기후변화 시나리오 자료의 불확실성 고려를 위한 대표 GCM 선정기법 개발

정임국,음형일,이은정,박지훈,조재필 한국농공학회 2018 韓國農工學會誌 : 전원과 자원 Vol.60 No.3

바누아투 장기기후분석을 위한 관측자료 갭필링 알고리즘 비교 및 적용

정임국,전종안,이성규,박경원,김지현,함제희,전정빈 한국기상학회 2023 한국기상학회 학술대회 논문집 Vol.2023 No.10

통계적 공간상세화 기법의 시공간적 강우분포 재현성 비교평가

정임국,황세운,조재필 한국농공학회 2023 한국농공학회논문집 Vol.65 No.1

Various techniques for bias correction and statistical downscaling have been developed to overcome the limitations related to the spatial and temporalresolution 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 climatechange scenarios produced by applying each method were comparatively evaluated. In order to compare the average rainfall characteristics of the pastperiod, an index representing the average rainfall characteristics was used, and the reproducibility of extreme weather conditions was evaluated throughthe abnormal climate-related index. The reproducibility comparison of spatial distribution and variability was compared through variogram and patternidentification of spatial distribution using the average value of the index of the past period. For temporal reproducibility comparison, the raw data andeach detailing technique were compared using the transition probability. The results of the study are presented by quantitatively evaluating the strengthsand weaknesses of each method. Through comparison of statistical techniques, we expect that the strengths and weaknesses of each detailing techniquecan be represented, and the most appropriate statistical detailing technique can be advised for the relevant research.

기후변화 시나리오 자료의 불확실성 고려를 위한 대표 GCM 선정기법 개발

정임국 ( Jung Imgook ),음형일 ( Eum Hyung-il ),이은정 ( Lee Eun-jeong ),박지훈 ( Park Jihoon ),조재필 ( Cho Jaepil ) 한국농공학회 2018 한국농공학회논문집 Vol.60 No.5

It is necessary to select the appropriate global climate model (GCM) to take into account the impacts of climate change on integrated water management. The objective of this study was to develop the selection technique of representative GCMs for uncertainty in climate change scenario. The selection technique which set priorities of GCMs consisted of two steps. First step was evaluating original GCMs by comparing with grid-based observational data for the past period. Second step was evaluating whether the statistical downscaled data reflect characteristics for the historical period. Spatial Disaggregation Quantile Delta Mapping (SDQDM), one of the statistical downscaling methods, was used for the downscaled data. The way of evaluating was using explanatory power, the stepwise ratio of the entire GCMs by Expert Team on Climate Change Detection and Indices (ETCCDI) basis. We used 26 GCMs based on CMIP5 data. The Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios were selected for this study. The period for evaluating reproducibility of historical period was 30 years from 1976 to 2005. Precipitation, maximum temperature, and minimum temperature were used as collected climate variables. As a result, we suggested representative 13 GCMs among 26 GCMs by using the selection technique developed in this research. Furthermore, this result can be utilized as a basic data for integrated water management.

불균형 두 집단의 매칭방법 제안

정임국(Imgook Jung),노윤환(Yunhwan Noh),조영석(Youngseuk Cho) 한국데이터정보과학회 2018 한국데이터정보과학회지 Vol.29 No.3

관찰연구 (observational study)에서 사건이 발생한 관측데이터와 사건이 발생하지 않은 관측데이터는 연구 참여 이전에 다른 성향의 데이터일 가능성이 높고, 표본선택 편의 (sample selection bias)의 발생 가능성이 높아지게 된다. 또한 관심 있는 사건이 발생한 관측데이터와 그렇지 않은 관측데이터 수의 불일치가 일어날 가능성이 매우 높다. 이러한 불균형을 해결하는 방법으로 성향점수매칭(propensity score matching: PSM)이 사용되고 있다. 본 논문은 표본선택 편의와 관측데이터 수의 불균형을 해결하기 위해 새로운 방법을 제안하고 그 결과를 비교하고자 한다. In this article, we propose a statistical method to find the equivalent group in observational data by using conversion score. In observational study, treatment group and control group are likely to be different groups before research participation. Thus the difference makes rise of selection bias occurrence possibility. In addition, selection bias makes difference between treatment group and control group. One of the methods to overcome the imbalance is propensity score matching (PSM). For case analysis, we use the 2014 traffic accident data.

한반도지역 가뭄 모니터링 활용을 위한 위성강우 편의보정

박지훈,정임국,박경원 대한원격탐사학회 2018 大韓遠隔探査學會誌 Vol.34 No.6

Drought monitoring is the important system for disasters by climate change. To perform this, it is necessary to measure the precipitation based on satellite rainfall estimation. The data developed in this study provides two kinds of satellite data (raw satellite data and bias-corrected satellite data). The spatial resolution of satellite data is 10 km and the temporal resolution is 1 day. South Korea was selected as the target area, and the original satellite data was constructed, and the bias-correction method was validated. The raw satellite data was constructed using TRMM TMPA and GPM IMERG products. The GRA-IDW was selected for bias-correction method. The correlation coefficient of 0.775 between 1998 and 2017 is relatively high, and TRMM TMPA and GPM IMERG 10 km daily rainfall correlation coefficients are 0.776 and 0.753, respectively. The BIAS values were found to overestimate the raw satellite data over observed data. By using the technique developed in this study, it is possible to provide reliable drought monitoring to Korean peninsula watershed. It is also a basic data for overseas projects including the un-gaged regions. It is expected that reliable gridded data for end users of drought management. 가뭄감시는 기후변화로 인해 빈번히 발생하는 자연재해를 저감하기 위해 필요한 중요한 요소 중의 하나이다. 한반도 지역의 가뭄감시를 수행하기 위해서는 위성기반 강수량을 관측하는 것이 필요하다. 본 연구에서는 위성기반의 원시위성강우자료와 편의보정한 위성자료를 이용하여 위성기반 강수량의 정확도를 확인하였다. 서로 다른 공간/시간 해상도를 가지는 원시위성자료(TRMM TMPA, GPM IMERG)를 10 km로 재격자화하고, 일단위로 변환하였다. 최종적으로 원시위성강우의 표준 시간대를 한반도 표준시(GMT+9)로 변환하여 데이터베이스를 구축하였다. 한반도를 대상지역으로 선정하여, 지상관측자료와 검증을 실시하였다. 편의보정 기법은 GRA-IDW 기법을 선정하여 수행하였다. 먼저 원시위성자료를 검증한 결과를 살펴보면, 상관계수는1998년부터 2017년까지 0.775로 비교적 정확도가 높게 나왔으며, TRMM TMPA, GPM IMERG 각각의 10 km 일강수량 상관계수값은 0.776, 0.753으로 크게 차이 나지 않았다. BIAS값은 원시위성자료 값이 지상관측자료보다 과대추정하는 것으로 나타났다. 편의보정한 위성자료를 검증한 결과를 살펴보면, 상관계수와 RMSE가 편의보정 전보다 개선된 값을 보여주고 있다. 본 연구에서 검증한 위성강우자료는 가뭄감시시스템의 기초자료로 충분히 활용할 수 있으며, 향후 미계측지역의 가뭄관리 의사결정을 위한 격자자료로 활용할 수 있을 것으로 판단된다.

AIMS 플랫폼을 통한 프로그램 사용자에서 개발자료의 변화 및 참여

조재필,정임국,이준혁,이승수,Jo, Jae-Pil,Jeong, Im-Guk,Lee, Jun-Hyeok,Lee, Seung-Su 한국수자원학회 2019 물과 미래(한국수자원학회지) Vol.52 No.6

TIGGE/S2S 기반 중장기 토양수분 예측 및 검증

신용희,정임국,이현주,신용철 한국농공학회 2019 한국농공학회논문집 Vol.61 No.1

Developing reliable soil moisture prediction techniques at agricultural regions is a pivotal issue for sustaining stable crop productions. In this study,a physically-based SWAP(Soil-Water-Atmosphere-Plant) model was suggested to estimate soil moisture dynamics at the study sites. ROSETTA was alsointegrated to derive the soil hydraulic properties( , ,  ,  , ) as the input variables to SWAP based on the soil information(Sand, Silt andClay-SSC, %). In order to predict the soil moisture dynamics in future, the mid-term TIGGIE(THORPEX Interactive Grand Global Ensemble) andlong-term S2S(Subseasonal to Seasonal) weather forecasts were used, respectively. Our proposed approach was tested at the six study sites ofRDA(Rural Development Administration). The estimated soil moisture values based on the SWAP model matched the measured data with the statisticsof Root Mean Square Error(RMSE: 0.034∼0.069) and Temporal Correlation Coefficient(TCC: 0.735∼0.869) for validation. When we predicted themid-/long-term soil moisture values using the TIGGE(0∼15 days)/S2S(16∼46 days) weather forecasts, the soil moisture estimates showed lessvariations during the TIGGE period while uncertainties were increased for the S2S period. Although uncertainties were relatively increased based onthe increased leading time of S2S compared to those of TIGGE, these results supported the potential use of TIGGE/S2S forecasts in evaluatingagricultural drought. Our proposed approach can be useful for efficient water resources management plans in hydrology, agriculture, etc.

도로위의 기상요인이 교통사고에 미치는 영향 - 부산지역을 중심으로 -

이경준,정임국,노윤환,윤상경,조영석,Lee, Kyeongjun,Jung, Imgook,Noh, Yunhwan,Yoon, Sanggyeong,Cho, Youngseuk 한국데이터정보과학회 2015 한국데이터정보과학회지 Vol.26 No.3

Them traffic accidents have been increased every year due to increasing of vehicles numbers as well as the gravitation of the population. The carelessness of drivers, many road weather factors have a great influence on the traffic accidents. Especially, the number of traffic accident is governed by precipitation, visibility, humidity, cloud amounts and temperature. The purpose of this paper is to analyse the effect of road weather factors on traffic accident. We use the data of traffic accident, AWS weather factors (precipitation, existence of rainfall, temperature, wind speed), time zone and day of the week in 2013. We did statistical analysis using logistic regression analysis and decision tree analysis. These prediction models may be used to predict the traffic accident according to the weather condition. 교통사고는 인구의 증가와 그에 따른 자동차의 증가로 인하여 매년 증가하고 있다. 그러한 교통사고의 원인은 운전자의 부주의뿐만 아니라 도로상의 기상상황에 의해 영향을 받는다. 특히, 강수량, 시계, 습도, 흐림 정도, 기온 등에 의해 많은 교통사고들이 영향을 받는다. 따라서 본 연구는 다양한 기상 요인의 영향 정도에 따른 교통사고 발생 유무의 분석을 목적으로 하였다. 부산 해운대구의 센텀남대로 및 해운대로의 2013년도 교통사고 발생 자료와 지역별 상세 기상 관측 자료인 AWS 기상자료(시간당 강수량, 강수유무, 기온, 풍속), 시간대, 요일을 활용하여 로지스틱 회귀모형 및 의사결정나무모형을 이용하여 분석하였다. 그 결과 기상 요인 중 강수유무와 기온이 교통사고 발생에 영향을 미치는 요인으로 나타났다. 이러한 결과는 도로위의 기상상태에 따른 교통사고의 발생을 예측하는데 유용하게 사용할 수 있을 것이다.

기후변화 적응을 위한 사용자 중심의 기후서비스체계 제안 및 사용자인터페이스 플랫폼 개발

조재필(Cho, Jaepil),정임국(Jung, Imgook),조원일(Cho, Wonil),이은정(Lee, Eun-Jeong),강대인(Kang, Daein),이준혁(Lee, Junhyuk) 한국기후변화학회 2018 한국기후변화학회지 Vol.9 No.1