Offshore Wind Resources Assessment from Multiple Satellite Data and WRF Modeling over South China Sea
Using accurate inputs of wind speed is crucial in wind resource assessment, as predicted power is proportional to the wind speed cubed. This study outlines a methodology for combining multiple ocean satellite winds and winds from WRF simulations in order to acquire the accurate reconstructed offshor...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-01-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/7/1/467 |
| _version_ | 1798024470654353408 |
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| author | Rui Chang Rong Zhu Merete Badger Charlotte Bay Hasager Xuhuang Xing Yirong Jiang |
| author_facet | Rui Chang Rong Zhu Merete Badger Charlotte Bay Hasager Xuhuang Xing Yirong Jiang |
| author_sort | Rui Chang |
| collection | DOAJ |
| description | Using accurate inputs of wind speed is crucial in wind resource assessment, as predicted power is proportional to the wind speed cubed. This study outlines a methodology for combining multiple ocean satellite winds and winds from WRF simulations in order to acquire the accurate reconstructed offshore winds which can be used for offshore wind resource assessment. First, wind speeds retrieved from Synthetic Aperture Radar (SAR) and Scatterometer ASCAT images were validated against in situ measurements from seven coastal meteorological stations in South China Sea (SCS). The wind roses from the Navy Operational Global Atmospheric Prediction System (NOGAPS) and ASCAT agree well with these observations from the corresponding in situ measurements. The statistical results comparing in situ wind speed and SAR-based (ASCAT-based) wind speed for the whole co-located samples show a standard deviation (SD) of 2.09 m/s (1.83 m/s) and correlation coefficient of R 0.75 (0.80). When the offshore winds (i.e., winds directed from land to sea) are excluded, the comparison results for wind speeds show an improvement of SD and R, indicating that the satellite data are more credible over the open ocean. Meanwhile, the validation of satellite winds against the same co-located mast observations shows a satisfactory level of accuracy which was similar for SAR and ASCAT winds. These satellite winds are then assimilated into the Weather Research and Forecasting (WRF) Model by WRF Data Assimilation (WRFDA) system. Finally, the wind resource statistics at 100 m height based on the reconstructed winds have been achieved over the study area, which fully combines the offshore wind information from multiple satellite data and numerical model. The findings presented here may be useful in future wind resource assessment based on satellite data. |
| first_indexed | 2024-04-11T18:02:59Z |
| format | Article |
| id | doaj.art-50e9cd045e9b499c942d92a2d086006e |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T18:02:59Z |
| publishDate | 2015-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-50e9cd045e9b499c942d92a2d086006e2022-12-22T04:10:25ZengMDPI AGRemote Sensing2072-42922015-01-017146748710.3390/rs70100467rs70100467Offshore Wind Resources Assessment from Multiple Satellite Data and WRF Modeling over South China SeaRui Chang0Rong Zhu1Merete Badger2Charlotte Bay Hasager3Xuhuang Xing4Yirong Jiang5Public Meteorological Service Center of China Meteorological Administration (CMA), Beijing 10081, ChinaNational Climate Center, Beijing 10081, ChinaDepartment of Wind Energy, Technical University of Denmark (DTU), Frederiksborgvej 339, 4000 Roskilde, DenmarkDepartment of Wind Energy, Technical University of Denmark (DTU), Frederiksborgvej 339, 4000 Roskilde, DenmarkHainan Climate Center, Haikou 570203, ChinaPublic Meteorological Service Center of China Meteorological Administration (CMA), Beijing 10081, ChinaUsing accurate inputs of wind speed is crucial in wind resource assessment, as predicted power is proportional to the wind speed cubed. This study outlines a methodology for combining multiple ocean satellite winds and winds from WRF simulations in order to acquire the accurate reconstructed offshore winds which can be used for offshore wind resource assessment. First, wind speeds retrieved from Synthetic Aperture Radar (SAR) and Scatterometer ASCAT images were validated against in situ measurements from seven coastal meteorological stations in South China Sea (SCS). The wind roses from the Navy Operational Global Atmospheric Prediction System (NOGAPS) and ASCAT agree well with these observations from the corresponding in situ measurements. The statistical results comparing in situ wind speed and SAR-based (ASCAT-based) wind speed for the whole co-located samples show a standard deviation (SD) of 2.09 m/s (1.83 m/s) and correlation coefficient of R 0.75 (0.80). When the offshore winds (i.e., winds directed from land to sea) are excluded, the comparison results for wind speeds show an improvement of SD and R, indicating that the satellite data are more credible over the open ocean. Meanwhile, the validation of satellite winds against the same co-located mast observations shows a satisfactory level of accuracy which was similar for SAR and ASCAT winds. These satellite winds are then assimilated into the Weather Research and Forecasting (WRF) Model by WRF Data Assimilation (WRFDA) system. Finally, the wind resource statistics at 100 m height based on the reconstructed winds have been achieved over the study area, which fully combines the offshore wind information from multiple satellite data and numerical model. The findings presented here may be useful in future wind resource assessment based on satellite data.http://www.mdpi.com/2072-4292/7/1/467satellite retrieval windsdata assimilationoffshore wind resources assessment |
| spellingShingle | Rui Chang Rong Zhu Merete Badger Charlotte Bay Hasager Xuhuang Xing Yirong Jiang Offshore Wind Resources Assessment from Multiple Satellite Data and WRF Modeling over South China Sea Remote Sensing satellite retrieval winds data assimilation offshore wind resources assessment |
| title | Offshore Wind Resources Assessment from Multiple Satellite Data and WRF Modeling over South China Sea |
| title_full | Offshore Wind Resources Assessment from Multiple Satellite Data and WRF Modeling over South China Sea |
| title_fullStr | Offshore Wind Resources Assessment from Multiple Satellite Data and WRF Modeling over South China Sea |
| title_full_unstemmed | Offshore Wind Resources Assessment from Multiple Satellite Data and WRF Modeling over South China Sea |
| title_short | Offshore Wind Resources Assessment from Multiple Satellite Data and WRF Modeling over South China Sea |
| title_sort | offshore wind resources assessment from multiple satellite data and wrf modeling over south china sea |
| topic | satellite retrieval winds data assimilation offshore wind resources assessment |
| url | http://www.mdpi.com/2072-4292/7/1/467 |
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