정보의 활용 정도에 따라서 4가지 서로 다른 방법을 통합하여 사용하였다. 예측정보 기반의 방법들로는 대상지역의 강수량 예측을 위해서 대상 지역 상공의 계절예측 강수자료를 보정을 통해 직접적으로 사용하는 SBC (Simple Bias Correction) 방법과 대상 지역 상공의 강수 예측자료를 사용하는 대신에 지역 강수량과 높은 상관 관계를 보이는 다른 지역의 대리변수를 예측인자로 사용하는 MWR (Moving Window Regression) 방법이 있다. 또한 예측자료의 사용 없이 과거자료 기반의 기후지수(Climate Index) 중에서 지체시간을 고려하여 지역 강수량과 높은 상관관계를 갖는 경우 예측에 활용하는 관측자료 기반의 CIR (Climate Index Regression) 방법과 예측기반 MWR과 관측기반의 CIR 방법에서 선정된 예측인자를 동시에 활용하는 ITR (Integrated Time Regression) 방법이 사용되었다. 장기 강수량 예측은 보르네오 섬의 4개 지역에서 3개월 이하의 선행예측기간에 대하여 0.5 이상의 TCC (Temporal Correlation Coefficient)의 값을 보여 양호한 예측성능을 보였다. 예측된 강수량 자료는 위성기반 관측 강수량 및 관측 탄소 배출량 관계에서 결정된 강수량의 임계값과의 비교를 통해 산불발생 가능성으로 환산하였다. 개발된 조기경보 시스템은 산불 발생에 가장 취약한 해당지역의 건조기(8월~10월) 강수량을 4월부터 예측해 산불 연무에 대한 조기경보를 수행한다. 개발된 화재 연무조기경보 시스템은 지속적인 개선을 통해 현장 실효성을 높여 동남아국가 정부의 화재 및 산림관리자들에게 보급함으로써 동남아의 화재 연무로 인한 환경문제 해결에 기여할 수 있으리라 판단된다.

• Atmosphere
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• v.28 no.4
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• pp.403-414
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• 2018

Predictability of Northern Hemisphere blocking in the Korea Meteorological Administration (KMA) Global Data Assimilation and Prediction System (GDAPS) is evaluated for the period of July 2016 to May 2017. Using the operational model output, blocking is defined by a meridional gradient reversal of 500-hPa geopotential height as Tibaldi-Molteni Index. Its predictability is quantified by computing the critical success index and bias score against ERA-Interim data. It turns out that Northwest Pacific blockings, among others, are reasonably well predicted with a forecast lead time of 2~3 days. The highest prediction skill is found in spring with 3.5 lead days, whereas the lowest prediction skill is observed in autumn with 2.25 lead days. Although further analyses are needed with longer dataset, this result suggests that Northern Hemisphere blocking is not well predicted in the operational weather prediction model beyond a short-term weather prediction limit. In the spring, summer, and autumn periods, there was a tendency to overestimate the Western North Pacific blocking.

• Atmosphere
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• v.31 no.5
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• pp.637-656
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• 2021

The United States has been known as the world's major producer of crops such as wheat, corn, and soybeans. Therefore, using meteorological long-term forecast data to project reliable crop yields in the United States is important for planning domestic food policies. The current study is part of an effort to improve the seasonal predictability of regional-scale precipitation across the United States for estimating crop production in the country. For the purpose, a dynamic downscaling method using Weather Research and Forecasting (WRF) model is utilized. The WRF simulation covers the crop-growing period (March to October) during 2000-2020. The initial and lateral boundary conditions of WRF are derived from the Pusan National University Coupled General Circulation Model (PNU CGCM), a participant model of Asia-Pacific Economic Cooperation Climate Center (APCC) Long-Term Multi-Model Ensemble Prediction System. For bias correction of downscaled daily precipitation, empirical quantile mapping (EQM) is applied. The downscaled data set without and with correction are called WRF_UC and WRF_C, respectively. In terms of mean precipitation, the EQM effectively reduces the wet biases over most of the United States and improves the spatial correlation coefficient with observation. The daily precipitation of WRF_C shows the better performance in terms of frequency and extreme precipitation intensity compared to WRF_UC. In addition, WRF_C shows a more reasonable performance in predicting drought frequency according to intensity than WRF_UC.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.371-371
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• 2021

돌발홍수 예경보시스템의 입력자료로 예측강우가 활용된다. 기상청과 환경부에서는 초단기 예보의 목적으로 MAPLE(McGill Algorithm for Precipitation nowcasting and Lagrangian Extrapolation)을 생산하고 있다. MAPLE은 선행 30분까지의 예측품질은 어느 정도 정확하다고 볼 수 있으나 그 이후 특히 3시간 이상이 되면 예측품질이 크게 떨어지는 문제가 있다. 예측강우의 편의보정을 위한 여러 시도들이 있었으나 호우의 규모 및 이동특성을 고려한 사례는 제한적이다. 호우의 이동특성을 고려해야하는 이유로는 첫째, 예측의 특성상 예측강우가 생성되고 편의보정이 이루어지는 시간 동안 호우는 이동을 하기 때문이다. 둘째, 호우가 이동을 하면서 편의보정의 대상이 되는 지역에 적합한 보정계수의 결정이 어렵기 때문이다. 마지막으로 돌발홍수는 장마와 같은 전선형 강수가 아닌 국지성 호우와 같이 빠르게 움직이며 강한 호우를 내리는 강수에 의해 발생하기 때문이다. 본 연구에서는 이러한 문제점을 극복하기 위해 호우의 이동특성을 고려하여 예측강우 보정계수를 결정하고 이를 예측강우에 실시간으로 적용할 수 있는 방법을 제시하였다. 이 과정에서 Backward tracking은 미래에 호우가 도달할 지역(대상지역)으로부터 현재 호우가 위치하는 지역을 추적하는데 이용된다. 추적된 지역에서 보정계수가 결정된다. Forward tracking은 현재 호우가 위치하는 지역으로부터 대상지역을 다시 추적하는데 이용된다. 앞서 결정된 보정계수는 대상지역의 예측강우에 적용된다. 해당 방법론을 2019년에 발생한 주요 호우사상에 실시간 적용하고 평가하였다. 그 결과, Backward-Forward tracking 기반 예측강우 보정방법을 적용한 경우에는 실제 관측된 강우와 매우 유사한 보정결과가 도출됨을 확인되었다.

• Journal of the Korean earth science society
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• v.36 no.2
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• pp.139-157
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• 2015

We investigated the relative errors of satellite-observed Surface Skin Temperature (SST) data caused by sea ice in the northern hemispheric ocean ($30-90^{\circ}N$) during April 16-24, 2003-2014 by intercomparing MODerate Resolution Imaging Spectroradiometer (MODIS) Ice Surface Temperature (IST) data with two types of Atmospheric Infrared Sounder (AIRS) SST data including one with the AIRS/Advanced Microwave Sounding Unit-A (AMSU) and the other with 'AIRS only'. The MODIS temperatures, compared to the AIRS/AMSU, were systematically up to ~1.6 K high near the sea ice boundaries but up to ~2 K low in the sea ice regions. The main reason of the difference of skin temperatures is that the MODIS algorithm used infrared channels for the sea ice detection (i.e., surface classification), while microwave channels were additionally utilized in the AIRS/AMSU. The 'AIRS only' algorithm has been developed from NASA's Goddard Space Flight Center (NASA/GSFC) to prepare for the degradation of AMSU-A by revising part of the AIRS/AMSU algorithm. The SST of 'AIRS only' compared to AIRS/AMSU showed a bias of 0.13 K with RMSE of 0.55 K over the $30-90^{\circ}N$ region. The difference between AIRS/AMSU and 'AIRS only' was larger over the sea ice boundary than in other regions because the 'AIRS only' algorithm utilized the GCM temperature product (NOAA Global Forecast System) over seasonally-varying frozen oceans instead of the AMSU microwave data. Three kinds of the skin temperatures consistently showed significant warming trends ($0.23-0.28Kyr^{-1}$) in the latitude band of $70-80^{\circ}N$. The systematic disagreement among the skin temperatures could affect the discrepancies of their trends in the same direction of either warming or cooling.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.587-601
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• 2020

The ocean circulation was simulated in the East Sea and Ulleungdo-Dokdo region using ROMS (Regional Ocean Modeling System) model. By adopting the East Sea 3 km model and the HYCOM 9 km data, Ulleungdo 1 km model and Ulleungdo-Dokdo 300 m model were constructed with one-way grid nesting method. During the model development, a correction method was proposed for the distortion of the open boundary data which may be caused by the bathymetry data difference between the mother and child models and the interpolation/extrapolation method. Using this model, a super-high resolution ocean circulation with a horizontal resolution of 300 m near the Ulleungdo and Dokdo region was simulated for year 2018. In spite of applying the same conditions except for the initial and boundary data, the numerical models result indicated significantly different characteristics in the study area. Therefore, these results were compared and verified by using the surface current data estimated by satellites altimeter data and temperature data from NIFS (National Institute of Fisheries Science). They suggest that in general, the improvement of the one-way grid nesting with the HYCOM data on RMSE, Mean Bias, Pattern correlation and Vector correlation is greater in 300 m model than in the 1 km model. However, the nesting results of using East Sea 3 km model showed that simulations of the 1 km model were better than 300 m model. The models better resolved distinct ridge/trough structures of isotherms in the vertical sections of water temperature when using the higher horizontal resolution. Furthermore, Karman vortex street was simulated in Ulleungdo-Dokdo 300 m model due to the terrain effect of th islands that was not shown in the Ulleungdo 1 km model.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.1
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• pp.1-10
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• 2010

A regional climate model (RCM) can be a powerful tool to enhance spatial resolution of climate and weather information (IPCC, 2001). In this study we conducted dynamical downscaling using Weather Research and Forecasting Model (WRF) as a RCM in order to obtain high resolution regional agroclimate indices over the Korean Peninsula. For the purpose of obtaining detailed high resolution agroclimate indices, we first reproduced regional weather for the period of March to June, 2002-2008 with dynamic downscaling method under given lateral boundary conditions from NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis data. Normally, numerical model results have shown biases against observational results due to the uncertainties in the modelis initial conditions, physical parameterizations and our physical understanding on nature. Hence in this study, by employing a statistical method, the systematic bias in the modelis results was estimated and corrected for better reproduction of climate on high resolution. As a result of the correction, the systematic bias of the model was properly corrected and the overall spatial patterns in the simulation were well reproduced, resulting in more fine-resolution climatic structures. Based on these results, the fine-resolution agro-climate indices were estimated and presented. Compared with the indices derived from observation, the simulated indices reproduced the major and detailed spatial distributions. Our research shows a possibility to simulate regional climate on high resolution and agro-climate indices by using a proper downscaling method with a dynamical weather forecast model and a statistical correction method to minimize the model bias.

• Atmosphere
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• v.25 no.4
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• pp.639-657
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• 2015

There is an increasing need to improve the air quality over South Korea to protect public health from local and remote anthropogenic pollutant emissions that are in an increasing trend. Here, we evaluate the performance of the WRF-Chem (Weather Research and Forecasting-Chemistry) model in simulating near-surface air quality of major Korean cities, and investigate the impacts of time-varying chemical initial and lateral boundary conditions (IC/BCs) on the air quality simulation using a chemical downscaling technique. The model domain was configured over the East Asian region and anthropogenic MICS-Asia 2010 emissions and biogenic MEGAN-2 emissions were applied with RACM gaseous chemistry and MADE/SORGAM aerosol mechanism. Two simulations were conducted for a 30-days period on April 2010 with chemical IC/BCs from the WRF-Chem default chemical species profiles ('WRF experiment') and the MOZART-4 (Model for OZone And Related chemical Tracers version 4) ('WRF_MOZART experiment'), respectively. The WRF_MOZART experiment has showed a better performance to predict near-surface CO, $NO_2$, $SO_2$, and $O_3$ mixing ratios at 7 major Korean cities than the WRF experiment, showing lower mean bias error (MBE) and higher index of agreement (IOA). The quantitative impacts of the chemical IC/BCs have depended on atmospheric residence time of the pollutants as well as the relative difference of chemical mixing ratios between the WRF and WRF_MOZART experiments at the lateral boundaries. Specifically, the WRF_MOZART experiment has reduced MBE in CO and O3 mixing ratios by 60~80 ppb and 5~10 ppb over South Korea than those in the WRF-Chem default simulation, while it has a marginal impact on $NO_2$ and $SO_2$ mixing ratios. Without using MOZART-4 chemical IC, the WRF simulation has required approximately 6-days chemical spin-up time for the East Asian model domain. Overall, the results indicate that realistic chemical IC/BCs are prerequisite in the WRF-Chem simulation to improve a forecast skill of local air quality over South Korea, even in case the model domain is sufficiently large to represent anthropogenic emissions from China, Japan, and South Korea.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.201-217
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• 2022

The United States is one of the largest producers of major crops such as wheat, maize, and soybeans, and is a major exporter of these crops. Therefore, it is important to estimate the crop production of the country in advance based on reliable long- term weather forecast information for stable crops supply and demand in Korea. The purpose of this study is to improve the seasonal predictability of the agro-climatic indices over the United States by using regional-scale daily temperature. For long-term numerical weather prediction, a dynamical downscaling is performed using Weather Research and Forecasting (WRF) model, a regional climate model. As the initial and lateral boundary conditions of WRF, the global hourly prediction data obtained from the Pusan National University Coupled General Circulation Model (PNU CGCM) are used. The integration of WRF is performed for 22 years (2000-2021) for period from June to December of each year. The empirical quantile mapping, one of the bias correction methods, is applied to the timeseries of downscaled daily mean, minimum, and maximum temperature to correct the model biases. The uncorrected and corrected datasets are referred WRF_UC and WRF_C, respectively in this study. The daily minimum (maximum) temperature obtained from WRF_UC presents warm (cold) biases over most of the United States, which can be attributed to the underestimated the low (high) temperature range. The results show that WRF_C simulates closer to the observed temperature than WRF_UC, which lead to improve the long- term predictability of the temperature- based agro-climatic indices.