• Journal of Korean Society for Atmospheric Environment
• /
• v.23 no.6
• /
• pp.653-665
• /
• 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.

• Journal of the Korean Geographical Society
• /
• v.43 no.4
• /
• pp.511-525
• /
• 2008

Residential segregation is often considered to be one of the social problems that intensify urban inequality This study reviews three different frameworks about the causes of residential segregation and tests their validity in the real world. The review focuses on racial/ethnic residential segregation in U.S. cities since it has been blamed for persistent socio-economic gap among racial/ethnic groups. The three different segregation frameworks include 'spatial assimilation' that attributes segregation to low degree of assimilation and acculturation, 'place stratification' to discriminatory practices in the housing and mortgage markets such as steering, blockbusting, and redlining, and 'resurgent ethnicity' to racial/ethnic preference in residential choice, particularly in-group attraction. As an effort to test their validity, the paper examined residential pattern changes of the four major Asian nationality groups through 1990s and found that their residences got decentralized but re-cluster in some selected suburbs. This supports 'resurgent ethnicity' largely and 'spatial assimilation' only partly.

• Journal of Korean Society for Atmospheric Environment
• /
• v.20 no.4
• /
• pp.523-537
• /
• 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.

• Journal of the Korean Mathematical Society
• /
• v.58 no.1
• /
• pp.1-28
• /
• 2021

We study a continuous data assimilation algorithm for the three-dimensional simplified Bardina model utilizing measurements of only two components of the velocity field. Under suitable conditions on the relaxation (nudging) parameter and the spatial mesh resolution, we obtain an asymptotic in time estimate of the difference between the approximating solution and the unknown reference solution corresponding to the measurements, in an appropriate norm, which shows exponential convergence up to zero.

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

• Atmosphere
• /
• v.28 no.2
• /
• pp.141-151
• /
• 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 earth science society
• /
• v.33 no.4
• /
• pp.309-319
• /
• 2012

In order to clarify the characteristics of satellite based sea wind data assimilations applied for the estimation of wind resources around the Korean peninsula, several numerical experiments were carried out using WRF. Satellite sea wind data used in this study are QuikSCAT from NASA and ASCAT from ESA. When the wind resources are estimated with data assimilation, its estimation accuracy is improved clearly. Since the band width is broad for QuikSCAT, statistical accuracy of the estimated wind resources with QuikSCAT assimilations is better than that with ASCAT assimilations. But the wind estimated around sub-satellite point matches better with of ASCAT compared to QuikSCAT assimilation. The impact of sea wind data assimilation on the prediction of wind resources lasts for 6 hours after data assimilation starts, therefore the data assimilation processes using both fine spatial and temporal resolutions of sea wind are needed to make a more useful wind resource map of the Korean Peninsula.

• International Journal of Computer Science & Network Security
• /
• v.23 no.3
• /
• pp.177-186
• /
• 2023

Hyperspectral imaging technology is one of the most efficient and fast-growing technologies in recent years. Hyperspectral image (HSI) comprises contiguous spectral bands for every pixel that is used to detect the object with significant accuracy and details. HSI contains high dimensionality of spectral information which is not easy to classify every pixel. To confront the problem, we propose a novel RGB channel Assimilation for classification methods. The color features are extracted by using chromaticity computation. Additionally, this work discusses the classification of hyperspectral image based on Domain Transform Interpolated Convolution Filter (DTICF) and 3D-CNN with Bi-directional-Long Short Term Memory (Bi-LSTM). There are three steps for the proposed techniques: First, HSI data is converted to RGB images with spatial features. Before using the DTICF, the RGB images of HSI and patch of the input image from raw HSI are integrated. Afterward, the pair features of spectral and spatial are excerpted using DTICF from integrated HSI. Those obtained spatial and spectral features are finally given into the designed 3D-CNN with Bi-LSTM framework. In the second step, the excerpted color features are classified by 2D-CNN. The probabilistic classification map of 3D-CNN-Bi-LSTM, and 2D-CNN are fused. In the last step, additionally, Markov Random Field (MRF) is utilized for improving the fused probabilistic classification map efficiently. Based on the experimental results, two different hyperspectral images prove that novel RGB channel assimilation of DTICF-3D-CNN-Bi-LSTM approach is more important and provides good classification results compared to other classification approaches.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.152-152
• /
• 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 The Korean Society of Agricultural Engineers
• /
• v.62 no.6
• /
• pp.11-20
• /
• 2020

We estimated the spatio-temporally distributed soil moisture using Sentinel-1A/B SAR (Synthetic Aperture Radar) sensor images and soil moisture data assimilation technique in South Korea. Soil moisture data assimilation technique can extract the hydraulic parameters of soils using observed soil moisture and GA (Genetic Algorithm). The SWAP (Soil Water Atmosphere Plant) model associated with a soil moisture assimilation technique simulates the soil moisture using the soil hydraulic parameters and meteorological data as input data. The soil moisture based on Sentinel-1A/B was validated and evaluated using the pearson correlation and RMSE (Root Mean Square Error) analysis between estimated soil moisture and TDR soil moisture. The soil moisture data assimilation technique derived the soil hydraulic parameters using Sentinel-1A/B based soil moisture images, ASOS (Automated Synoptic Observing System) weather data and TRMM (Tropical Rainfall Measuring Mission)/GPM (Global Precipitation Measurement) rainfall data. The derived soil hydrological parameters as the input data to SWAP were used to simulate the daily soil moisture values at the spatial domain from 2001 to 2018 using the TRMM/GPM satellite rainfall data. Overall, the simulated soil moisture estimates matched well with the TDR measurements and Sentinel-1A/B based soil moisture under various land surface conditions (bare soil, crop, forest, and urban).