The Impact of Observation Systems on Medium-Range Weather Forecasting in a Global Forecast System

Seung-On Hwang,홍성유 한국기상학회 2012 Asia-Pacific Journal of Atmospheric Sciences Vol.48 No.2

To investigate the impact of various types of data on medium-range forecasts, observing system experiments are performed using an assimilation algorithm based on the National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE)reanalysis system. Data-denial experiments for radiosonde, satellite,aircraft, and sea surface observations, and selected data experiments for radiosonde and surface data, are conducted for the boreal summer of 1997 and the boreal winter of 1997/1998. The data assimilation system used in this study is remarkably dependent on radiosonde data,which provides information about the three-dimensional structure of the atmosphere. As expected, the impact of radiosonde observations on medium-range forecasts is strongly positive over the Northern Hemisphere and tropics, whereas the satellite system is most beneficial over the Southern Hemisphere. These results are also found in experiments simulating historical changes in observation systems. Over the tropics, assimilation without radiosonde observations generates unbalanced analyses resulting in unrealistic forecasts that must be corrected by the forecast model. Forecasts based on analysis from the observation data before the era of radiosonde observation are found to be less meaningful. In addition, the impacts on forecasts are closely related to the geographical distribution of observation data. The memory of observation data embedded in the analysis tends to persist throughout forecasts. However, cases exist where the effect of forecast error growth is more dominant than that of analysis error, e.g.,over East Asia in summer, and where the deficiency in observations is supplemented or the imbalance in analysis is adjusted by the forecast model during the period of forecasts. Forecast error growth may be related to the synoptic correction performed by the data assimilation system. Over data-rich areas, analysis fields are corrected to a greater extent by the data assimilation system than are those over data-poor areas, which can cause the forecast model to produce more forecast errors in medium-range forecasts. It is found that even one month per season is sufficient for forecast skill verification in data impact experiments. Additionally, the use of upper-air observations is found to benefit areas that are downstream of observation data-rich areas.

Assimilation of Sea Surface Temperature in the Northwest Pacific Ocean and its Marginal Seas using the Ensemble Kalman Filter

서광호,최병주,조양기,김영호,김상일 한국해양과학기술원 2010 Ocean science journal Vol.45 No.4

Satellite-borne sea surface temperature (SST) data were assimilated with the ensemble Kalman filter (EnKF) in a Northwest Pacific Ocean circulation model to examine the effect of data assimilation. The model domain included the northwestern part of the Pacific Ocean and its marginal seas, such as the Yellow Sea and East/Japan Sea. The performance of the data assimilation was evaluated by comparing the simulated ocean state with that observed. Spatially averaged root-meansquared errors in the SST and sea surface height (SSH) decreased by 0.44 °C and 4 cm, respectively, by the assimilation. The results of the numerical experiments substantiated the effectiveness of the SST assimilation via the EnKF for all marginal seas, as well as the Kuroshio region. The benefit of the data assimilation depended on the characteristics of each marginal sea. The variation of the SST in the East/Japan Sea and the Kuroshio extension (KE) region were improved 34% and those in the Yellow Sea 12.5%. The variation of the SSH was improved approximately 36% in the KE region. This large improvement was achieved in the deep-water regions because assimilation of SST data corrected the separation point of the western boundary currents, such as the Kuroshio and the East Korea Warm Current, and the associated horizontal surface currents. The SST assimilation via the EnKF also improved the subsurface temperature profiles. The effectiveness of SST assimilation was seasonally dependent, with the improvement being relatively larger in winter than in summer, which was related to the seasonal variation of the vertical mixing and stratification in the ocean surface layer.

Assimilation of Oceanographic Data into Numerical Models over the Seas around Korea

Kim, Seung-Bum The Korean Society of Remote Sensing 2001 大韓遠隔探査學會誌 Vol.17 No.4

This review provides a summary of data assimilation applied to the seas around Korea. Currently the worldwide efforts are devoted to applying advanced assimilation to realistic cases, thanks to improvements in mathematical foundations of assimilation methods and the computing capabilities, and also to the availability of extensive observational data such as from satellites. Over the seas around Korea, however, the latest developments in the advanced assimilation methods have yet to be applied. Thus it would be timely to review the progress in data assimilation over the seas. Firstly, the definition and necessity of data assimilation are described, continued by a brief summary of major assimilation methods. Then a review of past research on the ocean data assimilation in the regional seas around Korea is given and future trends are considered. Special consideration is given to the assimilation of remotely-sensed data.

Review : Assimilation of Oceanographic Data into Numerical Models over the Seas around Korea

Seung Bum Kim 大韓遠隔探査學會 2001 大韓遠隔探査學會誌 Vol.17 No.4

This review provides a summary of data assimilation applied to the seas around Korea. Currently the worldwide efforts are devoted to applying advanced assimilation to realistic cases, thanks to improvements in mathematical foundations of assimilation methods and the computing capabilities, and also to the availability of extensive observational data such as from satellites. Over the seas around Korea, however, the latest developments in the advanced assimilation methods have yet to be applied. Thus it would be timely to review the progress in data assimilation over the seas. Firstly, the definition and necessity of data assimilation are described, continued by a brief summary of major assimilation methods. Then a review of past research on the ocean data assimilation in the regional seas around Korea is given and future trends are considered. Special consideration is given to the assimilation of remotely-sensed data.

관측자료별 자료동화 성능이 한반도 동부 지역 기상 예보에 미치는 영향 분석 연구

김지선 ( Ji-seon Kim ),이순환 ( Soon-hwan Lee ),손건태 ( Keon-tae Sohn ) 한국환경과학회 2018 한국환경과학회지 Vol.27 No.11

Numerical experiments were carried out to investigate the effect of data assimilation of observational data on weather and PM (particulate matter) prediction. Observational data applied to numerical experiment are aircraft observation, satellite observation, upper level observation, and AWS (automatic weather system) data. In the case of grid nudging, the prediction performance of the meteorological field is largely improved compared with the case without data assimilations because the overall pressure distribution can be changed. So grid nudging effect can be significant when synoptic weather pattern strongly affects Korean Peninsula. Predictability of meteorological factors can be expected to improve through a number of observational data assimilation, but data assimilation by single data often occurred to be less predictive than without data assimilation. Variation of air pressure due to observation nudging with high prediction efficiency can improve prediction accuracy of whole model domain. However, in areas with complex terrain such as the eastern part of the Korean peninsula, the improvement due to grid nudging were only limited. In such cases, it would be more effective to aggregate assimilated data.

Assimilation of wind profiler observations and its impact on three-dimensional transport of ozone over the Southeast Korean Peninsula

Park, S.Y.,Lee, S.H.,Lee, H.W. Pergamon Press ; Elsevier [distribution] 2014 Atmospheric environment Vol.99 No.-

In order to investigate the impact of data assimilation on the assessment of ozone concentration in inland regions in the eastern area of the Korean Peninsula, several numerical experiments have been carried out using the Weather Research and Forecasting (WRF) model to estimate atmospheric circulations and the Community Multiscale Air Quality (CMAQ) model to assess air quality. Observations of wind that are assimilated into the modeling system are obtained from a wind profiler located at Changwon (CW), which is an urbanized coastal region in the Korean Peninsula. The simulated wind and temperature that is related to a well-developed sea breeze circulation are more consistent with observations in the experiment with dada assimilation than that without the assimilation. The ozone concentrations at both the coastal area of CW and the inland region of DG are well reproduced in the simulation with application of profiler data assimilation. Results from experiments without data assimilation are less realistic than that from the experiment with data assimilation. However, the improvement in simulation of meteorological variables and ozone concentration due to data assimilation is greater in the inland area than in the coastal area, where the wind profiler is located. The ozone concentration in CW changes only over a limited area and below the altitude of 1 km with a maximum change of 25 ppb. In contrast, the simulated ozone concentration in DG has been improved from the ground to upper levels of the planetary boundary layer (PBL), despite the fact that the observations are collected and assimilated into the model at the coastal region. Based on the results of process analysis, we find that the horizontal and vertical transportation of ozone related to the sea-breeze is more important than the local contribution of chemical production in determining the ozone concentration over the inland area. Therefore, observations of wind profiles in the coastal area and assimilation of these observations into the modeling system are important in our modeling study to assess the ozone concentration in inland areas. The assimilation of observations can greatly improve the model performance in both circulation simulation and ozone concentration simulation.

태풍 수치모의에서 GPS-RO 인공위성을 사용한 관측 자료동화 효과

박순영 ( Soon-young Park ),유정우 ( Jung-woo Yoo ),강남영 ( Nam-young Kang ),이순환 ( Soon-hwan Lee ) 한국환경과학회 2017 한국환경과학회지 Vol.26 No.5

In order to simulate a typhoon precisely, the satellite observation data has been assimilated using WRF (Weather Research and Forecasting model) three-Dimensional Variational (3DVAR) data assimilation system. The observation data used in 3DVAR was GPS Radio Occultation (GPS-RO) data which is loaded on Low-Earth Orbit (LEO) satellite. The refractivity of Earth is deduced by temperature, pressure, and water vapor. GPS-RO data can be obtained with this refractivity when the satellite passes the limb position with respect to its original orbit. In this paper, two typhoon cases were simulated to examine the characteristics of data assimilation. One had been occurred in the Western Pacific from 16 to 25 October, 2015, and the other had affected Korean Peninsula from 22 to 29 August, 2012. In the simulation results, the typhoon track between background (BGR) and assimilation (3DV) run were significantly different when the track appeared to be rapidly change. The surface wind speed showed large difference for the long forecasting time because the GPS-RO data contained much information in the upper level, and it took a time to impact on the surface wind. Along with the modified typhoon track, the differences in the horizontal distribution of accumulated rain rate was remarkable with the range of 600~500 mm. During 7 days, we estimated the characteristics between daily assimilated simulation (3DV) and initial time assimilation (3DV_7). Because 3DV_7 demonstrated the accurate track of typhoon and its meteorological variables, the differences in two experiments have found to be insignificant. Using observed rain rate data at 79 surface observatories, the statistical analysis has been carried on for the evaluation of quantitative improvement. Although all experiments showed underestimated rain amount because of low model resolution (27 km), the reduced Mean Bias and Root-Mean-Square Error were found to be 2.92 mm and 4.53 mm, respectively.

연구논문 : 기상 모델의 초기장 및 자료동화 차이에 따른 수도권 지역의 CMAQ 오존 예측 결과 -2007년 6월 수도권 고농도 오존 사례 연구-

이대균 ( Dae Gyun Lee ),이미향 ( Mi Hyang Lee ),이용미 ( Yong Mi Lee ),유철 ( Chul Yoo ),홍성철 ( Sung Chul Hong ),장기원 ( Kee Won Jang ),홍지형 ( Ji Hyung Hong ) 한국환경영향평가학회 2013 환경영향평가 Vol.22 No.6

Air quality models have been widely used to study and simulate many air quality issues. In the simulation, it is important to raise the accuracy of meteorological predicted data because the results of air quality modeling is deeply connected with meteorological fields. Therefore in this study, we analyzed the effects of meteorological fields on the air quality simulation. This study was designed to evaluate MM5 predictions by using different initial condition data and different observations utilized in the data assimilation. Among meteorological scenarios according to these input data, the results of meteorological simulation using National Centers for Environmental Prediction (Final) Operational Global Analysis data were in closer agreement with the observations and resulted in better prediction on ozone concentration. And in Seoul, observations from Regional Meteorological Office for data assimilations of MM5 were suitable to predict ozone concentration. In other areas, data assimilation using both observations from Regional Meteorological Office and Automatical Weather System provided valid method to simulate the trends of meteorological fields and ozone concentrations. However, it is necessary to vertify the accuracy of AWS data in advance because slightly overestimated wind speed used in the data assimilation with AWS data could result in underestimation of high ozone concentrations.

VAF 변분법을 이용한 전구 해양자료 동화 연구

안중배,윤용훈,조익현,오혜람,Ahn, Joong-Bae,Yoon, Yong-Hoon,Cho, Eek-Hyun,Oh, He-Ram 한국해양학회 2005 바다 Vol.10 No.1

본 연구에서는 전구 해양에서 관측되는 ARGO및 TAO해양 자료를 이용하여 해양의 3차원적인 구조를 분석.동화하고 궁극적으로 해양대순환모형을 위한 초기장을 생산하였다. 초기장의 생산을 위하여 전구 해양대순환 모형인 MOM3.1을 이용하였으며 생산한 배경장에, 계산시간과 계산공간을 절약할 수 있는 공간필터를 사용한 변분법(VAF, variational analysis using filter)을 이용하여 ARGO와 TAO 수온 자료를 동화하였다. 또한 본 연구에서는 자료 동화가 미치는 지속적인 영향을 살펴보고자 실험적분을 수행하였는데, 모형의 초기입력 자료를 자료동화 기법을 적용한 경우와 적용하지 않은 두 가지로 나누어 비교 실험을 수행하였다. 본 연구에서 자료 동화된 분석장은 OISST와의 비교를 통해 적절히 생산되었음을 보여주었다. 관측자료를 동화한 분석장을 초기자료로 한 10개월간의 적분결과를 살펴보면, 자료 동화를 통해 제거된 모형의 계통적 bias가 적분이 진행되는 과정에서 관성 중력파 등의 형태로 소멸되지 않고 지속적으로 관측과 유사하게 유지되었다. 이는 본 연구에서 실행한 자료동화가 모형의 역학적인 균형을 유지하면서 적절히 이루어졌음을 의미하며, 전구 대순환 모형을 이용한 중.장기 대기.해양 예측에 이러한 해양 자료동화가 대단히 유용하다는 것을 의미한다. ARCO and TAO data which supply three dimensional global ocean information are assimilated to the background field from a general circulation model, MOM3. Using a variational Analysis using Filter (VAF), which is a spatial variational filter designed to reduce computational time and space efficiently and economically, observed ARGO and TAO data are assimilated to the OGCM-generated background sea temperature for the generation of initial condition of the model. For the assessment of the assimilation impact, a comparative experiment has been done by integrating the model from different intial conditions: one from ARGO-, TAO-data assimilated initial condition and the other from background state without assimilation. The assimilated analysis field not only depicts major oceanic features more realistically but also reduces several systematic model bias that appear in every current OGCMs experiments. From the 10-month of model integrations with and without assimilated initial conditions, it is found that the major assimilated characteristics in sea temperature appeared in the initial field remain persistently throughout the integration. Such implies that the assimilated characteristics of the reduced sea temperature bias is to last in the integration without rapid restoration to the non-assimilated OGCM integration state by dispersing mass field in the form of internal gravity waves. From our analysis, it is concluded that the data assimilation method adapted in this study to MOM3 is reasonable and applicable with dynamical consistency. The success in generating initial condition with ARGO and TAO data assimilation has significant implication upon the prediction of the long-term climate and weather using ocean-atmosphere coupled model.

중규모 기상 모델을 이용한 안개 사례의 초기장 및 자료동화 민감도 분석

강미선,임윤규,조창범,김규랑,박준상,김백조 한국지구과학회 2015 한국지구과학회지 Vol.36 No.6

The accurate simulation of micro-scale weather phenomena such as fog using the mesoscale meteorological models is a very complex task. Especially, the uncertainty arisen from initial input data of the numerical models has a decisive effect on the accuracy of numerical models. The data assimilation is required to reduce the uncertainty of initial input data. In this study, the limitation of the mesoscale meteorological model was verified by WRF (Weather Research and Forecasting) model for a summer fog event around the Nakdong river in Korea. The sensitivity analyses of simulation accuracy from the numerical model were conducted using two different initial and boundary conditions: KLAPS (Korea Local Analysis and Prediction System) and LDAPS (Local Data Assimilation and Prediction System) data. In addition, the improvement of numerical model performance by FDDA (Four-Dimensional Data Assimilation) using the observational data from AWS (Automatic Weather System) was investigated. The result of sensitivity analysis showed that the accuracy of simulated air temperature, dew point temperature, and relative humidity with LDAPS data was higher than those of KLAPS, but the accuracy of the wind speed of LDAPS was lower than that of KLAPS. Significant difference was found in case of relative humidity where RMSE (Root Mean Square Error) for LDAPS and KLAPS was 15.7% and 35.6%, respectively. The RMSE for air temperature, wind speed, and relative humidity was improved by approximately 0.3℃, 0.2ms-1, and 2.2%, respectively after incorporating the FDDA. 중규모 기상 모델을 이용하여 안개와 같은 미세규모 국지현상을 정확히 재현하는 것은 매우 어려운 실정이다. 특히 수치모델의 초기 입력 자료의 불확도는 수치모델의 예측 정확도에 결정적인 영향을 미치며, 이를 보완하기 위한 자료동화가 요구되어진다. 본 연구에서는 WRF(Weather Research and Forecasting) 모델을 이용하여 낙동강 지역에서 발생한 여름철 안개사례의 재현실험을 대상으로 중규모 기상 모델의 한계를 검증하였다. 중규모 기상 모델에서 초기 및 경계장으로 사용되는 KLAPS (Korea Local Analysis and Prediction System)와 LDAPS (Local Data Assimilation and Prediction System) 분석장 자료를 이용하여 수치모델 모의 정확도 민감도 분석을 수행하였다. 또한 AWS (Automatic Weather System) 자료를 이용한 자료동화 (Four-Dimensional Data Assimilation)에 의한 수치모델의 정확도 개선 정도를 평가하였다. 초기 및 경계장 민감도 분석 결과에서 LDAPS 자료를 입력 자료로 사용한 경우가 KLAPS 자료 보다 기온과 이슬점온도, 상대습도에서 높은 정확도를 보였고, 풍속은 더 낮은 수준을 나타내었다. 특히, 상대습도에서 LDAPS의 경우는 RMSE (Root Mean Square Error)가 15.9%, KLAPS는 35.6%의 수준을 보여 그 차이가 매우 크게 나타났다. 또한 자료동화를 통하여 기온, 풍속, 상대습도의 RMSE가 각각 0.3℃, 0.2ms-1, 2.2% 수준으로 개선되었다.