• Journal of Korean Society for Atmospheric Environment
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• v.25 no.4
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• pp.264-274
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• 2009

Concentration of Air Quality Models (CMAQ) has a deep connection with emissions and wind fields. In particular the wind field is highly affected by local topography and plays an important role in transport and dispersion of contaminants from the pollution sources. The purpose of this study is to examine the impact of interpolation on Air quality model. This study was designed to evaluate enhancement of MM5 and CMAQ predictions by using Four Dimensional Data Assimilation (FDDA), the SONDE data and the national meteorological station and the MODerate resolution Imaging Spectroradiometer (MODIS). The alternative meteorological fields predicted with and without MODIS data were used to simulate spatial and temporal variations of ozone in combined with CMAQ on June 2006. The result of this study indicated that data assimilation using MODIS data provided an attractive method for generating realistic meteorological fields and dispersion fields of ozone in the Korea peninsular, because MODIS data in 10 km domain are grid horizontally and vertically. In order to ensure the success of Air quality model, it is necessary to FDDA using MODIS data.

• New & Renewable Energy
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• v.7 no.1
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• pp.22-28
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• 2011

The surface solar radiations were calculated and analyzed with spatial resolutions (4 km and 1 km) using by GWNU (Gangneung-Wonju National University) solar radiation model. The GWNU solar radiation model is used various data such as aerosol optical thickness, ozone amount, total precipitable water and cloud factor are retrieved from Moderate Resolution Imaging Spectrometer (MODIS), Ozone Monitoring Instrument (OMI), MTSAT-1R satellite data and output of the Regional Data Assimilation Prediction System(RDAPS) model by Korea Meteorological Administration (KMA), respectively. The differences of spatial resolutions were analyzed with input data (especially, cloud factor from MTSAT-1R satellite). And the Maximum solar radiation by GWNU model were found in Andong, Daegu and Jinju regions and these results were corresponded with the MTSAT-1R cloud factor.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.337-342
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• 2019

This letter describes the development of WRI (Wildfire Risk Index) using GDAPS (Global Data Assimilation and Prediction System) and satellite data, and its application to the Goseong-Sokcho and Gangneung-Donghae wildfires in April 4, 2019. We made sure that the proposed WRI represented the change of wildfire risk of around March 19 and April 4 very well. Our approach can be a viable option for wildfire risk monitoring, and future works will be necessary for the utilization of GK-2A products and the coupling with the wildfire prediction model of the Korea Forest Service.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.221-226
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• 1999

A data assimilation system for a 1-dimensional mixed layer model has been constructed using the adjoint method. The classical adjoint method does not work well for the mixed layer variabilities due to the occurrence of spikes in the gradient of the cost function. To solve this problem, the two techniques of scaling the cost function and optimization in the frequency space are used. As a result, the heat flux can be reliably estimated with an accuracy of 8Wm$^{-2}$ rms error in the identical twin experiments. We then applied this system to the tropical Pacific TOGA-TAO buoy data. The air-sea heat flux as well as the mixed layer variability were estimated in close approximation to the buoy data, particularly on time scales longer than the seasonal one.

• Atmosphere
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• v.32 no.1
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• pp.27-37
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• 2022

The Korean Integrated Model (KIM) forecast system was extended to assimilate Horizontal Line-Of-Sight (HLOS) wind observations from the Atmospheric Laser Doppler Instrument (ALADIN) on board the Atmospheric Dynamic Mission (ADM)-Aeolus satellite. Quality control procedures were developed to assess the HLOS wind data quality, and observation operators added to the KIM three-dimensional variational data assimilation system to support the new observed variables. In a global cycling experiment, assimilation of ALADIN observations led to reductions in average root-mean-square error of 2.1% and 1.3% for the zonal and meridional wind analyses when compared against European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) analyses. Even though the observable variable is wind, the assimilation of ALADIN observation had an overall positive impact on the analyses of other variables, such as temperature and specific humidity. As a result, the KIM 72-hour wind forecast fields were improved in the Southern Hemisphere poleward of 30 degrees.

• Korean Journal of Remote Sensing
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• v.29 no.6
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• pp.631-644
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• 2013

Significant Wave Height (SWH) data measured by satellite altimeters (Topex/Poseidon, Jason-1, Envisat, and Jason-2) were validated in the seas around Korea by comparison with wave height measurements from marine meteorological buoy stations of Korea Meteorological Administration (KMA). A total of 1,070 collocation matchups between Ku-band satellite altimeter data and buoy data were obtained for the periods of the four satellites from 1992 to the present. In the case of C-band and S-band observations, 1,086 matchups were obtained and used to assess the accuracy of satellite SWH. Root-Mean-Square (RMS) errors of satellite SWH measured with Ku-band were evaluated to roughly 0.2_2.1 m. Comparisons of the RMS errors and bias errors between different frequency bands revealed that SWH observed with Ku-band was much more accurate than other frequencies, such as C-band or S-band. The differences between satellite SWH and buoy wave height, satellite minus buoy, revealed some dependence on the magnitude of the wave height. Satellite SWH tended to be overestimated at a range of low wave height of less than 1 m, and underestimated for high wave height of greater than 2 m. Such regional characteristics imply that satellite SWH should be carefully used when employed for diverse purposes such as validating wave model results or data assimilation procedures. Thus, this study confirmed that satellite SWH products should be continuously validated for regional applications.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.1
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• pp.57-70
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• 2017

In this study, we evaluated daily root zone soil moisture dynamics and agricultural drought using a near-surface soil moisture data assimilation scheme with Soil Moisture Active & Passive (SMAP, $3km{\times}3km$) soil moisture footprints under different hydro-climate conditions. Satellite-based LANDSAT and MODIS image footprints were converted to spatially-distributed soil moisture estimates based on the regression model, and the converted soil moisture distributions were used for assessing uncertainties and applicability of SMAP data at fields. In order to overcome drawbacks of the discontinuity of SMAP data at the spatio-temporal scales, the data assimilation was applied to SMAP for estimating daily soil moisture dynamics at the spatial domain. Then, daily soil moisture values were used to estimate weekly agricultural drought based on the Soil Moisture Deficit Index (SMDI). The Yongdam-dam and Soyan river-dam watersheds were selected for validating our proposed approach. As a results, the MODIS/SMAP soil moisture values were relatively overestimated compared to those of the TDR-based measurements and LANDSAT data. When we applied the data assimilation scheme to SMAP, uncertainties were highly reduced compared to the TDR measurements. The estimated daily root zone soil moisture dynamics and agricultural drought from SMAP showed the variability at the sptio-temporal scales indicating that soil moisture values are influenced by not only the precipitation, but also the land surface characteristics. These findings can be useful for establishing efficient water management plans in hydrology and agricultural drought.

• Journal of Wetlands Research
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• v.18 no.2
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• pp.132-147
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• 2016

In this study the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) sensor onboard the Soil Moisture Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor onboard the Global Change Observation Mission-Water (GCOM-W1) based soil moisture retrievals were revised to obtain better accuracy of soil moisture and higher data acquisition rate over East Asia. These satellite-based soil moisture products are revised against a reference land model data set, called Global Land Data Assimilation System (GLDAS), using Cumulative Distribution Function (CDF) matching and regression approach. Since MIRAS sensor is perturbed by radio frequency interferences (RFI), the worst part of soil moisture retrieval, East Asia, constantly have been undergoing loss of data acquisition rate. To overcome this limitation, the threshold of RFI, DQX, and composite days were suggested to increase data acquisition rate while maintaining appropriate data quality through comparison of land surface model data set. The revised MIRAS and AMSR2 products were compared with in-situ soil moisture and land model data set. The results showed that the revising process increased correlation coefficient values of SMOS and AMSR2 averagely 27% 11% and decreased the root mean square deviation (RMSD) decreased 61% and 57% as compared to in-situ data set. In addition, when the revised products' correlation coefficient values are calculated with model data set, about 80% and 90% of pixels' correlation coefficients of SMOS and AMSR2 increased and all pixels' RMSD decreased. Through our CDF-based revising processes, we propose the way of mutual supplementation of MIRAS and AMSR2 soil moisture retrievals.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.20 no.1
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• pp.1-15
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• 2015

Impacts of Sea Surface Temperature (SST) assimilation to the prediction of upper ocean temperature is investigated by using a regional ocean forecasting system, in which 3-dimensional optimal interpolation is applied. In the present study, Sea Surface Temperature and Sea Ice Analysis (OSTIA) dataset is adopted for the daily SST assimilation. This study mainly compares two experimental results with (Exp. DA) and without data assimilation (Exp. NoDA). When comparing both results with OSTIA SST data during Sept. 2011, Exp. NoDA shows Root Mean Square Error (RMSE) of about $1.5^{\circ}C$ at 24, 48, 72 forecast hour. On the other hand, Exp. DA yields the relatively lower RMSE of below $0.8^{\circ}C$ at all forecast hour. In particular, RMSE from Exp. DA reaches $0.57^{\circ}C$ at 24 forecast hour, indicating that the assimilation of daily SST (i.e., OSTIA) improves the performance in the early SST prediction. Furthermore, reduction ratio of RMSE in the Exp. DA reaches over 60% in the Yellow and East seas. In order to examine impacts in the shallow costal region, the SST measured by eight moored buoys around Korean peninsula is compared with both experiments. Exp. DA reveals reduction ratio of RMSE over 70% in all season except for summer, showing the contribution of OSTIA assimilation to the short-range prediction in the coastal region. In addition, the effect of SST assimilation in the upper ocean temperature is examined by the comparison with Argo data in the East Sea. The comparison shows that RMSE from Exp. DA is reduced by $1.5^{\circ}C$ up to 100 m depth in winter where vertical mixing is strong. Thus, SST assimilation is found to be efficient also in the upper ocean prediction. However, the temperature below the mixed layer in winter reveals larger difference in Exp. DA, implying that SST assimilation has still a limitation to the prediction of ocean interior.

• Journal of Korea Water Resources Association
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• v.51 no.3
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• pp.273-280
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• 2018

Accurate estimation of evapotranspiration is mightily important for understanding and analyzing the hydrological cycle. There are various methods for estimating evapotranspiration and each method has its own advantages and limitations. Therefore, it is necessary to develop an optimal evapotranspiration product by combing different evapotranspiration products. In this study, we developed an optimal evapotranspiration by fusing two satellite- and model-based evapotranspiration estimates, including revised remote sensing-based Penman-Monteith (RS-PM) and Modified Satellite-Based Priestley-Taylor (MS-PT) methods, Global Land Data Assimilation System (GLDAS), and Global Land Evaporation Amsterdam Model (GLEAM). The statistical analysis (i.e., correlation coefficients, index of agreement, MAE, and RMSE) of combined evapotranspiration product showed to be improved compared to the individual model results. After confirming the overall results, in future studies, advanced data fusion techniques will be used to obtained improved results.