• Journal of Advanced Marine Engineering and Technology
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• v.32 no.1
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• pp.20-26
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• 2008

The nacelle cover and nosecone are made of composite materials, especially the stiffener is added in the nacelle cover in order to enhance it's stiffness. The nacelle cover consists of all three covers of left, right side cover and upper cover and each cover is connected with bolts. Also, the nacelle cover and nacelle frame are connected with bolts. The nacelle cover and nosecone have a important role to prevent the components of nacelle and rotor from external circumstances such as snow, rain and wind. Therefore, it is necessary to analyze and evaluate the strength and deformation for them in the design level. According to GL Wind Specifications, this paper shows the results that nacelle cover of 2MW wind turbine satisfy the strength and deformation throughout analysis using Patran/Nastran programs.

• Journal of Korea Water Resources Association
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• v.40 no.8
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• pp.617-628
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• 2007

The purpose of this paper is to prepare snowmelt parameters using RS and GIS and to assess the snowmelt impact in SLURP (Semi-distributed Land Use-based Runoff Process) model for Chungju-Dam watershed $(6,661.5km^2)$. Three sets of NOAA AVHRR images (1998-1999, 2000-2001, 2001-2002) were analyzed to prepare snow-related data of the model during winter period. Snow cover areas were extracted using 1, 3 and 4 channels, and the snow depth was spatially interpolated using snowfall data of ground meteorological stations. With the snowmelt parameters, DEM (Digital Elevation Model), land cover, NDVI (Normalized Difference Vegetation Index) and weather data, the model was calibrated for 3 years (1998, 2000, 2001), and verified for 1 year (1999) using the calibrated parameters. The average Nash-Sutcliffe efficiencies for 4 years (1998-2001) discharge comparison with and without snowmelt parameters were 0.76 and 0.73 for the full period, and 0.57 and 0.19 for the period of January to May. The results showed that the spatially prepared snow-related data reduced the calibration effort and enhanced the model results.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.294-298
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• 2008

Snow cover is a potential water resource for later spring and summer seasons as well as a thermal mirror with high reflectivity causing decreases of surface air temperature during cold winter seasons. In this study, current and future changes in Northern Hemisphere snow extent and their potential linkages with atmospheric circulation are examined. The NOAA AVHRR visible snow extent (1967-2006) data as well as observational (NCEP-DOE 1979-2006) and modeled (GFDL 2.1 2081-2100) pressure and surface air temperature data are used. Analyses of observational data demonstrate that the snow extent in meteorological spring (March to April) and summer (June to August) has significantly decreased since the late 1980s. The offset of snow seasons (the timing of snow melt in spring) have also significantly advanced particularly in Europe, East Asia, and northwestern North America. Analyses of pressure fields reveal that the spatial patterns of the earlier snow melt are associated with changes in atmospheric circulation such as the Arctic Oscillation (AO). In the positive winter AO years, multiple positive pressure departure cores in the upper troposphere (200hPa) are observed over the mid-latitude regions from March to mid-April, while a negative pressure departure core (70hPa) prevails over the Arctic Ocean. The reversed anomaly patterns related to later snow melt occur in negative winter AO years. The comparison between current and future thermal spring onsets suggest that snow melt patterns will intensify with larger greenhouse gas emissions, indicating earlier hydrological spring onset.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.77-93
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• 2022

In this study, we present an algorithm that analyzes the risk by reflecting regional characteristics for factors affected by direct and indirect damage from heavy-snow. Factors affected by heavy-snow damage by 29 regions are selected as influencing variables, and the concept of sensitivity is derived through the relationship with the amount of damage. A snow damage risk prediction model was developed using a machine learning (XGBoost) algorithm by setting weather conditions (snow cover, humidity, temperature) and sensitivity as independent variables, and setting the risk derived according to changes in the independent variables as dependent variables.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.2
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• pp.92-101
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• 2020

To prevent and mitigate damage to farms due to heavy snowfall, snow weight information should be provided in addition to snow depth. This study reviews four formulae regarding snow density and weight used in extant studies and applies them in Suwon area to estimate snow weight in Korea. We investigated the observed snow depth of 94 meteorological stations and automatic weather stations (AWS) data over the past 30 years (1988-2017). Based on the spatial distribution of snow depth by area in Korea, much of the fresh snow cover, due to heavy snowfall, occurred in Jeollabuk-do and Gangwon-do. Record snowfalls occurred in Gyeongsangbuk-do and Gangwon-do. However, the most recent heavy snowfall in winter occurred in Gyeonggi-do, Gyeongsangbuk-do, and Jeollanam-do. This implies that even if the snow depth is high, there is no significant damage unless the snow weight is high. The estimation of snow weight in Suwon area yielded different results based on the calculation method of snow density. In general, high snow depth is associated with heavy snow weight. However, maximum snow weight and maximum snow depth do not necessarily occur on the same day. The result of this study can be utilized to estimate the snow weight at other locations in Korea and to carry out snow weight prediction based on a numerical model. Snow weight information is expected to aid in establishing standards for greenhouse design and to reduce the economic losses incurred by farms.

• Korean Journal of Remote Sensing
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• v.12 no.1
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• pp.60-80
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• 1996

AVHRR(Advanced Very High Resolution Radiometer) on NOAA satellite provides data in five spectral, one in visible range, one in near infrared and three in thermal range. In this paper, application of NOAA/AVHRR data is studied for environment monitoring such as cloud top temperature, surface temperature, albedo, sea surface temperature, vegetation index, forest fire, flood, snow cover and so on. The analyses for cloud top temperature, surface temperature, albedo, sea surface temperature, vegetation index and forest fire showed reasonable agreement. But monitoring for flood and snow cover was uneasy due to the limitations such as cloud contamination, low spatial resolution. So this research had only simple purpose to identify well-defined waterbody for dynamic monitoring of flood. Based on development of these basic algorithms, we have a plan to further reseach for environment monitoring using AVHRR data.

• Korean Journal of Remote Sensing
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• v.20 no.4
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• pp.235-244
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• 2004

This study focuses on the assessment for proposed algorithm to discriminate cloudy pixels from snowy pixels through use of visible, near infrared, and short wave infrared channel data in VEGETATION-1 sensor embarked on SPOT-4 satellite. Traditional threshold algorithms for cloud and snow masks did not show very good accuracy. Instead of these independent masking procedures, K-Means clustering scheme is employed for cloud/snow discrimination in this study. The pixels used in clustering were selected through an integration of two threshold algorithms, which group ensemble the snow and cloud pixels. This may give a opportunity to simplify the clustering procedure and to improve the accuracy as compared with full image clustering. This paper also compared the results with threshold methods of snow cover and clouds, and assesses discrimination capability in VEGETATION channels. The quality of the cloud and snow mask even more improved when present algorithm is implemented. The discrimination errors were considerably reduced by 19.4% and 9.7% for cloud mask and snow mask as compared with traditional methods, respectively.

• Magazine of the Korean Society of Agricultural Engineers
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• v.32 no.E
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• pp.88-94
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• 1990

Several models physically based to predict the evolution of the snowpack have been proposed. Validity of these models for hourly estimation is, however, questionable, since they have been tested only on a daily basis. A computational model to predict the amount of snowpack on an hourly basis in terms of snowload from a set of meterological measurements was developed and investigated the rapid snowmelt conditions during Fohn events in the Takada plain.

• Atmosphere
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• v.29 no.3
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• pp.269-282
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• 2019

While it is important to obtain the accurate information on snowfall data due to the increase in damage caused by the heavy snowfall in the winter season, it is not easy to observe the snowfall quantitatively. Recently, snow measurements using a weighing precipitation gauge have been carried out, but there is a problem that high snowfall intensity results in low accuracy. Also, the observed snowfall data are sensitive depending on wind speed, temperature, and humidity. In this study, a new process of quality control for snow water equivalent (SWE) data of the weighing precipitation gauge were proposed to cover the low accuracy of snow data and maximize the data utilization. Snowfall data (SWE) observed by Pluvio, Parsivel, snow-depth meter using laser or ultrasonic, and rainfall gauge in Cloud Physics Observation Site (CPOS) were compared and analyzed. Applying the QC algorithm including the use of number of hydrometeor particles as reference, the increased SWE per the unit time was determined and the data noise was removed and marked by flag. The SWE data converted by the number concentration of hydrometeor particles are tested as a method to restore the QC-removed data, and show good agreement with those of the weighing precipitation gauge, though requiring more case studies. The three events data for heavy snowfall disaster in Pyeongchang area was analyzed. The SWE data with improved quality was showed a good correlation with the eye-measured data ($R^2$ > 0.73).

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.109-126
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• 2014

We improved the Land Surface Emissivity (LSE) data (Kongju National University LSE v.2: KNULSE_v2) over the Communication, Ocean and Meteorological Satellite (COMS) observation region using recent(2009-2012) Moderate Resolution Imaging Spectroradiometer (MODIS) data. The surface emissivity was derived using the Vegetation Cover Method (VCM) based on the assumption that the pixel is only composed of ground and vegetation. The main issues addressed in this study are as follows: 1) the impacts of snow cover are included using Normalized Difference Snow Index (NDSI) data, 2) the number of channels is extended from two (11, 12 ${\mu}m$) to four channels (3.7, 8.7, 11, 12 ${\mu}m$), 3) the land cover map data is also updated using the optimized remapping of the five state-of-the-art land cover maps, and 4) the latest look-up table for the emissivity of land surface according to the land cover is used. The updated emissivity data showed a strong seasonal variation with high and low values for the summer and winter, respectively. However, the surface emissivity over the desert or evergreen tree areas showed a relatively weak seasonal variation irrespective of the channels. The snow cover generally increases the emissivity of 3.7, 8.7, and 11 ${\mu}m$ but decreases that of 12 ${\mu}m$. As the results show, the pattern correlation between the updated emissivity data and the MODIS LSE data is clearly increased for the winter season, in particular, the 11 ${\mu}m$. However, the differences between the two emissivity data are slightly increased with a maximum increase in the 3.7 ${\mu}m$. The emissivity data updated in this study can be used for the improvement of accuracy of land surface temperature derived from the infrared channel data of COMS.