• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.152-152
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• 2018

Soil moisture is a important factor in hydrologic analysis. So, if we have spatially distributed soil moisture data, it can help to study much research in a various field. Recently, there are a lot of satellite derived soil moisture data, and it can be served through web freely. Especially, NASA (National Aeronautics and Space Administration) launched the Soil Moisture Aperture Passive (SMAP) satellite for mapping global soil moisture on 31 January 2015. SMAP data have many advantages for study, for example, SMAP data has higher spatial resolution than other satellited derived data. However, becuase many satellited derived soil moisture data have a limitation to data accuracy, if we have ancillary materials for improving data accuracy, it can be used. So, in this study, after applying the alogorithm, which is data assimilation methods, applicability of satellite derived soil moisture data was analyzed. Among the various data assimilation methods, in this study, Model Output Statistics (MOS) technique was used for improving satellite derived soil moisture data. Model Output Statistics (MOS) is a type of statistical post-processing, a class of techniques used to improve numerical weather models' ability to forecast by relating model outputs to observational or additional model data.

• Journal of Environmental Impact Assessment
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• v.22 no.6
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• pp.609-626
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• 2013

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.

• Journal of the Korean earth science society
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• v.45 no.4
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• pp.349-362
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• 2024

We aimed to evaluate the effectiveness of ensemble optimal interpolation (EnOI) in improving the analysis of significant wave height (SWH) within wave models using satellite-derived SWH data. Satellite observations revealed higher SWH in mid-latitude regions (30° to 60° in both hemispheres) due to stronger winds, whereas equatorial and coastal areas exhibited lower wave heights, attributed to calmer winds and land interactions. Root mean square error (RMSE) analysis of the control experiment without data assimilation revealed significant discrepancies in high-latitude areas, underscoring the need for enhanced analysis techniques. Data assimilation experiments demonstrated substantial RMSE reductions, particularly in high-latitude regions, underscoring the effectiveness of the technique in enhancing the quality of analysis fields. Sensitivity experiments with varying ensemble sizes showed modest global improvements in analysis fields with larger ensembles. Sensitivity experiments based on different decorrelation length scales demonstrated significant RMSE improvements at larger scales, particularly in the Southern Ocean and Northwest Pacific. However, some areas exhibited slight RMSE increases, suggesting the need for region-specific tuning of assimilation parameters. Reducing the observation error covariance improved analysis quality in certain regions, including the equator, but generally degraded it in others. Rescaling background error covariance (BEC) resulted in overall improvements in analysis fields, though sensitivity to regional variability persisted. These findings underscore the importance of data assimilation, parameter tuning, and BEC rescaling in enhancing the quality and reliability of wave analysis fields, emphasizing the necessity of region-specific adjustments to optimize assimilation performance. These insights are valuable for understanding ocean dynamics, improving navigation, and supporting coastal management practices.

• Atmosphere
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• v.31 no.3
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• pp.251-265
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• 2021

This study examined the impact of assimilating the bending angle (BA) obtained via the global navigation satellite system radio occultation (GNSS RO) of the three new satellites (KOMPSAT-5, FY-3C, and FY-3D) on analyses and forecasts of a numerical weather prediction model. Numerical data assimilation experiments were performed using a three-dimensional variational data assimilation system in the Korean Integrated Model (KIM) at a 25-km horizontal resolution for August 2019. Three experiments were designed to select the height and quality control thresholds using the data. A comparison of the data with an analysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) integrated forecast system showed a clear positive impact of BA assimilation in the Southern Hemisphere tropospheric temperature and stratospheric wind compared with that without the assimilation of the three new satellites. The impact of new data in the upper atmosphere was compared with observations using the infrared atmospheric sounding interferometer (IASI). Overall, high volume GNSS RO data helps reduce the RMSE quantitatively in analytical and predictive fields. The analysis and forecasting performance of the upper temperature and wind were improved in the Southern and Northern Hemispheres.

• Atmosphere
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• v.28 no.2
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• pp.141-151
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• 2018

The KIAPS global model and data assimilation system were extended to assimilate brightness temperature from the Sondeur $Atmosph{\acute{e}}rique$ du Profil $d^{\prime}Humidit{\acute{e}}$ Intertropicale par $Radiom{\acute{e}}trie$ (SAPHIR) passive microwave water vapor sounder on board the Megha-Tropiques satellite. Quality control procedures were developed to assess the SAPHIR data quality for assimilating clear-sky observations over the ocean, and to characterize observation biases and errors. In the global cycle, additional assimilation of SAPHIR observation shows globally significant benefits for 1.5% reduction of the humidity root-mean-square difference (RMSD) against European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) analysis. The positive forecast impacts for the humidity and temperature in the experiment assimilating SAPHIR were predominant at later lead times between 96- and 168-hour. Even though its spatial coverage is confined to lower latitudes of $30^{\circ}S-30^{\circ}N$ and the observable variable is humidity, the assimilation of SAPHIR has a positive impact on the other variables over the mid-latitude domain. Verification showed a 3% reduction of the humidity RMSD with assimilating SAPHIR, and moreover temperature, zonal wind and surface pressure RMSDs were reduced up to 3%, 5% and 7% near the tropical and mid-latitude regions, respectively.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.1
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• pp.149-168
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• 2008

Many organizations have introduced information technology (IT) as an innovation to gain competitive advantages as business environments have become increasingly complex and rapidly changing. Despite the large investment of IT, there have been mixed results about whether IT creates business values. Considering that IT assimilation may play an important role in explaining IT productivity paradox, this study examines the formation of IT assimilation and its effect on the organizational performance. To do so, this study suggests a research model based on Innovation Diffusion Theory by incorporating organization system, communication channel, change acceptability, and innovation factors, and then attempts to empirically explore the role of IT assimilation for enhanced organizational performance using data collected at the organizational level. Structural equation analysis using AMOS provides significant support for part of proposed relationships. Specifically, we have found encouraging results on the role of IT assimilation by identifying its mediating effect on the organizational performance. Theoretical and practical implications of the findings are discussed accordingly.

• Journal of Information Technology Services
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• v.13 no.3
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• pp.1-23
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• 2014

This study investigates the effect of technological and environmental factors on the cloud computing assimilation, which then affects firms' performance. The technological characteristics include cost-savings, technology use advantage, technology infrastructure, and technology compatibility while environmental characteristics include partner cooperation, competitive pressure, environmental uncertainty, and business agility. Furthermore, we examine inter-organizational trust as a moderating effect between environmental characteristics and cloud computing assimilation. Data from a sample of 219 firms show the significant impacts of proposed variables with exception of technology infrastructure and technology compatibility. The findings also show that inter-organizational trust has a significant moderating effect in all paths except the one between business agility and cloud computing assimilation. The implication of this study suggests a theoretical framework explaining cloud computing assimilation and performance within inter-organizational environment.

• Journal of the Korean Society of Visualization
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• v.16 no.2
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• pp.23-30
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• 2018

The present study compares flow fields reconstructed by data assimilation method with different combinations of parameters. As a data assimilation method, Vortex-in-Cell-sharp (VIC#), which supplements additional constraints and multigrid approximation to Vortex-in-Cell-plus (VIC+), is used to reconstruct flow fields from scattered particle tracks. Two parameters, standard deviation of Gaussian radial basis function (RBF) and grid spacing, are mainly tested using artificial data sets which contain few particle tracks. Consequent flow fields are analyzed in terms of flow structure sizes. It is demonstrated that sizes of the flow structures are proportional to an actual scale of the standard deviation of RBF. It implies that a combination of larger grid spacing and smaller standard deviation which preserves the actual standard deviation is able to save computational resources in case of a low track density. In addition, a simple comparison using an experimental data filled with dense particle tracks is conducted.

• Atmosphere
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• v.33 no.4
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• pp.423-440
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• 2023

The updated version of Global Ocean Data Assimilation and Prediction System (GODAPS) in the NIMS/KMA (National Institute of Meteorological Sciences/Korea Meteorological Administration), which has been in operation since December 2021, is being introduced. This technical note on GODAPS2 describes main progress and updates to the previous version of GODAPS, a software tool for the operating system, and its improvements. GODAPS2 is based on Forecasting Ocean Assimilation Model (FOAM) vn14.1, instead of previous version, FOAM vn13. The southern limit of the model domain has been extended from 77°S to 85°S, allowing the modelling of the circulation under ice shelves in Antarctica. The adoption of non-linear free surface and variable volume layers, the update of vertical mixing parameterization, and the adjustment of isopycnal diffusion coefficient for the ocean model decrease the model biases. For the sea-ice model, four vertical ice layers and an additional snow layer on top of the ice layers are being used instead of previous single ice and snow layers. The changes for data assimilation include the updated treatment for background error covariance, a newly added bias scheme combined with observation bias, the application of a new bias correction for sea level anomaly, an extension of the assimilation window from 1 day to 2 days, and separate assimilations for ocean and sea-ice. For comparison, we present the difference between GODAPS and GODAPS2. The verification results show that GODAPS2 yields an overall improved simulation compared to GODAPS.

• Journal of Information Processing Systems
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• v.11 no.4
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• pp.556-572
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• 2015

This paper presents the applications of spatial interpolation and assimilation methods for satellite and ground meteorological data, including temperature, relative humidity, and precipitation in regions of Vietnam. In this work, Universal Kriging is used for spatially interpolating ground data and its interpolated results are assimilated with corresponding satellite data to anticipate better gridded data. The input meteorological data was collected from 98 ground weather stations located all over Vietnam; whereas, the satellite data consists of the MODIS Atmospheric Profiles product (MOD07), the ASTER Global Digital Elevation Map (ASTER DEM), and the Tropical Rainfall Measuring Mission (TRMM) in six years. The outputs are gridded fields of temperature, relative humidity, and precipitation. The empirical results were evaluated by using the Root mean square error (RMSE) and the mean percent error (MPE), which illustrate that Universal Kriging interpolation obtains higher accuracy than other forms of Kriging; whereas, the assimilation for precipitation gradually reduces RMSE and significantly MPE. It also reveals that the accuracy of temperature and humidity when employing assimilation that is not significantly improved because of low MODIS retrieval due to cloud contamination.