• Title, Summary, Keyword: Data assimilation

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

  • Kim, Seung-Bum
    • Korean Journal of Remote Sensing
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    • v.17 no.4
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    • pp.345-357
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    • 2001
  • 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.

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Analysis of the Relation between Spatial Resolution of Initial Data and Satellite Data Assimilation for the Evaluation of Wind Resources in the Korean Peninsula (한반도 풍력자원 평가를 위한 초기 공간해상도와 위성자료 동화의 관계 분석)

  • Lee, Soon-Hwan;Lee, Hwa-Woon;Kim, Dong-Hyuk;Kim, Hyeon-Gu
    • Journal of Korean Society for Atmospheric Environment
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    • v.23 no.6
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    • pp.653-665
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    • 2007
  • Several numerical experiments were carried out to clarify the influence of satellite data assimilation with various spatial resolution on mesoscale meteorological wind and temperature field. Satellite data used in this study is QuikSCAT launched on ADEOS II. QuikSCAT data is reasonable and faithful sea wind data, which have been verified through many observational studies. And numerical model in the study is MM5 developed by NCAR. Difference of wind pattern with and without satellite data assimilation appeared clearly, especially wind speed dramatically reduced on East Sea, when satellite data assimilation worked. And sea breeze is stronger in numerical experiments with RDAPS and satellite data assimilation than that with CDAS and data assimilation. This caused the lower estimated surface temperature in CDAS used cases. Therefore the influence of satellite data assimilation acts differently according to initial data quality. And it is necessary to make attention careful to handle the initial data for numerical simulations.

A Suggestion for Data Assimilation Method of Hydrometeor Types Estimated from the Polarimetric Radar Observation

  • Yamaguchi, Kosei;Nakakita, Eiichi;Sumida, Yasuhiko
    • Proceedings of the Korea Water Resources Association Conference
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    • pp.2161-2166
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    • 2009
  • It is important for 0-6 hour nowcasting to provide for a high-quality initial condition in a meso-scale atmospheric model by a data assimilation of several observation data. The polarimetric radar data is expected to be assimilated into the forecast model, because the radar has a possibility of measurements of the types, the shapes, and the size distributions of hydrometeors. In this paper, an impact on rainfall prediction of the data assimilation of hydrometeor types (i.e. raindrop, graupel, snowflake, etc.) is evaluated. The observed information of hydrometeor types is estimated using the fuzzy logic algorism. As an implementation, the cloud-resolving nonhydrostatic atmospheric model, CReSS, which has detail microphysical processes, is employed as a forecast model. The local ensemble transform Kalman filter, LETKF, is used as a data assimilation method, which uses an ensemble of short-term forecasts to estimate the flowdependent background error covariance required in data assimilation. A heavy rainfall event occurred in Okinawa in 2008 is chosen as an application. As a result, the rainfall prediction accuracy in the assimilation case of both hydrometeor types and the Doppler velocity and the radar echo is improved by a comparison of the no assimilation case. The effects on rainfall prediction of the assimilation of hydrometeor types appear in longer prediction lead time compared with the effects of the assimilation of radar echo only.

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The Effects of Data Assimilation on Simulated Wind Fields Using Upper-Air Observations (고층기상관측자료를 이용한 바람장 개선 효과 연구)

  • Jeong, Ju-Hee;Kwun, Ji-Hye;Kim, Yoo-Keun
    • Journal of Environmental Science International
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    • v.16 no.10
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    • pp.1127-1137
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    • 2007
  • We focused on effects on data assimilation of simulated wind fields by using upper-air observations (wind profiler and sonde data). Local Analysis Prediction System (LAPS), a type of data assimilation system, was used for wind field modeling. Five cases of simulation experiments for sensitivity analysis were performed: which are EXP0) non data assimilation, EXP1) surface data, EXP2) surface data and sonde data, EXP3) surface data and wind profiler data, EXP4) surface data, sonde data and wind profiler data. These were compared with observation data. The result showed that the effects of data assimilation with wind profiler data were found to be greater than sonde data. The delicate wind fields in complex coastal area were simulated well in EXP3. EXP3 and EXP4 using wind profiler data with vertically high resolution represented well sophisticated differences of wind speed compared with EXP1 and EXP2, this is because the effects of wind profiler data assimilation were sensitively adjusted to first guess field than those of sonde observations.

Numerical Study on Surface Data Assimilation for Estimation of Air Quality in Complex Terrain (복잡 지형의 대기질 예측을 위한 지상자료동화의 효용성에 관한 수치연구)

  • 이순환;김헌숙;이화운
    • Journal of Korean Society for Atmospheric Environment
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    • v.20 no.4
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    • pp.523-537
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    • 2004
  • In order to raise the accuracy of meteorological data, several numerical experiments about the usefulness of data assimilation to prediction of air pollution was carried out. Used data for data assimilation are surface meteorological components observed by Automatical Weather System with high spatial density. The usage of surface data assimilation gives changes of temperature and wind fields and the change caused by the influence of land-use on meterological simulation is more sensitive at night than noon. The data quality in assimilation it also one of the important factors to predict the meteorological field precisely and through the static IOA (Index of Agreement), simulated meteorological components with selected limited surface data assimilation are agree well with observations.

An Analysis on Effects of the Initial Condition and Emission on PM10 Forecasting with Data Assimilation (초기조건과 배출량이 자료동화를 사용하는 미세먼지 예보에 미치는 영향 분석)

  • Park, Yun-Seo;Jang, Im-suk;Cho, Seog-yeon
    • Journal of Korean Society for Atmospheric Environment
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    • v.31 no.5
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    • pp.430-436
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    • 2015
  • Numerical air quality forecasting suffers from the large uncertainties of input data including emissions, boundary conditions, earth surface properties. Data assimilation has been widely used in the field of weather forecasting as a way to reduce the forecasting errors stemming from the uncertainties of input data. The present study aims at evaluating the effect of input data on the air quality forecasting results in Korea when data assimilation was invoked to generate the initial concentrations. The forecasting time was set to 36 hour and the emissions and initial conditions were chosen as tested input parameters. The air quality forecast model for Korea consisting of WRF and CMAQ was implemented for the test and the chosen test period ranged from November $2^{nd}$ to December $1^{st}$ of 2014. Halving the emission in China reduces the forecasted peak value of $PM_{10}$ and $SO_2$ in Seoul as much as 30% and 35% respectively due to the transport from China for the no-data assimilation case. As data assimilation was applied, halving the emissions in China has a negligible effect on air pollutant concentrations including $PM_{10}$ and $SO_2$ in Seoul. The emissions in Korea still maintain an effect on the forecasted air pollutant concentrations even after the data assimilation is applied. These emission sensitivity tests along with the initial condition sensitivity tests demonstrated that initial concentrations generated by data assimilation using field observation may minimize propagation of errors due to emission uncertainties in China. And the initial concentrations in China is more important than those in Korea for long-range transported air pollutants such as $PM_{10}$ and $SO_2$. And accurate estimation of the emissions in Korea are still necessary for further improvement of air quality forecasting in Korea even after the data assimilation is applied.

Variational Data Assimilation for Optimal Initial Conditions in Air Quality Modeling

  • Park, Seon-Ki
    • Journal of Korean Society for Atmospheric Environment
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    • v.19 no.E2
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    • pp.75-81
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    • 2003
  • Variational data assimilation, which is recently introduced to the air quality modeling, is a promising tool for obtaining optimal estimates of initial conditions and other important parameters such as emission and deposition rates. In this paper. two advanced techniques for variational data assimilation, based on the adjoint and quasi-inverse methods, are tested for a simple air quality problem. The four-dimensional variational assimilation (4D-Var) requires to run an adjoint model to provide the gradient information in an iterative minimization process, whereas the inverse 3D-Var (I3D-Var) seeks for optimal initial conditions directly by running a quasi -inverse model. For a process with small dissipation, I3D-Vu outperforms 4D-Var in both computing time and accuracy. Hybrid application which combines I3D-Var and standard 4D-Var is also suggested for efficient data assimilation in air quality problems.

Data Assimilation for Oceanographic Application: A Brief Overview

  • Park, Seon-K.
    • Journal of the korean society of oceanography
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    • v.38 no.2
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    • pp.52-59
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    • 2003
  • In this paper, a brief overview on data assimilation is provided in the context of oceanographic application. The ocean data assimilation needs to ingest various types of data such as satellites and floats, thus essentially requires dynamically-consistent assimilation methods. For such purpose, sequential and variational approaches are discussed and compared. The major advantage of the Kalman filter (KF) is that it can forecast error covariances at each time step. However, for models with very large dimension of state vector, the KF Is exceedingly expensive and computationally less efficient than four-dimensional variational assimilation (4D-Var). For operational application, simplified 4D-Var schemes as well as ensemble KF may be considered.

Study on the Impact of Various Observations Data Assimilation on the Meteorological Predictions over Eastern Part of the Korean Peninsula (관측자료별 자료동화 성능이 한반도 동부 지역 기상 예보에 미치는 영향 분석 연구)

  • Kim, Ji-Seon;Lee, Soon-Hwan;Sohn, Keon-Tae
    • Journal of Environmental Science International
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    • v.27 no.11
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    • pp.1141-1154
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    • 2018
  • 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.

Impact of Wind Profiler Data Assimilation on Wind Field Assessment over Coastal Areas

  • Park, Soon-Young;Lee, Hwa-Woon;Lee, Soon-Hwan;Kim, Dong-Hyeok
    • Asian Journal of Atmospheric Environment
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    • v.4 no.3
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    • pp.198-210
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    • 2010
  • Precise analysis of local winds for the prediction of atmospheric phenomena in the planetary boundary layer is extremely important. In this study, wind profiler data with fine time resolution and density in the lower troposphere were used to improve the performance of a numerical atmospheric model of a complex coastal area. Three-dimensional variational data assimilation (3DVAR) was used to assimilate profiler data. Two experiments were conducted to determine the effects of the profiler data on model results. First, we performed an observing system experiment. Second, we implemented a sensitivity test of data assimilation intervals to extend the advantages of the profiler to data assimilation. The lowest errors were observed when using both radio sonde and profiler data to interpret vertical and surface observation data. The sensitivity to the assimilation interval differed according to the synoptic conditions when the focus was on the surface results. The sensitivity to the weak synoptic effect was much larger than to the strong synoptic effect. The hourly-assimilated case showed the lowest root mean square error (RMSE, 1.62 m/s) and highest index of agreement (IOA, 0.82) under weak synoptic conditions, whereas the statistics in the 1, 3, and 6 hourly-assimilated cases were similar under strong synoptic conditions. This indicates that the profiler data better represent complex local circulation in the model with high time and vertical resolution, particularly when the synoptic effect is weak.