• 제목/요약/키워드: Seasonal forecasting system

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경험적 분위사상법을 이용한 지역기후모형 기반 미국 강수 및 가뭄의 계절 예측 성능 개선 (Improvement in Seasonal Prediction of Precipitation and Drought over the United States Based on Regional Climate Model Using Empirical Quantile Mapping)

  • 송찬영;김소희;안중배
    • 대기
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    • 제31권5호
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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.

Policy evaluation of the rice market isolation system and production adjustment system

  • Dae Young Kwak;Sukho Han
    • 농업과학연구
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    • 제50권4호
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    • pp.629-643
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    • 2023
  • The purpose of this study was to examine the effectiveness and efficiency of a policy by comparing and analyzing the impact of the rice market isolation system and production adjustment system (strategic crops direct payment system that induces the cultivation of other crops instead of rice) on rice supply, rice price, and government's financial expenditure. To achieve this purpose, a rice supply and demand forecasting and policy simulation model was developed in this study using a partial equilibrium model limited to a single item (rice), a dynamic equation model system, and a structural equation system that reflects the casual relationship between variables with economic theory. The rice policy analysis model used a recursive model and not a simultaneous equation model. The policy is distinct from that of previous studies, in which changes in government's policy affected the price of rice during harvest and the lean season before the next harvest, and price changes affected the supply and demand of rice according to the modeling, that is, a more specific policy effect analysis. The analysis showed that the market isolation system increased government's financial expenditure compared to the production adjustment system, suggesting low policy financial efficiency, low policy effectiveness on target, and increased harvest price. In particular, the market isolation system temporarily increased the price during harvest season but decreased the price during the lean season due to an increase in ending stock caused by increased production and government stock. Therefore, a decrease in price during the lean season may decrease annual farm-gate prices, and the reverse seasonal amplitude is expected to intensify.

지역 파랑 예측시스템과 해양기상 부이의 파랑 특성 비교 연구 (Research on Wind Waves Characteristics by Comparison of Regional Wind Wave Prediction System and Ocean Buoy Data)

  • 유승협;박종숙
    • 한국해양공학회지
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    • 제24권6호
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    • pp.7-15
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    • 2010
  • Analyses of wind wave characteristics near the Korean marginal seas were performed in 2008 and 2009 by comparisons of an operational wind wave forecast model and ocean buoy data. In order to evaluate the model performance, its results were compared with the observed data from an ocean buoy. The model used in this study was very good at predicting the characteristics of wind waves near the Korean Peninsula, with correlation coefficients between the model and observations of over 0.8. The averaged Root Mean Square Error (RMSE) for 48 hrs of forecasting between the modeled and observed waves and storm surges/tide were 0.540 m and 0.609 m in 2008 and 2009, respectively. In the spatial and seasonal analysis of wind waves, long waves were found in July and September at the southern coast of Korea in 2008, while in 2009 long waves were found in the winter season at the eastern coast of Korea. Simulated significant wave heights showed evident variations caused by Typhoons in the summer season. When Typhoons Kalmaegi and Morakot in 2008 and 2009 approached to Korean Peninsula, the accuracy of the model predictions was good compared to the annual mean value.

Monthly rainfall forecast of Bangladesh using autoregressive integrated moving average method

  • Mahmud, Ishtiak;Bari, Sheikh Hefzul;Rahman, M. Tauhid Ur
    • Environmental Engineering Research
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    • 제22권2호
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    • pp.162-168
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    • 2017
  • Rainfall is one of the most important phenomena of the natural system. In Bangladesh, agriculture largely depends on the intensity and variability of rainfall. Therefore, an early indication of possible rainfall can help to solve several problems related to agriculture, climate change and natural hazards like flood and drought. Rainfall forecasting could play a significant role in the planning and management of water resource systems also. In this study, univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) model was used to forecast monthly rainfall for twelve months lead-time for thirty rainfall stations of Bangladesh. The best SARIMA model was chosen based on the RMSE and normalized BIC criteria. A validation check for each station was performed on residual series. Residuals were found white noise at almost all stations. Besides, lack of fit test and normalized BIC confirms all the models were fitted satisfactorily. The predicted results from the selected models were compared with the observed data to determine prediction precision. We found that selected models predicted monthly rainfall with a reasonable accuracy. Therefore, year-long rainfall can be forecasted using these models.

장기예보자료를 활용한 가뭄전망정보 생산 및 평가 (Generation and assessment of drought outlook information using long-term weather forecast data)

  • 소재민;손경환;배덕효
    • 한국수자원학회:학술대회논문집
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    • 한국수자원학회 2016년도 학술발표회
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    • pp.97-97
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    • 2016
  • 가뭄은 홍수와 더불어 매우 심각한 자연재해이며, 그 특성상 광역적이고 장기간 발생함에 따라 구체적인 발생시점, 규모, 범위 등을 규명하기가 어렵다. 다만, 적시에 경보해야 하는 홍수와 달리 진행속도가 느리고 시간적으로 대처할 여유가 있어 진행중 일지라도 초기에 감지한다면 그 피해를 최소화할 수 있다. 미국 등 수문기상 선진국에서는 수문기상 장기예보자료를 활용한 가뭄전망정보 생산 및 제공하고 있으며, 활용성을 검증한바 있다. 국내의 경우 기상청에서는 대기-해양-해빙 모델을 접합한 GloSea5 (Global Seasonal forecasting system version 5) 모델을 도입하였으며, 가뭄예보를 목적으로 장기예보자료 기반의 가뭄전망정보 생산체계를 구축한 바 있다(기상청, 2012; 손경환 등, 2015). 본 연구에서는 장기예보자료 기반의 수문기상 전망정보를 이용하여 2014-15년 가뭄사례에 대한 가뭄감시 및 전망정보를 생산 및 평가하였다. 수문기상전망 정보는 기상청 현업예보 모델인 GloSea5와 지면모델을 이용하여 생산하였으며, 관측자료와 수문전망정보 기반의 가뭄지수를 산정하였다. 매스컴 및 언론 보도 자료부터 2014-15년 가뭄에 대한 행정구역별 피해사례를 수집하였으며, 이를 기반으로 시계열, 지역별 및 통계적(CC, RMSE) 분석을 이용하여 선행시간별 정확도를 평가하였다. 1개월 및 2개월 전망정보의 정확도가 높음을 확인하였으며, 가뭄심도가 심각한 시기의 가뭄상황을 적절히 재현하는 것으로 나타났다.

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앙상블 기반 모델을 이용한 서울시 PM2.5 농도 예측 및 분석 (Prediction and Analysis of PM2.5 Concentration in Seoul Using Ensemble-based Model)

  • 류민지;손상훈;김진수
    • 대한원격탐사학회지
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    • 제38권6_1호
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    • pp.1191-1205
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    • 2022
  • 복잡하고 광범위한 원인을 가진 대기오염물질 중 particulate matter (PM)은 입자의 크기에 따라 분류된다. 그 중 PM2.5는 그 크기가 매우 작아 사람이 흡입하면 인간의 호흡기나 심혈관에 질병을 유발할 수 있다. 이러한 위험에 대비하기 위해서는 국가 중심의 관리와 사전에 예방할 수 있는 모니터링 및 예측이 중요하다. 본 연구는 고농도 미세먼지의 발생이 잦은 서울시의 PM2.5를 local data assimilation and prediction system (LDAPS) 기상 관련 인자 15가지와 aerosol optical depth (AOD), 화학인자 4가지를 독립변수로 하여 앙상블 모델 두 가지 random forest (RF)와 extreme gradient boosting (XGB)로 예측하고자 하였다. 예측에 사용된 두 모델의 성능 평가와 인자 중요도 평가를 수행하였으며, 계절별 모델 분석도 수행하였다. 예측 정확도 결과, RF가 R2 = 0.85, XGB가 R2 = 0.91의 높은 예측 정확도를 보이며 XGB가 RF보다 PM2.5 예측에 적합한 모델임을 확인하였다. 계절별 모델 분석 결과, 봄에 농도가 높은 관측 값과 비교하여 예측 수행이 잘 되었다고 할 수 있다. 본 연구는 다양한 인자를 이용하여 서울시의 PM2.5를 예측하였고, 좋은 성능을 보이는 앙상블 기반의 PM2.5 예측 모델을 구축하였다.

현업 기후예측시스템에서의 지면초기화 적용에 따른 예측 민감도 분석 (Application of Land Initialization and its Impact in KMA's Operational Climate Prediction System)

  • 임소민;현유경;지희숙;이조한
    • 대기
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    • 제31권3호
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    • pp.327-340
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    • 2021
  • In this study, the impact of soil moisture initialization in GloSea5, the operational climate prediction system of the Korea Meteorological Administration (KMA), has been investigated for the period of 1991~2010. To overcome the large uncertainties of soil moisture in the reanalysis, JRA55 reanalysis and CMAP precipitation were used as input of JULES land surface model and produced soil moisture initial field. Overall, both mean and variability were initialized drier and smaller than before, and the changes in the surface temperature and pressure in boreal summer and winter were examined using ensemble prediction data. More realistic soil moisture had a significant impact, especially within 2 months. The decreasing (increasing) soil moisture induced increases (decreases) of temperature and decreases (increases) of sea-level pressure in boreal summer and its impacts were maintained for 3~4 months. During the boreal winter, its effect was less significant than in boreal summer and maintained for about 2 months. On the other hand, the changes of surface temperature were more noticeable in the southern hemisphere, and the relationship between temperature and soil moisture was the same as the boreal summer. It has been noted that the impact of land initialization is more evident in the summer hemispheres, and this is expected to improve the simulation of summer heat wave in the KMA's operational climate prediction system.

GloSea5의 과거기후 모의자료에서 나타난 El Niño와 관련된 동아시아 강수 및 기온 예측성능 (Prediction Skill of East Asian Precipitation and Temperature Associated with El Niño in GloSea5 Hindcast Data)

  • 임소민;현유경;강현석;예상욱
    • 대기
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    • 제28권1호
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    • pp.37-51
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    • 2018
  • In this study, we investigate the performance of Global Seasonal Forecasting System version 5 (GloSea5) in Korea Meteorological Administration on the relationship between El $Ni{\tilde{n}}o$ and East Asian climate for the period of 1991~2010. It is found that the GloSea5 has a great prediction skill of El $Ni{\tilde{n}}o$ whose anomaly correlation coefficients of $Ni{\tilde{n}}o$ indices are over 0.96 during winter. The eastern Pacific (EP) El $Ni{\tilde{n}}o$ and the central Pacific (CP) El $Ni{\tilde{n}}o$ are considered and we analyze for EP El $Ni{\tilde{n}}o$, which is well simulated in GloSea5. The analysis period is divided into the developing phase of El $Ni{\tilde{n}}o$ summer (JJA(0)), mature phase of El $Ni{\tilde{n}}o$ winter (D(0)JF(1)), and decaying phase of El $Ni{\tilde{n}}o$ summer (JJA(1)). The GloSea5 simulates the relationship between precipitation and temperature in East Asia and the prediction skill for the East Asian precipitation and temperature varies depending on the El $Ni{\tilde{n}}o$ phase. While the precipitation and temperature are simulated well over the equatorial western Pacific region, there are biases in mid-latitude region during the JJA(0) and JJA(1). Because the low level pressure, wind, and vertical stream function are simulated weakly toward mid-latitude region, though they are similar with observation in low-latitude region. During the D(0)JF(1), the precipitation and temperature patterns analogize with observation in most regions, but there is temperature bias in inland over East Asia. The reason is that the GloSea5 poorly predicts the weakening of Siberian high, even though the shift of Aleutian low is predicted. Overall, the predictability of precipitation and temperature related to El $Ni{\tilde{n}}o$ in the GloSea5 is considered to be better in D(0)JF(1) than JJA(0) and JJA(1) and better in ocean than in inland region.

중소 인터넷 쇼핑몰을 위한 판매자 재고관리 시스템 설계 및 구현 (The Design and Implementation of a Vendor Managed Inventory System for Smaller Online Shopping Malls)

  • 최오훈;임정은;나홍석;백두권
    • 디지털콘텐츠학회 논문지
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    • 제9권2호
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    • pp.295-303
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    • 2008
  • 인터넷을 통한 전자상거래가 보편화됨에 따라 다품종, 소량 생산품을 취급하는 중소 인터넷 쇼핑몰의 수가 증대되었다. 중소 인터넷 쇼핑몰은 그 특성상 다수의 재고물량을 확보할 수 있는 공간이 부족하다. 따라서 전통적인 재고 관리 방법으로 고객의 요구에 즉각적으로 반응하기 어렵다. 본 논문에서는 판매자의 판매량에 따라 공급업체가 재고량을 조절할 수 있는 VMI를 인터넷 쇼핑몰에 도입한 SOHO-VMI를 제안한다. 제안된 SOHO-VMI는 다수의 공급업자 및 판매자와 상호작용 할 수 있는 M $\times$ N 구조를 지원한다. 그리고 기존 시스템에서 사용하는 EDI 문서와 상호작용을 위해 XML/EDI를 사용하도록 제안하였다. 또한, 판매자의 물품 판매 정보 및 계절적 요인을 고려하여, 공급업체에서 물품생산량 및 유통량을 조절 할 수 있는 물류 통계 예측 알고리즘을 제안하였다.

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기상청 기후예측시스템(GloSea6) - Part 2: 기후모의 평균 오차 특성 분석 (The KMA Global Seasonal forecasting system (GloSea6) - Part 2: Climatological Mean Bias Characteristics)

  • 현유경;이조한;신범철;최유나;김지영;이상민;지희숙;부경온;임소민;김혜리;류영;박연희;박형식;추성호;현승훤;황승언
    • 대기
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    • 제32권2호
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    • pp.87-101
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    • 2022
  • In this paper, the performance improvement for the new KMA's Climate Prediction System (GloSea6), which has been built and tested in 2021, is presented by assessing the bias distribution of basic variables from 24 years of GloSea6 hindcasts. Along with the upgrade from GloSea5 to GloSea6, the performance of GloSea6 can be regarded as notable in many respects: improvements in (i) negative bias of geopotential height over the tropical and mid-latitude troposphere and over polar stratosphere in boreal summer; (ii) cold bias of tropospheric temperature; (iii) underestimation of mid-latitude jets; (iv) dry bias in the lower troposphere; (v) cold tongue bias in the equatorial SST and the warm bias of Southern Ocean, suggesting the potential of improvements to the major climate variability in GloSea6. The warm surface temperature in the northern hemisphere continent in summer is eliminated by using CDF-matched soil-moisture initials. However, the cold bias in high latitude snow-covered area in winter still needs to be improved in the future. The intensification of the westerly winds of the summer Asian monsoon and the weakening of the northwest Pacific high, which are considered to be major errors in the GloSea system, had not been significantly improved. However, both the use of increased number of ensembles and the initial conditions at the closest initial dates reveals possibility to improve these biases. It is also noted that the effect of ensemble expansion mainly contributes to the improvement of annual variability over high latitudes and polar regions.