• Journal of the Korean earth science society
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• v.33 no.4
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• pp.309-319
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• 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.

• 한국신재생에너지학회:학술대회논문집
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• 2009.06a
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• pp.486-490
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• 2009

In order to investigate the offshore wind resources, the "QuikSCAT Level 3" data by the QuikSCAT satellite was analyzed from Jan 2000 to Dec 2008. QuikSCAT satellite is a specialized device for a microwave scatterometer that measures near-surface wind speed and direction under all weather and cloud conditions. Wind speed measured at 10 m above from the sea surface as extrapolated to the hub height by using the power law model. It has been found that the high wind energy prevailing in the south sea and the east sea of the Korean peninsula. From the limitation of seawater depth for piling the tower and archipelagic environment around the south sea, the west and the south-west sea are favorable to construct the large scale wind farm. Wind map and monthly variation of wind speed are investigate at the positions.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.11
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• pp.1121-1130
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• 2009

In order to investigate the offshore wind resources, the measured data from the QuikSCAT satellite was analyzed from Jan 2000 to Dec 2008. QuikSCAT satellite is a specialized device for a microwave scatterometer that measures near-surface wind speed and direction under all weather and cloud conditions. Wind speed measured at 10 m above from the sea surface was extrapolated to the hub height by using the power law model. It has been found that the high wind energy prevailing in the south sea and the east sea of the Korean peninsula. From the limitation of seawater depth for piling the tower and archipelagic environment around the south sea, the west and the south-west sea are favorable to construct the large scale offshore wind farm, but it needs efficient blade considering relatively low wind speed. Wind map and monthly variation of wind speed and wind rose using wind energy density were investigated at the specified positions.

• Journal of Environmental Science International
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• v.18 no.8
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• pp.841-845
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• 2009

In this paper, offshore wind resources within the Japan's EEZ (Exclusive Economic Zone) are assessed using wind speed data from the microwave scatterometer SeaWinds onboard QuikSCAT. At first, from the 10m-height wind speed from QuikSCAT, 60 m-height wind speed is estimated by using an empirical equation for height correction. Based on the 60 m-height wind speeds, annual energy Production is calculated under an assumption of installing 2 MW wind turbines every $0.64km^2$. The annual energy production is then accumulated for the entire Japan's territorial waters and EEZ ($4.47{\times}10^6km^2$). As a result, it is shown that the total energy Production is estimated to be $4.86{\times}10^4$ TWh/yr. This offshore wind energy Potential within the EEZ is approximately 50 times higher than the actual annual electricity production in Japan.

• Journal of the Korean Solar Energy Society
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• v.23 no.2
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• pp.35-41
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• 2003

In order to investigate the offshore wind resources around Korean peninsula, the "QuikSCAT Level 3" data by ADEOS II satellite was analyzed from Jan 1 2000 to Jan 18 2003. The "SeaWinds" on the satellite is a specialize4 device for microwave scatterometery that measures near-surface wind speed and direction under all weather and cloud conditions. Wind speed are extrapolated from 10m to 60m with the exponent of 1/10 in the power law model. It has been found that the High wind energy potentials are prevailing in the South sea and Southeastern end of Korean peninsula.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.19 no.5
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• pp.467-475
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• 2007

This study compared the sea surface wind pattern between model results from KMA operational model (RDAPS) and observational results from QuikSCAT in the 2005-2006 year. The mean spatial distributions of sea surface wind show the prominent seasonal patterns of summer and winter season adjacent to Korean Peninsular. The statistical analysis also shows well seasonal variation of sea surface wind patterns between model and observation results. The BIAS value represents less than -0.5 m/s and -1 m/s in summer and winter seasons, respectively. The spatially averaged correlation coefficient shows larger than 0.7 and 0.8 in summer and winter seasons, respectively. The correlation coefficient of winter season shows higher value than that of summer season in the comparison between model and observation. This results show that the RDAPS model simulate well strong sea surface wind in winter season rather than weak sea surface wind in summer season.

• Korean Journal of Remote Sensing
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• v.24 no.1
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• pp.1-16
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• 2008

Steam and advection fogs are frequently observed in the Yellow Sea from March to July except for May. This study uses remote sensing (RS) data for the monitoring of sea fog. Meteorological data obtained from the Ieodo Ocean Research Station provided a valuable information for the occurrence of steam and advection fogs as a ground truth. The RS data used in this study were GOES-9, MTSAT-1R images and QuikSCAT wind data. A dual channel difference (DCD) approach using IR and shortwave IR channel of GOES-9 and MTSAT-1R satellites was applied to detect sea fog. The results showed that DCD, texture-related measurement and the weak wind condition are required to separate the sea fog from the low cloud. The QuikSCAT wind data was used to provide the wind speed criteria for a fog event. The laplacian computation was designed for a measurement of the homogeneity. A new combined method, which includes DCD, QuikSCAT wind speed and laplacian computation, was applied to the twelve cases with GOES-9 and MTSAT-1R. The threshold values for DCD, QuikSCAT wind speed and laplacian are -2.0 K, $8m\;s^{-1}$ and 0.1, respectively. The validation results showed that the new combined method slightly improves the detection of sea fog compared to DCD method: improvements of the new combined method are $5{\sim}6%$ increases in the Heidke skill score, 10% decreases in the probability of false detection, and $30{\sim}40%$ increases in the odd ratio.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.294-297
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• 2007

Steam and advection fogs are frequently observed in the Yellow Sea located between Korea and China during the periods of March-April and June-July respectively. This study uses the remote sensing (RS) data for monitoring sea fog. Meteorological data obtained from the Ieodo Ocean Research Station provided an informative synopsis for the occurrence of steam and advection fogs through a ground truth. The RS data used in this study was GOES-9, MTSAT-1R images and QuikSCAT wind data. A dual channel difference (DCD) approach using IR and near-IR channel of GOES-9 and MTSAT-1R satellites was applied to estimate the extension of the sea fog. For the days examined, it was found that not only the DCD but also the texture-related measurement and the weak wind condition are required to separate the sea fog from the low cloud. The QuikSCAT wind is used to provide a weak wind area less than threshold under stable condition of the surface wind around a fog event. The Laplacian computation for a measurement of the homogeneity was designed. A new combined method of DCD, QuikSCAT wind speed and Laplacian was applied in the twelve cases with GOES-9 and MTSAT-1R. The threshold values for DCD, QuikSCAT wind speed and Laplacian are -2.0 K, 8 m $s^{-1}$ and 0.1, respectively. The validation methods such as Heidke skill score, probability of detection, probability of false detection, true skill score and odds ratio show that the new combined method improves the detection of sea fog rather than DCD method.

• 한국신재생에너지학회:학술대회논문집
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• 2008.05a
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• pp.331-332
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• 2008

Wind energy issued as most spotlight general energy by excellence of actuality as well as economical efficiency, solving environmental problem which caused by creating the energy and possibility of eternal production. Accordingly, government is at the stage of corresponding level by requesting development of new technology to the developed countries as a part of national key industries. The grievous situation from such a rapid movement is meteorological comprehension and assessment as well as the problem of estimation exactness about the wind. In this study, we use the regional meteorological station data, automatic weather station data and QuikSCAT SeaWinds data.

• Ocean and Polar Research
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• v.30 no.1
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• pp.47-58
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• 2008

Several validation studies have been made for QuikSCAT(QSCAT) wind data around the world, mainly in the offshore. However, until now, there were no validation studies for QSCAT wind with resolution of 12.5 km ('QSCAT 12.5 km wind') in the vicinity of Korean Peninsula. To validate 'QSCAT 12.5 km wind' and to investigate its characteristics around Korean Peninsula, the wind data from Ieodo Ocean Research Station, KMA buoys, and KORDI Realtime Observation Stations have been compared. Validation results showed that 'QSCAT 12.5 km wind' RMSE of wind direction and speed were $25.85^{\circ}$ and 1.83 m/s, respectively, at Ieodo Station. The mean wind speed correlation coefficient of KMA buoys and KORDI Realtime Observation Station were 0.78 and 0.61, and the mean wind speed RMSE were 2.2 m/s and 3.2 m/s, respectively. This seems to be mainly because of the distance between QSCAT and in-situ observation stations. The RMSE of wind direction were bigger than $40^{\circ}$ at all in-situ observation stations located near the shore, within 20 km from coastlines. Geophysical features where in-situ observation stations are located seem to affect wind validation scores.