• 제목/요약/키워드: QuikSCAT data

검색결과 35건 처리시간 0.023초

QuikSCAT과 ASCAT 해상풍 자료동화가 연안 지역 국지 바람장 예측에 미치는 영향 분석 (Analysis of the Impact of QuikSCAT and ASCAT Sea Wind Data Assimilation on the Prediction of Regional Wind Field near Coastal Area)

  • 이순환
    • 한국지구과학회지
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    • 제33권4호
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    • pp.309-319
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    • 2012
  • 연안 해상 바람 자원 평가에 적용되는 해상풍 위성자료 동화특성을 평가하기 위하여 수치실험을 실시하였다. 사용된 위성자료는 미항공우주국의 QuikSCAT과 유럽우주국의 ASCAT이다. 해상풍 위성자료 동화과정은 연안지역 바람 자원 평가의 정확성을 향상시키는 주요한 요소의 하나이다. QuikSCAT의 관측 가능한 빔폭이 상대적으로 넓기 때문에 QuikSCAT 해상풍 자료를 동화하여 제시된 연안 바람장이 ASCAT를 사용한 바람장보다 약간 높은 정확도를 제시한다. 그러나 센서의 직하 부근의 바람장은 상대적으로 ASCAT의 예측 정확도가 높게 나타난다. 이러한 해상풍 위성자료의 동화효과는 6시간 정도 지속되기 때문에 정확한 연안지역 바람장을 평가하기 위해서는 센서의 공간해상도뿐 아니라 시간해상도가 높은 해상풍 위성자료 동화 과정이 필요하다.

위성 관측 자료를 이용한 서해 해상 풍력자원 평가 (The Estimaion of Wind Energy Resources through out the QuikSCAT Data)

  • 장재경;유병민;유기완;이준신
    • 한국신재생에너지학회:학술대회논문집
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    • 한국신재생에너지학회 2009년도 춘계학술대회 논문집
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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.

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QuikSCAT 위성 데이터를 이용한 한반도 주변의 해상 풍력자원 평가 (Offshore Wind Resource Assessment around Korean Peninsula by using QuikSCAT Satellite Data)

  • 장재경;유병민;유기완;이준신
    • 한국항공우주학회지
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    • 제37권11호
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    • pp.1121-1130
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    • 2009
  • QuikSCAT 위성의 관측자료를 이용하여 2000년 1월로부터 2008년 12월에 걸쳐 한반도 근해의 풍력자원을 평가 하였다. QuikSCAT 위성은 초단파 scatterometer를 이용하여 해수면 가까이의 풍향과 풍속을 전천후 상태에서 측정한다. 해면으로부터 10 m 높이에서 측정된 풍속을 power law모델을 이용하여 허브 높이에 맞게 외삽 보정하였다. 계산 결과 한반도의 남해와 동해에서 풍력에너지가 상대적으로 우세하다는 것을 알 수 있었다. 풍력 터빈 타워의 설치를 위해 깊은 수심을 피하고 대규모 풍력단지 조성을 위해 남해의 다도해 지역을 피한다면 한반도 서쪽 또는 남서쪽 연안이 대규모 풍력단지 조성에 유리하나 상대적으로 낮은 풍속을 고려한 블레이드 개발을 요한다. 바람 지도를 작성하였으며, 특정 지점에 대한 월별 풍속 변화를 파악하였다. 그리고 풍력에너지 밀도를 이용한 바람장미를 파악하였다.

Assessment of Offshore Wind Resources Within Japan's EEZ Using QuikSCAT Data

  • Ohsawa, Teruo;Tanaka, Masahiro;Shimada, Susumu;Tsubouchi, Nobuki;Kozai, Katsutoshi
    • 한국환경과학회지
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    • 제18권8호
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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.

한반도해역의 해상 풍력 자원 평가 (An Assessment of Offshore Wind Energy Resources around Korean Peninsula)

  • 경남호;윤정은;장문석;장동순
    • 한국태양에너지학회 논문집
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    • 제23권2호
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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.

기상청 현업 모델 RDAPS와 QuikSCAT 해상풍 자료의 비교 (Comparison of KMA Operational Model RDAPS with QuikSCAT Sea Surface Wind Data)

  • 유승협;조재갑;서장원
    • 한국해안해양공학회지
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    • 제19권5호
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    • pp.467-475
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    • 2007
  • 기상청 현업 모델 RDAPS 모델 자료와 QuikSCAT 관측결과 중 2005, 2006년 해상풍 자료를 비교하였다. 해상풍의 하계와 동계의 평균 공간 분포의 분석결과 한반도 주변의 계절적인 해상풍의 뚜렷한 특성을 잘 나타낸다. 모델과 관측의 해상풍 통계 분석 비교에서도 계절적인 차이를 잘 나타낸다. 한반도 주변의 하계 BIAS 값은 -0.5m/s이하의 분포를 보이고, 동계에는 -1 m/s이하의 분포를 보인다. 상관계수의 경우 하계에는 0.7, 동계에는 0.8 이상으로 분포한다. 공간적으로 평균된 상관계수의 경우 2005년, 2006년 모두 하계보다 동계에 더욱 상관관계가 높은 것으로 나타났다. 이것은 하계의 약한 풍속보다 동계의 강한 해상풍을 RDAPS에서 더 잘 재현하고 있는 것으로 판단된다.

A Remote Sensed Data Combined Method for Sea Fog Detection

  • Heo, Ki-Young;Kim, Jae-Hwan;Shim, Jae-Seol;Ha, Kyung-Ja;Suh, Ae-Sook;Oh, Hyun-Mi;Min, Se-Yun
    • 대한원격탐사학회지
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    • 제24권1호
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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.

EVALUATION OF SEA FOG DETECTION USING A REMOTE SENSED DATA COMBINED METHOD

  • Heo, Ki-Young;Ha, Kyung-Ja;Kim, Jae-Hwan;Shim, Jae-Seol;Suh, Ae-Sook
    • 대한원격탐사학회:학술대회논문집
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    • 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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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.

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한반도 풍력자원의 시공간적 특성 분석 (The spatial-temporal characteristics for wind power resources on the Korean Peninsula)

  • 이화운;김민정;김동혁;김현구;이순환
    • 한국신재생에너지학회:학술대회논문집
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    • 한국신재생에너지학회 2008년도 춘계학술대회 논문집
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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.

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한반도 연안에서의 12.5 km 해상도 QuikSCAT 해상풍 검증 (Validation of QuikSCAT Wind with Resolution of 12.5 km in the Vicinity of Korean Peninsula)

  • 정진용;심재설;이동규;민인기;권재일
    • Ocean and Polar Research
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    • 제30권1호
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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.