• Journal of the Korean Association of Geographic Information Studies
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• v.5 no.2
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• pp.1-15
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• 2002

Atmospheric aerosols interact with sunlight and affect the global radiation balance that can cause climate change through direct and indirect radiative forcing. Because of the spatial and temporal uncertainty of aerosols in atmosphere, aerosol characteristics are not considered through GCMs (General Circulation Model). Therefor it is important physical and optical characteristics should be evaluated to assess climate change and radiative effect by atmospheric aerosols. In this study GMS-5 satellite data and surface measurement data were analyzed using a radiative transfer model for the Yellow Sand event of April 7~8, 2000 in order to investigate the atmospheric radiative effects of Yellow Sand aerosols, MODTRAN3 simulation results enable to inform the relation between satellite channel albedo and aerosol optical thickness(AOT). From this relation AOT was retreived from GMS-5 visible channel. The variance observations of satellite images enable remote sensing of the Yellow Sand particles. Back trajectory analysis was performed to track the air mass from the Gobi desert passing through Korean peninsular with high AOT value measured by ground based measurement. The comparison GMS-5 AOT to ground measured RSR aerosol optical depth(AOD) show that for Yellow Sand aerosols, the albedo measured over ocean surfaces can be used to obtain the aerosol optical thickness using appropriate aerosol model within an error of about 10%. In addition, LIDAR network measurements and backward trajectory model showed characteristics and appearance of Yellow Sand during Yellow Sand events. These data will be good supporting for monitoring of Yellow Sand aerosols.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.3
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• pp.127-143
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• 2022

Recently, many attempts to run numerical ocean models in cloud computing environments have been tried actively. A cloud computing environment can be an effective means to implement numerical ocean models requiring a large-scale resource or quickly preparing modeling environment for global or large-scale grids. Many commercial and private cloud computing systems provide technologies such as virtualization, high-performance CPUs and instances, ether-net based high-performance-networking, and remote direct memory access for High Performance Computing (HPC). These new features facilitate ocean modeling experimentation on commercial cloud computing systems. Many scientists and engineers expect cloud computing to become mainstream in the near future. Analysis of the performance and features of commercial cloud services for numerical modeling is essential in order to select appropriate systems as this can help to minimize execution time and the amount of resources utilized. The effect of cache memory is large in the processing structure of the ocean numerical model, which processes input/output of data in a multidimensional array structure, and the speed of the network is important due to the communication characteristics through which a large amount of data moves. In this study, the performance of the Regional Ocean Modeling System (ROMS), the High Performance Linpack (HPL) benchmarking software package, and STREAM, the memory benchmark were evaluated and compared on commercial cloud systems to provide information for the transition of other ocean models into cloud computing. Through analysis of actual performance data and configuration settings obtained from virtualization-based commercial clouds, we evaluated the efficiency of the computer resources for the various model grid sizes in the virtualization-based cloud systems. We found that cache hierarchy and capacity are crucial in the performance of ROMS using huge memory. The memory latency time is also important in the performance. Increasing the number of cores to reduce the running time for numerical modeling is more effective with large grid sizes than with small grid sizes. Our analysis results will be helpful as a reference for constructing the best computing system in the cloud to minimize time and cost for numerical ocean modeling.

• Korean Journal of Environmental Biology
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• v.24 no.3
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• pp.248-257
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• 2006

Total naturalized plant species in the streams of Jeonju were listed as 109 taxa; 24 families, 75 genera, 106 species, 3 varieties. Dividing by stream, Jeonju stream has 75 taxa; 20 families, 55 genera, 73 species, 2 varieties. Samcheon stream has 86 taxa; 19 families, 64 genera, 84 species, 2 varieties. Soyang stream has 80 taxa; 21 families, 60 genera, 77 species, 3 varieties. Urbanization Index (UI) of total streams (109 taxa) was 40.2%. UI was 27.7% in Jeonju stream (75 taxa), 31.7% in Samcheon stream (86 taxa), 29.5% in Soyang stream (80 taxa). Dividing by degree of naturalization classification, 25 taxa (9.2%) were found in class 5, 17 taxa (6.2%) in class 4, 32 taxa (11.8%) in class 3, 27 taxa (9.9%) in class 2 and 8 taxa (2.9%) in class 1. Dividing by introduction period, 48 taxa (44%) aye in period I, 19 taxa (17%) in period II, 42 taxa (39%) in period III. Dividing by growth type, 48 taxa (44%) are annuals, 25 taxa (23%) are biennials, 33 taxa (30%) are perennials. Dividing by the place of origin, 39 taxa (35%) are from Euyope, 33 taxa (30%) from North America, 11 taxa (10%) from Tropic America, 9 taxa (8%) from Europe Asia,5 taxa (5%) from South America, 5 taxa (5%) from China.