{"title":"南海风能的变率","authors":"Yisheng Zhang, Yongcun Cheng, Yizhi Li","doi":"10.1109/piers55526.2022.9793037","DOIUrl":null,"url":null,"abstract":"The offshore wind energy has been evaluated in the South China Sea. However, few works focus on the long-term variability of the wind resources, which is vital for regional wind farm planning and construction. In this work, we analyzed combined remote sensing data from Advanced Scatterometer (ASCAT) and QuickSCAT, ERA-interim and Climate Forecast System Reanalysis (CFSR) data to investigate the spatial and temporal variations of wind resources in the South China Sea. The CSEOF (cyclostationary empirical orthogonal function decomposition) analysis was adopted to show the spatial-temporal patterns of the offshore wind energy. The results indicate that the first modes (annual cycle signals) accounted for about 80% of the total variability in the analyzed datasets, and the second or third mode (depends on the length of the dataset) of CSEOF high correlated with ENSO (El Niño-Southern Oscillation). Significant wind speed variability was observed during the El Nino events.","PeriodicalId":422383,"journal":{"name":"2022 Photonics & Electromagnetics Research Symposium (PIERS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Variability of Wind Energy in the South China Sea\",\"authors\":\"Yisheng Zhang, Yongcun Cheng, Yizhi Li\",\"doi\":\"10.1109/piers55526.2022.9793037\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The offshore wind energy has been evaluated in the South China Sea. However, few works focus on the long-term variability of the wind resources, which is vital for regional wind farm planning and construction. In this work, we analyzed combined remote sensing data from Advanced Scatterometer (ASCAT) and QuickSCAT, ERA-interim and Climate Forecast System Reanalysis (CFSR) data to investigate the spatial and temporal variations of wind resources in the South China Sea. The CSEOF (cyclostationary empirical orthogonal function decomposition) analysis was adopted to show the spatial-temporal patterns of the offshore wind energy. The results indicate that the first modes (annual cycle signals) accounted for about 80% of the total variability in the analyzed datasets, and the second or third mode (depends on the length of the dataset) of CSEOF high correlated with ENSO (El Niño-Southern Oscillation). Significant wind speed variability was observed during the El Nino events.\",\"PeriodicalId\":422383,\"journal\":{\"name\":\"2022 Photonics & Electromagnetics Research Symposium (PIERS)\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-04-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 Photonics & Electromagnetics Research Symposium (PIERS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/piers55526.2022.9793037\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Photonics & Electromagnetics Research Symposium (PIERS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/piers55526.2022.9793037","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The offshore wind energy has been evaluated in the South China Sea. However, few works focus on the long-term variability of the wind resources, which is vital for regional wind farm planning and construction. In this work, we analyzed combined remote sensing data from Advanced Scatterometer (ASCAT) and QuickSCAT, ERA-interim and Climate Forecast System Reanalysis (CFSR) data to investigate the spatial and temporal variations of wind resources in the South China Sea. The CSEOF (cyclostationary empirical orthogonal function decomposition) analysis was adopted to show the spatial-temporal patterns of the offshore wind energy. The results indicate that the first modes (annual cycle signals) accounted for about 80% of the total variability in the analyzed datasets, and the second or third mode (depends on the length of the dataset) of CSEOF high correlated with ENSO (El Niño-Southern Oscillation). Significant wind speed variability was observed during the El Nino events.