남한지역 주요도시의 여름철 기온의 통계적 규모축소와 역학적 규모축소의 비교

이효진,정여민,양유빈 한국자료분석학회 2013 Journal of the Korean Data Analysis Society Vol.15 No.6

Downscaling methods in meteorology are used for obtaining finer grid-scale data from large-scale data. In this study, two different downscaling methods are used and compared by a case study. The first method is statistical downsclaing using a moving window and simple linear regression which is developed by the Asia Pacific Economic Cooperation Climate Center (APCC). The second approach is using the weather research and forecasting (WRF) model, a popular model used for dynamical downscaling. These models are initialized by the APCC CCSM3 as a global climate model (GCM). Summer season (JUN, JUL, AUG, mean of JUN to AUG) temperature prediction from 2001 to 2010 in Seoul and Busan is used for case study. The Wilcoxon signed rank test is applied to give significance to the differences between the observed data and results of the each model. Statistical downscaling results have no differences in both regions during the summer season at the 5% significance level. However, dynamical downscaling results have a significant difference (p-value<0.05) in all cases. 기상학에서의 규모축소법이란 대규모의 성근 격자의 기후 자료를 이용하여 더욱 조밀한 격자의 기후 값을 구하는 방법이다. 본 연구는 통계적 방법과 역학적 방법으로 시행되는 두 가지 규모축소법에 대하여 사례 연구를 통한 비교에 목적이 있다. 첫 번째로 사용된 규모축소법은 APEC기후센터(APCC)에서 개발한 통계적 방법으로 관측 자료와 예측자료사이에 가장 높은 선형상관을 나타내는 지역과 최적 기후변수를 선정하여 회귀모델을 적합하는 방법이다. 다른 하나인 역학적 규모축소 방법은 널리 쓰이는 중규모 모형(weather research and forecasting model)을 사용하여 기후인자들 사이의 물리적 산술식을 이용하여 해상도를 높이는 방법이다. 두 모델에 사용되는 대규모 격자자료는 APCC에서 생산되는 접합모형(community climate system model version3)자료를 사용하였다. 2001년부터 2010년까지 서울과 부산의 여름철 실 관측 온도를 사례연구로 정하여 두 가지 방법을 적용하고 비교하였다. 각 도시의 분석시점은 4가지(6월, 7월, 8월, 여름철평균)로 나누어 시행하였으며, 비모수 대응자료 차이검정법인 Wilcoxon 부호순위검정을 시행하였다. 그 결과, 서울 부산지역의 여름철 규모축소에서는 통계적 방법의 결과가 실 관측치와 유의수준 5%에서 차이가 없는 것으로 나타났으며, 역학적 방법은 모든 경우에 매우 유의한 차이(p-value<0.05)가 있는 것으로 나타났다.

Development of a multimodel‐based seasonal prediction system for extreme droughts and floods: a case study for South Korea

Sohn, Soo‐,Jin,Tam, Chi‐,Yung,Ahn, Joong‐,Bae John Wiley Sons, Ltd. 2013 International journal of climatology Vol.33 No.4

<P><B>Abstract</B></P><P>An experimental, district‐level system was developed to forecast droughts and floods over South Korea to properly represent local precipitation extremes. The system is based on the Asia‐Pacific Economic Cooperation (APEC) Climate Center (APCC) multimodel ensemble (MME) seasonal prediction products. Three‐month lead precipitation forecasts for 60 stations in South Korea for the season of March to May are first obtained from the coarse‐scale MME prediction using statistical downscaling. Owing to the relatively small variance of the MME and regression‐based downscaling outputs, the downscaled MME (DMME) products need to be subsequently inflated. The final station‐scale precipitation predictions are then used to produce drought and flood forecasts on the basis of the Standardized Precipitation Index (SPI).</P><P>The performance of three different inflation schemes was also assessed. Of these three schemes, the method that simply rescales the variance of predicted rainfall to that based on climate records, irrespective of the prediction skill or the DMME variance itself at a particular station, gives the best overall improvement in the SPI predictions. However, systematic biases in the prediction system cannot be removed by variance inflation. This implies that DMME techniques must be further improved to correct the bias in extreme drought/flood predictions. Overall, it is seen that DMME, in conjunction with variance inflation, can predict hydrological extremes with reasonable skill. Our results could inform the development of a reliable early warning system for droughts and floods, which is invaluable to policy makers and stakeholders in agricultural and water management sectors, and so forth and is important for mitigation and adaptation measures. Copyright © 2012 Royal Meteorological Society</P>

NCAM-LAMP를 이용한 고해상도 일단위 기상기후 DB 구축: 일조시간 자료를 중심으로

이수정,이승재,구자섭 한국농림기상학회 2020 한국농림기상학회지 Vol.22 No.3

Shortwave radiation and sunshine hours (SHOUR) are important variables having many applications, including crop growth. However, observational data for these variables have low horizontal resolution, rendering its application to related research and decision making on f arming practices challenging. In the present study, hourly solar radiation data were physically generated using the Land-Atmosphere Modeling Package (LAMP) at the National Center for Agro-Meteorology, and then daily SHOUR fields were calculated through statistical downscaling. After data quality evaluation, including case studies, the SHOUR data were added to the existing publically accessible LAMP daily database. The LAMP daily dataset, newly updated with SHOUR, has been provided operationally as input data to the “Gyeonggi-do Agricultural Drought Prediction System,” which predicts agricultural weather disasters and field crop growth status. 단파 복사와 일조시간은 농작물 재배에 중요한 변수들이다. 그러나 국내에서 제공되는 일사 관측 자료는 수평 해상도가 높지 않아 농업 현장에 활용하기 어렵다. 본 연구에서는 지면대기모델링패키지(LAMP)를 이용하여 시간단위 일사 자료를 물리역학적으로 생산하고, 통계적 다운스케일링을 통해 고해상도 일단위 기상기후 DB를 구축하였다. 현재 이 DB는 품질 평가를 거쳐 농업가뭄 재해와 밭작물의 생육 현황을 진단하고 예측하는 ‘경기도 농업가뭄 예측시스템’의 공식 빅데이터 입력 자료로 활용되고 있다.

회귀크리깅을 이용한 AMSR2 토양수분자료의 다운스케일링

김대선,박노욱,김나리,김광진,이수진,김영호,김지원,신대윤,조영현,이양원 한국지도학회 2017 한국지도학회지 Vol.17 No.2

토양수분은 지면환경에서 일어나는 수문 순환을 이해하기 위한 중요한 기상인자일 뿐만 아니라 가뭄, 홍수, 산불 등과 같은 자연재해와 밀접하게 연관되어 있다. 그러나 위성기반 토양수분 자료는 공간해상도가 매우 떨어져서 국지규모 분석에 직접적 으로 적용하기에는 한계가 있다. 이 연구에서는 마이크로파 위성센서로부터 산출된 토양수분 자료가 가지는 공간해상도의 제약을 완화하기 위하여, 다양한 지면 변수와 공간통계법을 활용한 다운스케일링 기법을 도입하였다. 가장 정교한 다운스케일링 기법으로 평가되는 회귀크리깅을 이 연구를 통하여 토양수분 자료에 처음으로 적용하였다. 우리나라의 2013년과 2014년의 4월부터 10월까지 의 일자별 AMSR2(Advanced Microwave Scanning Radiometer 2) 공간해상도 10km와 25km의 토양수분 자료를 각각 2km와 4km로 다운스케일링한 결과, 고해상도로 다운스케일링된 자료와 저해상도 원자료와의 일관성이 우수하게 유지되어, 다운스케일링 전후의 공간패턴과 자료특성이 잘 보존되는 것을 확인할 수 있었다. 이 연구에서 제시한 다운스케일링 기법은 토양수분뿐만 아니라 여러 기상요소에 적용될 수 있으며, 위성영상이나 모형자료의 공간해상도 한계를 극복하기 위한 방편이 될 수 있을 것으로 기대된다. Soil moisture is an important meteorological factor to understand hydrological circulation and is closely associated with disasters such as drought, flood and wildfire. However, the spatial resolution of satellite-based soil moisture data is too coarse to be applied directly to local analysis. To solve the problem of the restricted spatial resolution of soil moisture data retrieved from microwave satellite sensors, this study presents a downscaling method that combines spatial statistical models with various land surface variables. Regression-kriging, which is known as the most elaborated downscaling technique, was employed to downscaling of the daily soil moisture data from AMSR2 (Advanced Microwave Scanning Radiometer 2) at the resolution of 10km and 25km for the period between April and October, 2013-2014. The downscaled result at the resolution of 2km and 4km showed very good consistency with the original data, which means the spatial patterns and the data properties were well preserved even after downscaling. Our approach will be applied to other meteorological factors and can be a viable option for overcoming the problem of limited spatial resolution in satellite images and numerical model data.

Statistical downscaling methods based on APCC multi‐model ensemble for seasonal prediction over South Korea

Kang, Suchul,Hur, Jina,Ahn, Joong‐,Bae John Wiley Sons, Ltd 2014 International Journal of Climatology Vol.34 No.14

<P><B>ABSTRACT</B></P><P>An investigation was conducted to optimize the application of the multi‐model ensemble (MME) technique for statistical downscaling using 1‐ to 6‐month lead hindcasts obtained from six operational coupled general circulation models (GCMs) participating in the APEC Climate Center (APCC) MME prediction system. Three different statistical downscaling MME methods (SDMMEs) were compared and estimated over South Korea. The study results revealed that under the same number of ensemble members, simple changes in the statistical downscaling method, such as an applicative order or a type of MME, can help to improve the predictability. The first method, the conventional technique, performed MME using data downscaled from the single‐model ensemble means of each GCM (SDMME‐Sm), whereas the second and third methods, newly designed in this study, calculated the simple ensemble mean (SDMME‐Ae) and the weighted ensemble mean (SDMME‐We) after statistical downscaling for each member of all model ensembles. These three methods were applied to predict temperature and precipitation for the 6‐month summer‐fall season over 23 years (1983–2005) at 60 weather stations over South Korea. The predictors were variables from hindcasts integrated by the six coupled GCMs. According to the analysis, both SDMME‐Ae and SDMME‐We showed increased predictability compared with SDMME‐Sm. In particular, SDMME‐We showed more significant improvement in long‐term prediction. In addition, in order to assess the dependence of predictability on the number of downscaled ensemble members and the type of MME, an additional experiment was performed, the results of which revealed that the model performance was closely related to the number of downscaled ensemble members. However, after approximately 30 ensemble members, the predictive skills became rapidly saturated when using the SDMME‐Ae method. SDMME‐We overcame the limited skills that can be achieved by merely increasing the number of downscaled ensemble members, thereby improving the performance.</P>

한반도 미래 기온 변화 예측을 위한 ECHO-G/S 시나리오의 통계적 상세화에 관한 연구

신진호(Jinho Shin),이효신(Hyo-Shin Lee),권원태(Won-Tae Kwon),김민지(Minji Kim) 한국기상학회 2009 대기 Vol.19 No.2

Statistical downscaled surface temperature datasets by employing the cyclostationary empirical orthogonal function (CSEOF) analysis and multiple linear regression method are examined. For evaluating the efficiency of this statistical downscaling method, monthly surface temperature of the ECMWF has been downscaled into monthly temperature having a fine spatial scale of ~20km over the Korean peninsula for the 1973-2000 period. Monthly surface temperature of the ECHOG has also been downscaled into the same spatial scale data for the same period. Comparisons of temperatures between two datasets over the Korean peninsula show that annual mean temperature of the ECMWF is about 2℃ higher than that of the ECHOG. After applying to the statistical downscaling method, the difference of two annual mean temperatures reduces less than 1℃ and their spatial patterns become even close to each other. Future downscaled data shows that annual temperatures in the A1B scenario will increase by 3.5℃ by the late 21st century. The downscaled data are influenced by the ECHOG as well as observation data which includes effects of complicated topography and the heat island.

A Weibull Distribution Based Technique for Downscaling of Climatic Wind Field

Mohamad Javad Alizadeh,Mohamad Reza Kavianpour,Bahareh Kamranzad,Amir Etemad-Shahidi 한국기상학회 2019 Asia-Pacific Journal of Atmospheric Sciences Vol.55 No.4

This study proposes a simple approach based on Weibull distribution parameters for downscaling climatic wind speed and direction. In this method, the Weibull parameters of a Global Climate Model (GCM) are modified using Weibull parameters of the reference data (ECMWF). To correct the wind direction, the downscaling technique was applied to the eastward and northward wind components. All the wind components were simply transformed to positive values in order to fit a Weibull distribution. The unbiased wind speed was calculated by integrating the corrected wind components. Moreover, other models were considered to directly modify the wind speed (not wind components) using the same methodology. Performance and ability of the proposed approach were evaluated against the existing statistical downscaling techniques such as Multiplicative Shift Method (MSM), quantile mapping and support vector regression. In the models, the 6-h GCM wind component/speed was the sole predictor and the ECMWFreanalysis wind data was considered as the predictand. It is demonstrated that direct application of the proposed method on the wind speed slightly gives better estimation of the predictand rather than its application on wind components. The results indicate theWeibull distribution based method outperforms the other techniques for wind direction and magnitude. The method provides sound predictions for a wide range of wind speed from low to high values. By using the proposed downscaling technique for wind components, wind direction can be adjusted accordingly.

Air temperature distribution over Mongolia using dynamical downscaling and statistical correction

Gerelchuluun, Bayasgalan,Ahn, Joong‐,Bae John Wiley Sons, Ltd 2014 International journal of climatology Vol.34 No.7

<P><B>ABSTRACT</B></P><P>In this study, dynamical downscaling was performed using the Weather Research and Forecast (WRF) model to attain fine‐resolution gridded meteorological information capable of reflecting Mongolia's complex topographical effect. Mongolia's sparse station network, with an average inter‐station distance 107 km, is incapable of representing the spatial distribution of climate variables, such as temperature, over the country's complex topography. In order to reproduce fine‐scale air temperature in Mongolia, the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis II data with 6‐h intervals from 1981 to 2010 were used as the initial and boundary conditions of the WRF model. A one‐way nesting system was applied for two nested domains with horizontal grid spaces of 60 and 20 km. For correction of the systematic biases induced by dynamical downscaling, a statistical correction method was used for the downscaled results simulated by the WRF model. The bias was divided into two parts: the mean and the perturbation. The former was corrected by using a weighting function and a temperature inversion, and the latter by using the self‐organizing maps method. In the former correction, the temperature inversion, characterized by an inverted lapse rate, in which temperature increases with increasing height in the lower atmosphere, was considered only when the temperature inversion occurred. According to our result, the domain‐averaged Root Mean Square Difference of the model‐simulated annual mean temperature was decreased from 3.7 °C to 2.1 °C after the statistical and temperature inversion corrections. On the basis of our study, we suggested that the area‐averaged, fine‐resolution, annual mean temperature of Mongolia was 1.1 °C (station mean temperature 0.5 °C). Our correction method improves not only spatial patterns with fine resolution but also the time‐varying temperature variance over Mongolia.</P>

고해상도 지상 기온 상세화 모델 개발

이두일(Doo-Il Lee),이상현(Sang-Hyun Lee),정형세(Hyeong-Se Jeong),김연희(Yeon-Hee Kim) 한국기상학회 2021 대기 Vol.31 No.5

A new physical/statistical diagnostic downscale model has been developed for use to improve near-surface air temperature forecasts. The model includes a series of physical and statistical correction methods that account for un-resolved topographic and land-use effects as well as statistical bias errors in a low-resolution atmospheric model. Operational temperature forecasts of the Local Data Assimilation and Prediction System (LDAPS) were downscaled at 100 m resolution for three months, which were used to validate the model’s physical and statistical correction methods and to compare its performance with the forecasts of the Korea Meteorological Administration Post-processing (KMAP) system. The validation results showed positive impacts of the un-resolved topographic and urban effects (topographic height correction, valley cold air pool effect, mountain internal boundary layer formation effect, urban land-use effect) in complex terrain areas. In addition, the statistical bias correction of the LDAPS model were efficient in reducing forecast errors of the near-surface temperatures. The new high-resolution downscale model showed better agreement against Korean 584 meteorological monitoring stations than the KMAP, supporting the importance of the new physical and statistical correction methods. The new physical/statistical diagnostic downscale model can be a useful tool in improving near-surface temperature forecasts and diagnostics over complex terrain areas.

도시 지표 특성 반영을 통한 여름철 기온 통계적 상세화 개선

양호진,이채연,지준범 한국기후변화학회 2024 한국기후변화학회지 Vol.15 No.1

Due to the recent increase in heatwave intensity and frequency, there is a growing demand for high-resolution temperature information for heatwave impact assessment and response. Particularly in urban areas vulnerable to heatwaves, there is a need for high-resolution temperature information that reflects the unique characteristics of the urban surface. In this study, statistical interpolation using spatial information representing surface characteristics of urban areas in Korea was applied to downscale the 5km-resolution Korea Meteorological Administration temperature data to a finer 1km resolution. Using national spatial information, terrain variables reflecting topographical features were computed, as were urban surface variables representing the characteristics of urban areas. Applying terrain variables and urban surface variables, we statistically downscaled temperature information and analyzed temperature observations from meteorological stations operated by both the Korea Meteorological Administration and KT Telecommunication within the urban area. The temperature observation data from KT, which are from an urban environment, was more suitable for analyzing the downscaled data focusing on urban areas. In addition, the temperature downscaling results with the addition of urban surface variables produced more realistic urban temperatures compared to the temperature downscaling results using only terrain variables. The application of urban surface variables is expected to contribute to the production of realistic temperature data reflecting conditions according to the spatial distribution of urban areas. This can provide valuable information for analyzing urban heat vulnerability, establishing countermeasures, and developing plans to improve urban thermal environments.